Software Archives - gettectonic.com

05Feb

Unpatched.ai

The Mystery of Unpatched.ai: AI-Powered Vulnerability Discovery Raises Questions During January’s Patch Tuesday, Microsoft credited Unpatched.ai for reporting multiple high-severity vulnerabilities. Yet, despite its contributions, the AI-driven bug-finding tool remains an enigma to the cybersecurity community. Last month, Microsoft addressed 159 new vulnerabilities across its widely used products. Among them, Unpatched.ai was acknowledged for identifying three remote code execution flaws—CVE-2025-21186, CVE-2025-21366, and CVE-2025-21395—all of which affect Microsoft Access and received a CVSS score of 7.8. While Microsoft’s recognition highlights Unpatched.ai’s role in vulnerability discovery, little is known about the tool itself. Informa TechTarget reached out to multiple security vendors and experts for insights, but responses only deepened the mystery. A Cryptic Online Presence Unpatched.ai describes itself as “vulnerability discovery by an AI-guided cybersecurity platform” on its website. It provides a list of reported vulnerabilities, which consists solely of Microsoft-related flaws—primarily within Microsoft Access. The platform states that it collaborates with “select enterprise, government, and security vendors based in the U.S. and ally countries.” The company’s “About” page sheds some light on its mission, attributing its research to the need for greater transparency around unpatched software flaws: “We find unpatched issues in software to help customers better identify and manage cyber risk. Many issues are unknown or silently fixed by software vendors, hiding the true risk profile of their products. With the help of AI, we are developing an automated platform to help find and analyze these issues for our customers.” Beyond the website, Unpatched.ai maintains an X account, though much of its activity has been erased. A now-deleted post from January 29 warned that Microsoft’s patch for CVE-2025-21396 was insufficient. When contacted about the post, a Microsoft spokesperson responded, “We are aware of these reports and will take action as needed to help protect customers.” However, Microsoft did not provide additional background on Unpatched.ai. Attempts to reach Unpatched.ai directly have gone unanswered. Piecing Together the Puzzle Efforts to uncover more about Unpatched.ai yielded few concrete details. The domain was registered through Namecheap in September, with ownership masked by a privacy service based in Reykjavik, Iceland. Adam Barnett, lead software engineer at Rapid7, noted that beyond Unpatched.ai’s website, information is scarce. However, he identified a Reddit user, “Fit_Tie_9430,” who has claimed affiliation with the platform. This user shared details about Unpatched.ai’s vulnerability discoveries and linked to now-private YouTube videos demonstrating exploits against Microsoft Access vulnerabilities. Barnett pointed out that Unpatched.ai was also credited for a December Patch Tuesday flaw, CVE-2024-49142. Initially published without attribution, Microsoft later updated the advisory to acknowledge Unpatched.ai’s discovery. Interestingly, the Unpatched.ai website’s favicon—a simple “:)” emoticon—appears to reference the Windows Blue Screen of Death’s “:(” symbol. “It’s a nice touch,” Barnett said, “but I still don’t know who’s behind it. It could be just about anyone with the time, resources, and skills.” Other industry experts share the same uncertainty. Satnam Narang, senior staff research engineer at Tenable, observed that Unpatched.ai’s X account follows only a handful of infosec professionals. “It’s unclear if the service is still in a closed-door phase and will eventually provide more insights about its leadership and team, or who may be backing it,” he said. Alon Yamin, co-founder and CEO of Copyleaks, noted that an AI-driven vulnerability discovery platform was inevitable given the surge in software flaws. While AI can be a game-changer for proactive threat detection, he cautioned against potential misuse. “It’s crucial that Unpatched.ai is deployed carefully, responsibly, and ethically, with safeguards to prevent attackers from exploiting the vulnerabilities it identifies,” Yamin said. The Future of AI-Powered Bug Hunting AI-driven vulnerability discovery is an emerging focus in cybersecurity, though few major breakthroughs have been publicly confirmed. In November, Google announced it had discovered a zero-day vulnerability using AI. Google Project Zero and DeepMind’s AI-powered agent, Big Sleep, identified a buffer stack underflow flaw in the SQLite open-source database engine. With Unpatched.ai making waves yet remaining elusive, the cybersecurity community is left with more questions than answers. Is this the beginning of a new era in AI-powered vulnerability research, or is Unpatched.ai an outlier? Until more information surfaces, the mystery remains. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

February 5, 2025in Data, Enterprise, Google, Laws

03Feb

Pydantic AI

The evaluation of agentic applications is most effective when integrated into the development process, rather than being an afterthought. For this to succeed, developers must be able to mock both internal and external dependencies of the agent being built. PydanticAI introduces a groundbreaking framework that supports dependency injection from the start, enabling developers to build agentic applications with an evaluation-driven approach. An architectural parallel can be drawn to the historic Krakow Cloth Hall, a structure refined over centuries through evaluation-driven enhancements. Similarly, PydanticAI allows developers to iteratively address challenges during development, ensuring optimal outcomes. Challenges in Developing GenAI Applications Developers of LLM-based applications face recurring challenges, which become significant during production deployment: To address non-determinism, developers must adopt evaluation-driven development, a method akin to test-driven development. This approach focuses on designing software with guardrails, real-time monitoring, and human oversight, accommodating systems that are only x% correct. The Promise of PydanticAI PydanticAI stands out as an agent framework that supports dependency injection, model-agnostic workflows, and evaluation-driven development. Its design is Pythonic and simplifies testing by allowing the injection of mock dependencies. For instance, in contrast to frameworks like Langchain, where dependency injection is cumbersome, PydanticAI streamlines this process, making the workflows more readable and efficient. Building an Evaluation-Driven Application with PydanticAI Example Use Case: Evaluating Mountain Data By employing tools like Wikipedia as a data source, the agent can fetch accurate mountain heights during production. For testing, developers can inject mocked responses, ensuring predictable outputs and faster development cycles. Advancing Agentic Applications with PydanticAI PydanticAI provides the building blocks for creating scalable, evaluation-driven GenAI applications. Its support for dependency injection, structured outputs, and model-agnostic workflows addresses core challenges, empowering developers to create robust and adaptive LLM-powered systems. This paradigm shift ensures that evaluation is seamlessly embedded into the development lifecycle, paving the way for more reliable and efficient agentic applications. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

February 3, 2025in Salesforce

Einstein Service Agent

It’s been a little over a year since the global surge in GenAI chatbots, sparked by the excitement around ChatGPT. Since then, numerous vendors, both large and mid-sized, have invested heavily in the technology, and many users have already adopted AI-powered chatbots. The competition is intensifying, with CRM giant Salesforce releasing its own GenAI chatbot software, Einstein Service Agent. Einstein Service Agent, built on the Einstein 1 Platform, is Salesforce’s first fully autonomous AI agent. It interacts with large language models (LLMs) by analyzing the context of customer messages to determine the next actions. Utilizing GenAI, the agent generates conversational responses grounded in a company’s trusted business data, including Salesforce CRM data. Salesforce claims that service organizations can now significantly reduce the number of tedious inquiries that hinder productivity, allowing human agents to focus on more complex tasks. For customers, this means getting answers faster without waiting for human agents. Additionally, the service promises 24/7 availability for customer communication in natural language, with an easy handoff to human agents for more complicated issues. Businesses are increasingly turning to AI-based chatbots because, unlike traditional chatbots, they don’t rely on specific programmed queries and can understand context and nuance. Alongside Salesforce, other tech leaders like AWS and Google Cloud have released their own chatbots, such as Amazon Lex and Vertex AI, continuously enhancing their software. Recently, AWS updated its chatbot with the QnAIntent capability in Amazon Lex, allowing integration with a knowledge base in Amazon Bedrock. Similarly, Google released Vertex AI Agent Builder earlier this year, enabling organizations to build AI agents with no code, which can function together with one main agent and subagents. The AI arms race is just beginning, with more vendors developing software to meet market demands. For users, this means that while AI takes over many manual and tedious tasks, the primary challenge will be choosing the right vendor that best suits the needs and resources of their business. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

January 31, 2025in Data, Einstein 1 Platform, Google, Salesforce, Technology

25Jan

Direct Manipulation to Intent-Driven Design

The Shift from Direct Manipulation to Intent-Driven Design: How AI is Reshaping User Interaction The way we interact with software is constantly evolving — sometimes through gradual changes, other times through disruptive leaps. Today, as AI-powered applications gain momentum, design pioneers like Vitaly Friedman, Emily Campbell, and Greg Nudelman are exploring the emerging design patterns that are reshaping user experiences. This shift is more than just another tech trend — it represents a fundamental transformation in human-computer interaction. The shift can be compared to the transition from film cameras to digital photography. In the past, users manually adjusted settings like exposure and film speed. With digital cameras, the process became automated — users simply clicked a button, and the camera handled the rest. AI is now bringing a similar transformation to software interfaces, allowing users to express their desired outcomes without dictating every step of the process. As Jakob Nielsen notes, this shift moves us away from rigid, step-by-step commands toward a goal-driven approach. In his words: “With new AI systems, the user no longer tells the computer what to do. Rather, the user tells the computer what outcome they want.” This transformation is not just technological — it’s philosophical. It challenges traditional ideas about control, agency, and human-computer collaboration. Where users once defined every step in an interaction, they now express their intent and let AI determine the optimal approach to achieving it. Direct Manipulation: The Foundation of Intuitive Design Before diving into how AI reshapes user interaction, it’s essential to understand the principles of direct manipulation, which have long defined intuitive user interfaces. In 1985, Edwin Hutchins, James Hollan, and Don Norman introduced the concept of direct manipulation in user interfaces. Direct manipulation refers to interaction styles where users directly engage with on-screen objects using physical, incremental, and reversible actions. For example, when you drag a file from one folder to another, you are directly manipulating the object. This interaction style is intuitive because it minimizes cognitive load — users can see immediate feedback as they interact with digital objects, reinforcing a sense of control. The Transition to Goal-Oriented Interactions AI is now challenging the dominance of direct manipulation by introducing goal-driven interactions. Instead of dictating each step of a process, users now express their desired outcome, and the system interprets and executes the task. Consider the AI-powered ‘Erase’ feature in Windows Photos. Instead of manually editing pixels to remove an unwanted object from a photo, users simply select the object and let the AI complete the task. This shifts the interaction from direct manipulation to intent-driven collaboration. Researchers like Desolda have explored this shift in their model of human-AI interaction. In traditional direct manipulation, users act in a linear sequence — recognize a goal, take an action, receive feedback. With AI, interactions become iterative and dynamic — users provide high-level input, AI executes, and users refine the output as needed. Enhancing Rather Than Replacing Direct Manipulation While AI introduces new interaction paradigms, it does not eliminate direct manipulation; instead, it layers new capabilities on top of it. For example, open input fields — like those found in ChatGPT or generative design tools — are built upon familiar UI patterns. These patterns reduce friction while allowing AI to extend the user‘s capabilities. Similarly, emerging frameworks like ‘Promptframes’ — introduced by Evan Sunwall — blend traditional wireframing with AI-generated content, accelerating design workflows without discarding familiar structures. This hybrid approach illustrates how AI can augment direct manipulation rather than replace it. Designing Seamless AI Interactions The ultimate goal of AI in design is to make interactions seamless. The most effective AI experiences do not draw attention to themselves — they quietly enhance user workflows. A prime example is Netflix’s recommendation engine. It does not ask users to configure settings or provide detailed input — it simply learns, adapts, and presents relevant content. This is the gold standard for AI-powered design: reducing friction, minimizing cognitive load, and allowing users to focus on their goals rather than the mechanics of interaction. As we design for AI, the focus should remain on enabling users to achieve outcomes effortlessly, rather than demanding their continuous attention. The future of user experience lies in balancing direct manipulation with AI-driven augmentation — empowering users to act with minimal friction while achieving powerful, intelligent outcomes. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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January 25, 2025in Salesforce

23Jan

AI Market Heat

Alibaba Feels the Heat as DeepSeek Shakes Up AI Market Chinese tech giant Alibaba is under pressure following the release of an AI model by Chinese startup DeepSeek that has sparked a major reaction in the West. DeepSeek claims to have trained its model—comparable to advanced Western AI—at a fraction of the cost and with significantly fewer AI chips. In response, Alibaba launched Qwen 2.5-Max, its latest AI language model, on Tuesday—just one day before the Lunar New Year, when much of China’s economy typically slows down for a 15-day holiday. A Closer Look at Qwen 2.5-Max Qwen 2.5-Max is a Mixture of Experts (MoE) model trained on 20 trillion tokens. It has undergone supervised fine-tuning and reinforcement learning from human feedback to enhance its capabilities. MoE models function by using multiple specialized “minds,” each focused on a particular domain. When a query is received, the model dynamically routes it to the most relevant expert, improving efficiency. For instance, a coding-related question would be processed by the model‘s coding expert. This MoE approach reduces computational requirements, making training more cost-effective and faster. Other AI vendors, such as France-based Mistral AI, have also embraced this technique. DeepSeek’s Disruptive Impact While Qwen 2.5-Max is not a direct competitor to DeepSeek’s R1 model—the release of which triggered a global selloff in AI stocks—it is similar to DeepSeek-V3, another MoE-based model launched earlier this month. Alibaba’s swift release underscores the competitive threat posed by DeepSeek. As the world’s fourth-largest public cloud vendor, Alibaba, along with other Chinese tech giants, has been forced to respond aggressively. In the wake of DeepSeek R1’s debut, ByteDance—the owner of TikTok—also rushed to update its AI offerings. DeepSeek has already disrupted the AI market by significantly undercutting costs. In 2023, the startup introduced V2 at just 1 yuan ($0.14) per million tokens, prompting a price war. By comparison, OpenAI’s GPT-4 starts at $10 per million tokens—a staggering difference. The timing of Alibaba and ByteDance’s latest releases suggests that DeepSeek has accelerated product development cycles across the industry, forcing competitors to move faster than planned. “Alibaba’s cloud unit has been rapidly advancing its AI technology, but the pressure from DeepSeek’s rise is immense,” said Lisa Martin, an analyst at Futurum Group. A Shifting AI Landscape DeepSeek’s rapid growth reflects a broader shift in the AI market—one driven by leaner, more powerful models that challenge conventional approaches. “The drive to build more efficient models continues,” said Gartner analyst Arun Chandrasekaran. “We’re seeing significant innovation in algorithm design and software optimization, allowing AI to run on constrained infrastructure while being more cost-competitive.” This evolution is not happening in isolation. “AI companies are learning from one another, continuously reverse-engineering techniques to create better, cheaper, and more efficient models,” Chandrasekaran added. The AI industry’s perception of cost and scalability has fundamentally changed. Sam Altman, CEO of OpenAI, previously estimated that training GPT-4 cost over $100 million—but DeepSeek claims it built R1 for just $6 million. “We’ve spent years refining how transformers function, and the efficiency gains we’re seeing now are the result,” said Omdia analyst Bradley Shimmin. “These advances challenge the idea that massive computing power is required to develop state-of-the-art AI.” Competition and Data Controversies DeepSeek’s success showcases the increasing speed at which AI innovation is happening. Its distillation technique, which trains smaller models using insights from larger ones, has allowed it to create powerful AI while keeping costs low. However, OpenAI and Microsoft are now investigating whether DeepSeek improperly used their models’ data to train its own AI—a claim that, if true, could escalate into a major dispute. Ironically, OpenAI itself has faced similar accusations, leading some enterprises to prefer using its models through Microsoft Azure, which offers additional compliance safeguards. “The future of AI development will require stronger security layers,” Shimmin noted. “Enterprises need assurances that using models like Qwen 2.5 or DeepSeek R1 won’t expose their data.” For businesses evaluating AI models, licensing terms matter. Alibaba’s Qwen 2.5 series operates under an Apache 2.0 license, while DeepSeek uses an MIT license—both highly permissive, allowing companies to scrutinize the underlying code and ensure compliance. “These licenses give businesses transparency,” Shimmin explained. “You can vet the code itself, not just the weights, to mitigate privacy and security risks.” The Road Ahead The AI arms race between DeepSeek, Alibaba, OpenAI, and other players is just beginning. As vendors push the limits of efficiency and affordability, competition will likely drive further breakthroughs—and potentially reshape the AI landscape faster than anyone anticipated. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 23, 2025in Data, Enterprise, Technology

22Jan

Understanding AI Agents

Understanding AI Agents: How They Differ from Copilots and Assistants The AI landscape is evolving rapidly, with terms like AI agents, copilots, and assistants often used interchangeably. But what truly distinguishes them? This analysis clarifies their differences, maps them against real-world AI tools, and identifies gaps in today’s market. Why This Distinction Matters Understanding AI agent capabilities is crucial for: By 2025, AI agents are expected to become enterprise-ready, with the market projected to grow 45% annually, reaching $47 billion by 2030 (MarketsandMarkets). Microsoft CEO Satya Nadella even suggests that agentic applications could replace traditional SaaS. But what makes an AI tool an agent rather than just a copilot or assistant? Defining AI Agents, Copilots, and Assistants 1. AI Agents: Autonomous Goal-Seekers Gartner’s definition (2024): “AI agents are autonomous or semi-autonomous software entities that use AI techniques to perceive, make decisions, take actions, and achieve goals in their digital or physical environments.” Key capabilities:✔ Autonomy – Acts independently.✔ Goal-driven behavior – Works toward broader objectives.✔ Environmental interaction – Uses tools (actions), sensors (perception), and data retrieval.✔ Learning & memory – Adapts over time.✔ Proactivity – Acts on triggers, not just user commands. Example: Agentforce (Salesforce’s AI agent) autonomously creates marketing campaigns by analyzing CRM data. 2. AI Copilots: Collaborative Partners Microsoft’s perspective: “Copilots enhance decision-making by offering context-specific recommendations and work collaboratively with humans.” Key differences from agents: Example: Cursor (AI coding assistant) helps developers by auto-completing and refining code in real time. 3. AI Assistants: Task-Based Helpers Example: ChatGPT (basic version) answers questions but doesn’t autonomously execute tasks. The Agent-Copilot-Assistant Spectrum Feature AI Assistant AI Copilot AI Agent Autonomy ❌ No ⚠️ Semi ✅ Yes Goal-driven ❌ No ⚠️ Partial ✅ Yes Tools & Actions ❌ No ⚠️ Limited ✅ Yes Sensors/Triggers ❌ No ❌ No ✅ Yes Memory & Learning ❌ No ✅ Yes ✅ Yes Proactivity ❌ No ⚠️ Some ✅ Yes Current Market Gaps: Where AI Tools Fall Short Despite advancements, most AI tools today don’t fully meet agent or copilot criteria: 1. Most “Agents” Lack True Autonomy 2. Copilots Often Lack Memory 3. Assistants Dominate the Market Many popular AI tools (Grammarly, Canva AI, Remove.bg) are task-specific assistants, not true copilots or agents. The Future of AI Agents & Copilots Key Takeaways ✔ AI agents act autonomously, copilots collaborate, and assistants follow commands.✔ Today’s “agents” are semi-autonomous—true autonomy is still evolving.✔ Most AI tools are still assistants, with only a few (like GitHub Copilot) qualifying as copilots.✔ Memory, proactivity, and sensors are the biggest gaps in current AI offerings. For businesses and developers, this presents an opportunity: those who build true copilots and safe agents will lead the next wave of AI adoption. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

January 22, 2025in AI Tools, Data, Enterprise, Salesforce

19Jan

How Hackers Exploit GenAI

Hackers are increasingly leveraging generative AI (GenAI) to execute sophisticated cyberattacks, with real-world incidents highlighting its growing role in cybercrime. In early 2024, fraudsters used a deepfake of a multinational firm’s CFO to trick a finance employee into transferring $25 million—a stark example of how GenAI is reshaping cyber threats. Experts warn this is just the beginning. Here’s how cybercriminals are using GenAI to their advantage: 1. Crafting Advanced Phishing & Social Engineering Attacks GenAI-powered tools like ChatGPT enable hackers to generate professional-grade phishing emails that closely mimic corporate communications. These emails, now nearly flawless in grammar and formatting, are far more convincing to targets. Additionally, GenAI can: 2. Writing & Enhancing Malicious Code Just as developers use GenAI to accelerate coding, cybercriminals use it to: This automation fuels a rise in zero-day attacks, where vulnerabilities are exploited before developers can patch them. 3. Identifying Vulnerabilities at Scale GenAI accelerates the discovery of security weaknesses by: With GenAI, cybercriminals can scale and refine their tactics faster than ever. 4. Automating Target Research & Attack Planning Hackers use GenAI to: While mainstream AI tools have built-in safeguards, threat actors find ways to bypass them, using alternative AI models or dark web resources. 5. Lowering the Barrier to Cybercrime GenAI democratizes cyberattacks by: This increased accessibility means more people—beyond seasoned cybercriminals—can launch effective cyberattacks. The Hidden Risk: AI-Powered Coding in Enterprises The security risk of GenAI isn’t limited to adversarial use. Businesses adopting AI-powered coding tools may unintentionally introduce vulnerabilities into their systems. Joseph Nwankpa, director of cybersecurity initiatives at Miami University’s Farmer School of Business, warns: The Takeaway While GenAI offers groundbreaking advancements, it also amplifies cyber threats. Organizations must remain vigilant—investing in AI security measures, strengthening human oversight, and educating employees to counter AI-powered attacks. The race between AI-driven innovation and cybercrime is just getting started. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 19, 2025in AI Tools, Enterprise, Generative AI, Laws

17Jan

Marketing Cloud Account Engagement Page Completion Actions

Automate marketing tasks and follow-ups with completion actions. You can use standard actions that apply to all prospects equally or actions that apply only to prospects that meet the conditions you specify. Action Definition Add tags Applies a tag to a prospect. Add to list Adds a prospect to a list. Add to campaign Tags a prospect with the Salesforce campaign of your choice if the prospect has a lead or contact record in Salesforce. Adjust score Adjusts a prospect’s score by a selected number or resets it to a specific number. Adjust score for scoring category Adjusts a prospect’s score by a selected number for a scoring category. Assign prospect via Salesforce active assignment rule Pushes a prospect to your Salesforce active assignment rule. The prospect syncs back to Account Engagement after they’re assigned in Salesforce. This action doesn’t reassign prospects. Add to group Assigns a prospect to an Account Engagement lead distribution group. Assign to queue Assigns a prospect to the Salesforce queue. This action doesn’t reassign prospects. If your connector syncs prospects to Salesforce contacts instead of leads, don’t use this action. Assign to user Assigns a prospect to a user. This action doesn’t reassign prospects. Change prospect custom field value Changes a custom field value. Change prospect default field value Changes a default field value. Create Salesforce task† Creates a Salesforce task for prospects. The task isn’t synced until the prospect is assigned to a user, a queue‡, a group‡, or via an active assignment rule. Increment prospect field value Adds to or subtracts from values for number fields. Requires a number field in the business unit. Notify assigned user Notifies the prospect’s assigned user. Notify Slack channel Notifies a Slack channel if the Slack connector is verified. Notify user Notifies a user. Notify user via Twilio (phone) Notifies a user by phone if the Twilio connector is verified. Register for webinar Registers the prospect for an upcoming webinar if the webinar connector is verified. Remove from list Removes a prospect from a list. Remove tags Removes tags from a prospect. Send autoresponder email Sends a prospect an autoresponder email. This action is throttled in some circumstances. Set profile Changes a prospect’s profile. Set prospect’s first touch campaign Changes the prospect’s Account Engagement campaign. Set prospect’s source campaign Changes the prospect’s Account Engagement campaign. †: Task type and task status options are set in Salesforce. If a completion action specifies a status that no longer exists in Salesforce, the action uses the default status. If the status is a closed status that no longer exists, the action uses the first closed status it finds. ‡: You can’t assign Salesforce tasks to a group or a queue. Salesforce task completion actions assigned to the CRM Owner or Assigned User don’t work on prospects who are assigned to a group or queue in Salesforce. Filter Prospects with Conditional Completion Actions For more control over which prospects you take action on, add conditional completion actions to your marketing assets. While standard actions execute on all prospects who interact with an asset, conditional actions execute only on prospects who also meet criteria that you define. You can combine both types of actions on one asset to target your prospects more effectively. How Conditional Actions Work Conditional actions offer an added layer of control by letting you choose prospect criteria as a condition for the action. Filter based on system data such as email opens or prospect fields such as City or Territory. Conditional actions execute on prospects who meet the trigger criteria and also meet the prospect criteria you define. To add conditional actions, you first add a conditional group. For each conditional group, you define prospect criteria and add conditional actions. Conditional actions only execute if prospects meet the trigger criteria and the prospect criteria for that group. You can have up to six conditional groups per asset trigger. Use Cases Use a single conditional action to target specific prospects, or combine multiple standard and conditional actions for more robust targeting. Example You have a signup form for a new industry conference you’re hosting. You want to set the lead source for new prospects who sign up without changing the lead source for existing prospects. Add a conditional group and specify criteria to limit actions in that group to prospects with an empty source field. Then, use the Change prospect default field value action to update the source field to the value you want, such as Conference 2022. Example When prospects submit your signup form, you want to send a welcome email. But, you also want to add prospects to the appropriate sales queue and email list based on whether they live in California or Washington. Page Actions Page actions are completion actions that are triggered by a prospect’s page views. You can apply page actions to any page that contains your tracking code. Required Editions Available in: All Account Engagement Editions These completion actions are available for use in page actions: For assistance implementing page, form, or email completion actions in your Marketing Cloud Account Engagement contact Tectonic today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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January 17, 2025in Data, Salesforce, Salesforce Marketing Cloud Account Engagement (Pardot)

14Jan

On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record .6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 14, 2025in Data, Enterprise, Generative AI, Google, Technology

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 11, 2025in Agentic AI, Data, Enterprise, Technology

Apple's Privacy Changes: A Call for Email Marketing Innovation

09Jan

Liar Liar Apple on Fire

Apple Developing Update After AI System Generates Inaccurate News Summaries Apple is working on a software update to address inaccuracies generated by its Apple Intelligence system after multiple instances of false news summaries were reported. The BBC first alerted Apple in mid-December to significant errors in the system, including a fabricated summary that falsely attributed a statement to BBC News. The summary suggested Luigi Mangione, accused of killing United Healthcare CEO Brian Thompson, had shot himself, a claim entirely unsubstantiated. Other publishers, such as ProPublica, also raised concerns about Apple Intelligence producing misleading summaries. While Apple did not respond immediately to the BBC’s December report, it issued a statement after pressure mounted from groups like the National Union of Journalists and Reporters Without Borders, both of which called for the removal of Apple Intelligence. Apple assured stakeholders it is working to refine the technology. A Widespread AI Issue: Hallucinations Apple joins the ranks of other AI vendors struggling with generative AI hallucinations—instances where AI produces false or misleading information. In October 2024, Perplexity AI faced a lawsuit from Dow Jones & Co. and the New York Post over fabricated news content attributed to their publications. Similarly, Google had to improve its AI summaries after providing users with inaccurate information. On January 16, Apple temporarily disabled AI-generated summaries for news apps on iPhone, iPad, and Mac devices. The Core Problem: AI Hallucination Chirag Shah, a professor of Information Science at the University of Washington, emphasized that hallucination is inherent to the way large language models (LLMs) function. “The nature of AI models is to generate, synthesize, and summarize, which makes them prone to mistakes,” Shah explained. “This isn’t something you can debug easily—it’s intrinsic to how LLMs operate.” While Apple plans to introduce an update that clearly labels summaries as AI-generated, Shah believes this measure falls short. “Most people don’t understand how these headlines or summaries are created. The responsible approach is to pause the technology until it’s better understood and mitigation strategies are in place,” he said. Legal and Brand Implications for Apple The hallucinated summaries pose significant reputational and legal risks for Apple, according to Michael Bennett, an AI adviser at Northeastern University. Before launching Apple Intelligence, the company was perceived as lagging in the AI race. The release of this system was intended to position Apple as a leader. Instead, the inaccuracies have damaged its credibility. “This type of hallucinated summarization is both an embarrassment and a serious legal liability,” Bennett said. “These errors could form the basis for defamation claims, as Apple Intelligence misattributes false information to reputable news sources.” Bennett criticized Apple’s seemingly minimal response. “It’s surprising how casual Apple’s reaction has been. This is a major issue for their brand and could expose them to significant legal consequences,” he added. Opportunity for Publishers The incident highlights the need for publishers to protect their interests when partnering with AI vendors like Apple and Google. Publishers should demand stronger safeguards to prevent false attributions and negotiate new contractual clauses to minimize brand risk. “This is an opportunity for publishers to lead the charge, pushing AI companies to refine their models or stop attributing false summaries to news sources,” Bennett said. He suggested legal action as a potential recourse if vendors fail to address these issues. Potential Regulatory Action The Federal Trade Commission (FTC) may also scrutinize the issue, as consumers paying for products like iPhones with AI capabilities could argue they are not receiving the promised service. However, Bennett believes Apple will likely act to resolve the problem before regulatory involvement becomes necessary. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 9, 2025in Generative AI, Google, Laws, Technology

07Jan

Why Choose Salesforce as Your Mortgage CRM?

Centralized Data Storage & Management Salesforce Mortgage CRM consolidates all your mortgage data into a single platform, offering a unified view of client information, loan details, and transaction history. This eliminates the confusion of scattered data, ensuring brokers have quick access to crucial information. For instance, if a client calls with questions about their loan, brokers can immediately retrieve accurate details, enhancing trust and improving the client experience. Accurate Loan Forecasting With AI-driven data analytics, Salesforce provides actionable insights into future loan opportunities, potential risks, and projected income. By analyzing historical data, brokers can predict client behavior, such as the likelihood of refinancing. This proactive approach allows brokers to deliver tailored offers, increasing conversions and client satisfaction. Seamless Collaboration & Secure Data Sharing Effective communication within an organization is essential, and Salesforce Mortgage CRM excels at connecting team members across locations and devices. Features like document management ensure efficient workflows through version control, rights management, and advanced search capabilities. Brokers can also share select records or documents securely with external stakeholders by granting limited access, ensuring confidentiality while streamlining the lending process. Top-Notch Security Salesforce Mortgage CRM prioritizes data security with multi-level protocols, including multi-factor authentication, data encryption, 24/7 monitoring, and an advanced Trust Layer. These features ensure that sensitive client information, such as financial and personal data, is safeguarded, fostering confidence among potential borrowers. Personalized Client Interactions Every client’s journey is unique, and Salesforce empowers brokers to deliver personalized experiences. For example, if a client’s fixed-rate term is nearing its end, Salesforce can automate outreach with refinancing options. This level of personalization improves client satisfaction and strengthens retention strategies. In-Depth Analytics and Visualization Salesforce offers robust analytics tools to provide mortgage professionals with insights into client behavior, market trends, operational performance, and risk factors. Interactive dashboards and customizable reports enable brokers to visualize metrics like loan-to-value ratios, debt-to-income ratios, and delinquency rates. These insights help streamline operations, improve profitability, and mitigate risks. Automation and AI-Powered Assistance Salesforce integrates cutting-edge AI tools to automate routine tasks, segment clients, and deliver tailored recommendations. Brokers can use these tools to identify refinancing opportunities, streamline workflows, and increase efficiency. While advanced AI features may require premium subscriptions, Salesforce also provides no-code automation capabilities. Tools like Salesforce Flow allow users to create automated workflows for tasks such as data entry, follow-ups, and approvals. For complex workflows, Flow Orchestrator enables brokers to coordinate multi-step processes seamlessly. These automation features eliminate manual tasks, freeing brokers to focus on high-value activities. Streamline Your Lending Process with Salesforce Mortgage CRM Whether you’re looking to centralize your data, enhance client relationships, or improve operational efficiency, Salesforce Mortgage CRM offers a comprehensive solution. Its combination of AI, analytics, and automation ensures that your team can work smarter, not harder. Ready to transform your mortgage business? Let Salesforce help you achieve greater efficiency and client satisfaction today. Unlock the Full Potential of Mortgage Lending with Salesforce Mortgage CRM Automation Without AI Salesforce Mortgage CRM streamlines repetitive tasks through its no-code automation capabilities, removing the need for AI. Salesforce Flow allows brokers to create automated workflows triggered by specific events, such as follow-up tasks, reminders, or status updates. For more intricate workflows, Flow Orchestrator provides seamless coordination for multi-step processes, including approvals, ensuring a smooth lending experience. Whether sending monthly statements or routing documents between stakeholders in the approval process, Salesforce Mortgage CRM automates these tasks based on your predefined rules. For enterprises, Financial Services Cloud (FSC) offers pre-built templates to manage mortgage applications. For SMBs, building tailored flows from scratch can be a more efficient and cost-effective solution, offering greater customization without overpaying for unused FSC features. An excellent example of Salesforce’s versatility is integrating scheduling tools like Calendly with borrower portals built in Experience Cloud. These portals allow clients to book appointments while considering time zone differences and send automated reminders, ensuring seamless borrower engagement. Platform Customization and Scalability Salesforce is an agile platform that adapts to your business needs. Need custom fields for underwriting criteria? A new object to track loan pipeline opportunities? Or automated workflows to manage approvals? Salesforce’s intuitive point-and-click configuration tools empower you to customize your system without complex coding, making it a perfect fit for businesses of any size. Seamless Integration with Third-Party Tools Salesforce’s vast ecosystem, powered by the AppExchange, offers over 7,000 add-ons to enhance your CRM’s functionality. From digital signature solutions to mortgage rate comparison tools, these integrations eliminate the need for extensive custom development. If your unique needs aren’t met by existing options, Salesforce’s certified AppExchange partners can help create tailored solutions. Salesforce Products Tailored for Mortgage Lending Firms 1. Financial Services Cloud (FSC) FSC is purpose-built for the financial services industry, including mortgage lending. It provides a 360-degree view of clients, integrating their financial holdings, activities, and loan details. With FSC, lenders can offer personalized recommendations, streamline the underwriting process, and stay compliant with industry regulations using built-in risk management tools. 2. Marketing Cloud Account Engagement (Pardot) Pardot empowers lenders to engage potential borrowers effectively. For example, a mortgage firm targeting first-time homebuyers can use Pardot to create automated, personalized email campaigns, nurturing leads with tailored content about loans, interest rates, and homebuying tips.Real-time insights and Einstein-powered predictive lead scoring help mortgage professionals prioritize high-value leads and track engagement through detailed campaign performance metrics, such as Multi-Touch Attribution Reports. 3. Experience Cloud For mortgage firms looking to provide a self-service option, Experience Cloud allows for building custom portals and websites. Borrowers can use these portals to upload documents, track application statuses, and access loan details. The drag-and-drop design tools and pre-built templates make it easy to create branded, user-friendly interfaces without requiring extensive coding. 4. Service Cloud Customer service is critical in the mortgage process, and Salesforce Service Cloud enhances this experience with omnichannel support across email, chat, and phone. Borrowers can quickly get answers to questions about their applications, while automation retrieves and shares relevant data instantly. This improves response times, reduces manual effort, and ensures borrowers receive

January 7, 2025in AI Tools, Data, Enterprise, Salesforce, Salesforce Development Services, Salesforce Experience Cloud, Salesforce Financial Services Solutions, Salesforce Flow, Salesforce Marketing Cloud, Salesforce Service Cloud

07Jan

Exploring Generative AI

Like most employees at most companies, I wear a few different hats around Tectonic. Whether I’m building a data model, creating and scheduing an email campaign, standing up a platform generative AI is always at my fingertips. At my very core, I’m a marketer. Have been for so long I do it without eveven thinking. Or at least, everyuthing I do has a hat tip to its future marketing needs. Today I want to share some of the AI content generators I’ve been using, am looking to use, or just heard about. But before we rip into the insight, here’s a primer. Types of AI Content Generators ChatGPT, a powerful AI chatbot, drew significant attention upon its November 2022 release. While the GPT-3 language model behind it had existed for some time, ChatGPT made this technology accessible to nontechnical users, showcasing how AI can generate content. Over two years later, numerous AI content generators have emerged to cater to diverse use cases. This rapid development raises questions about the technology’s impact on work. Schools are grappling with fears of plagiarism, while others are embracing AI. Legal debates about copyright and digital media authenticity continue. President Joe Biden’s October 2023 executive order addressed AI’s risks and opportunities in areas like education, workforce, and consumer privacy, underscoring generative AI’s transformative potential. What is AI-Generated Content? AI-generated content, also known as generative AI, refers to algorithms that automatically create new content across digital media. These algorithms are trained on extensive datasets and require minimal user input to produce novel outputs. For instance, ChatGPT sets a standard for AI-generated content. Based on GPT-4o, it processes text, images, and audio, offering natural language and multimodal capabilities. Many other generative AI tools operate similarly, leveraging large language models (LLMs) and multimodal frameworks to create diverse outputs. What are the Different Types of AI-Generated Content? AI-generated content spans multiple media types: Despite their varied outputs, most generative AI systems are built on advanced LLMs like GPT-4 and Google Gemini. These multimodal models process and generate content across multiple formats, with enhanced capabilities evolving over time. How Generative AI is Used Generative AI applications span industries: These tools often combine outputs from various media for complex, multifaceted projects. AI Content Generators AI content generators exist across various media. Below are good examples organized by gen ai type: Written Content Generators Image Content Generators Music Content Generators Code Content Generators Other AI Content Generators These tools showcase how AI-powered content generation is revolutionizing industries, making content creation faster and more accessible. I do hope you will comment below on your favorites, other AI tools not showcased above, or anything else AI-related that is on your mind. Written by Tectonic’s Marketing Operations Director, Shannan Hearne. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

January 7, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Technology

04Jan

From Chatbots to Agentic AI

The transition from LLM-powered chatbots to agentic systems, or agentic AI, can be summed up by the old saying: “Less talk, more action.” Keeping up with advancements in AI can be overwhelming, especially when managing an existing business. The speed and complexity of innovation can make it feel like the first day of school all over again. This insight offers a comprehensive look at AI agents, their components, and key characteristics. The introductory section breaks down the elements that form the term “AI agent,” providing a clear definition. After establishing this foundation, we explore the evolution of LLM applications, particularly the shift from traditional chatbots to agentic systems. The goal is to understand why AI agents are becoming increasingly vital in AI development and how they differ from LLM-powered chatbots. By the end of this guide, you will have a deeper understanding of AI agents, their potential applications, and their impact on organizational workflows. For those of you with a technical background who prefer to get hands-on, click here for the best repository for AI developers and builders. What is an AI Agent? Components of AI Agents To understand the term “AI agent,” we need to examine its two main components. First, let’s consider artificial intelligence, or AI. Artificial Intelligence (AI) refers to non-biological intelligence that mimics human cognition to perform tasks traditionally requiring human intellect. Through machine learning and deep learning techniques, algorithms—especially neural networks—learn patterns from data. AI systems are used for tasks such as detection, classification, and prediction, with content generation becoming a prominent domain due to transformer-based models. These systems can match or exceed human performance in specific scenarios. The second component is “agent,” a term commonly used in both technology and human contexts. In computer science, an agent refers to a software entity with environmental awareness, able to perceive and act within its surroundings. A computational agent typically has the ability to: In human contexts, an agent is someone who acts on behalf of another person or organization, making decisions, gathering information, and facilitating interactions. They often play intermediary roles in transactions and decision-making. To define an AI agent, we combine these two perspectives: it is a computational entity with environmental awareness, capable of perceiving inputs, acting with tools, and processing information using foundation models backed by both long-term and short-term memory. Key Components and Characteristics of AI Agents From LLMs to AI Agents Now, let’s take a step back and understand how we arrived at the concept of AI agents, particularly by looking at how LLM applications have evolved. The shift from traditional chatbots to LLM-powered applications has been rapid and transformative. Form Factor Evolution of LLM Applications Traditional Chatbots to LLM-Powered Chatbots Traditional chatbots, which existed before generative AI, were simpler and relied on heuristic responses: “If this, then that.” They followed predefined rules and decision trees to generate responses. These systems had limited interactivity, with the fallback option of “Speak to a human” for complex scenarios. LLM-Powered Chatbots The release of OpenAI’s ChatGPT on November 30, 2022, marked the introduction of LLM-powered chatbots, fundamentally changing the game. These chatbots, like ChatGPT, were built on GPT-3.5, a large language model trained on massive datasets. Unlike traditional chatbots, LLM-powered systems can generate human-like responses, offering a much more flexible and intelligent interaction. However, challenges remained. LLM-powered chatbots struggled with personalization and consistency, often generating plausible but incorrect information—a phenomenon known as “hallucination.” This led to efforts in grounding LLM responses through techniques like retrieval-augmented generation (RAG). RAG Chatbots RAG is a method that combines data retrieval with LLM generation, allowing systems to access real-time or proprietary data, improving accuracy and relevance. This hybrid approach addresses the hallucination problem, ensuring more reliable outputs. LLM-Powered Chatbots to AI Agents As LLMs expanded, their abilities grew more sophisticated, incorporating advanced reasoning, multi-step planning, and the use of external tools (function calling). Tool use refers to an LLM’s ability to invoke specific functions, enabling it to perform more complex tasks. Tool-Augmented LLMs and AI Agents As LLMs became tool-augmented, the emergence of AI agents followed. These agents integrate reasoning, planning, and tool use into an autonomous, goal-driven system that can operate iteratively within a dynamic environment. Unlike traditional chatbot interfaces, AI agents leverage a broader set of tools to interact with various systems and accomplish tasks. Agentic Systems Agentic systems—computational architectures that include AI agents—embody these advanced capabilities. They can autonomously interact with systems, make decisions, and adapt to feedback, forming the foundation for more complex AI applications. Components of an AI Agent AI agents consist of several key components: Characteristics of AI Agents AI agents are defined by the following traits: Conclusion AI agents represent a significant leap from traditional chatbots, offering greater autonomy, complexity, and interactivity. However, the term “AI agent” remains fluid, with no universal industry standard. Instead, it exists on a continuum, with varying degrees of autonomy, adaptability, and proactive behavior defining agentic systems. Value and Impact of AI Agents The key benefits of AI agents lie in their ability to automate manual processes, reduce decision-making burdens, and enhance workflows in enterprise environments. By “agentifying” repetitive tasks, AI agents offer substantial productivity gains and the potential to transform how businesses operate. As AI agents evolve, their applications will only expand, driving new efficiencies and enabling organizations to leverage AI in increasingly sophisticated ways. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

January 4, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Technology

03Jan

Speed to Launch of Agentforce

Agentforce isn’t just another AI platform that requires months of customization. At most customers, they quickly saw its power, launching transformative generative AI experiences in just days—no AI engineers needed. For companies with larger admin teams, the benefits can be even greater. Unlike other platforms, Agentforce places a strong emphasis on data privacy, building on the trust that Salesforce is known for, making these virtual assistants invaluable. We began with employee-facing use cases, saving our team several hours per week. Now, with Agentforce, we’re seeing even more opportunities to drive efficiencies and better serve our customers. “We’re excited to leverage Agentforce to completely overhaul recruitment and enrollment at Unity Environmental University. Instead of traditional forms or chatbots, our students will soon engage with an autonomous recruitment agent directly on our website, offering personalized support throughout the college application process.”– Dr. Melik Khoury, President & CEO, Unity Environmental University “For first-generation college students, the 1:385 coach-to-student ratio makes personalized guidance challenging. By integrating Agentforce into our platform, we’re deploying cutting-edge solutions to better support students. These agents enable our coaches to focus on high-touch, personalized experiences while handling vital tasks like sharing deadlines and answering common questions—24/7.”– Siva Kumari, CEO, College Possible “Agentforce offers organizations a unique opportunity to move beyond incremental improvements and achieve exponential ROI. By automating customer interactions, improving outcomes, and reducing costs, it integrates data, flows, and user interfaces to mitigate risks and accelerate value creation. This agent-based platform approach allows businesses to harness AI’s full potential, revolutionizing customer engagement and paving the way for exponential growth.”– Rebecca Wettemann, CEO and Principal Analyst, Valoir “Autonomous agents powered by Salesforce’s Agentforce are revolutionizing customer experiences by providing fast, accurate, and personalized support around the clock. With advanced AI making decisions and taking actions autonomously, businesses can resolve customer issues more efficiently, fostering deeper interactions and enhancing satisfaction. This innovation enables companies to reallocate human resources to more complex tasks, boosting individual productivity and scaling business growth. Agentforce is setting new standards for seamless sales, service, marketing, and commerce interactions, reinforcing its leadership in customer experience.”– Michael Fauscette, CEO and Chief Analyst, Arion Research LLC “The best way to predict the future is to invent it.” — Alan Kay, Computer Science Pioneer Technology progresses in what biologists call punctuated equilibrium, with new capabilities slowly emerging from labs and tinkerers until a breakthrough shifts the axis of possibility. These pioneering feats create new paradigms, unleashing waves of innovation—much like the Apple Macintosh, the iPhone, and the Salesforce Platform, which revolutionized the enterprise software-as-a-service (SaaS) model and sparked an entire industry. The Age of Agentforce Begins At Dreamforce 2024, Salesforce Futures reflected on the launch of Agentforce, inspired by visions like the Apple Knowledge Navigator. In 2023, we used this inspiration to craft our Salesforce 2030 film, which showcased the collaboration between humans and autonomous AI agents. Now, with Agentforce, we’re witnessing that vision come to life. Agentforce is a suite of customizable AI agents and tools built on the Salesforce Platform, offering an elegant solution to the complexity of AI deployment. It addresses the challenges of integrating data, models, infrastructure, and applications into a unified system. With powerful tools like Agent Builder and Model Builder, organizations can easily create, customize, and deploy AI agents. Salesforce’s Atlas Reasoning Engine empowers these agents to handle both routine and complex tasks autonomously. A New Era of AI Innovation At Dreamforce 2024, over 10,000 attendees raced to build their own agents using the “Agent Builder” experience, turning verbal instructions into fully functioning agents in under 15 minutes. This wasn’t just another chatbot—it’s a new breed of AI that could transform how businesses operate and deliver superior customer experiences. Companies like Saks, OpenTable, and Wiley have quickly embraced this technology. As Mick Costigan and David Berthy of Salesforce Futures explain, “When we see signals like this, it pushes us toward the future. Soon, we’ll see complex, multi-agent systems solving higher-order challenges, both in the enterprise and in consumer devices.” Shaping the Future Agentforce isn’t just a product—it’s a platform for experimentation. With hundreds of thousands of Salesforce customers soon gaining access, the full potential of these tools will unfold in ways we can’t yet imagine. As with every major technological shift, the real magic will lie in how people use it. Every interaction, every agent deployed, and every problem solved will shape the future in unexpected ways. Platform Evolution Adam Evans, Salesforce SVP of Product, notes that Agentforce builds on the company’s transformation over the past four years, following the pattern of Salesforce’s original disruption of enterprise software. Unlike traditional solutions, Agentforce eliminates the need for customers to build their own AI infrastructure, providing a ready-to-use solution. At the core of Agentforce is the Atlas Reasoning Engine, delivering results that are twice as relevant and 33% more accurate than competing solutions. This engine integrates Salesforce Data Cloud, Flow for automation, and the Einstein Trust Layer for governance. Early Customer Results Early Agentforce deployments highlight how organizations are using autonomous agents to enhance, rather than replace, human workers: George Pokorny, Senior VP of Global Customer Success at OpenTable, shared, “Just saving two minutes on a ten-minute call lets our service reps focus on strengthening customer relationships, thanks to seamless integration with Service Cloud, giving us a unified view of diner preferences and history.” Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

January 3, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Salesforce Data Cloud, Technology

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