Claude Archives - gettectonic.com - Page 2
The Rise of AI Agents: 2024 and Beyond
28Feb

The Rise of AI Agents: 2024 and Beyond

In 2024, we witnessed major breakthroughs in AI agents. OpenAI’s o1 and o3 models demonstrated the ability to deconstruct complex tasks, while Claude 3.5 showcased AI’s capacity to interact with computers like humans—navigating interfaces and running software. These advancements, alongside improvements in memory and learning systems, are pushing AI beyond simple chat interactions into the realm of autonomous systems. AI agents are already making an impact in specialized fields, including legal analysis, scientific research, and technical support. While they excel in structured environments with defined rules, they still struggle with unpredictable scenarios and open-ended challenges. Their success rates drop significantly when handling exceptions or adapting to dynamic conditions. The field is evolving from conversational AI to intelligent systems capable of reasoning and independent action. Each step forward demands greater computational power and introduces new technical challenges. This article explores how AI agents function, their current capabilities, and the infrastructure required to ensure their reliability. What is an AI Agent? An AI agent is a system designed to reason through problems, plan solutions, and execute tasks using external tools. Unlike traditional AI models that simply respond to prompts, agents possess: Understanding the shift from passive responders to autonomous agents is key to grasping the opportunities and challenges ahead. Let’s explore the breakthroughs that have fueled this transformation. 2024’s Key Breakthroughs OpenAI o3’s High Score on the ARC-AGI Benchmark Three pivotal advancements in 2024 set the stage for autonomous AI agents: AI Agents in Action These capabilities are already yielding practical applications. As Reid Hoffman observed, we are seeing the emergence of specialized AI agents that extend human capabilities across various industries: Recent research from Sierra highlights the rapid maturation of these systems. AI agents are transitioning from experimental prototypes to real-world deployment, capable of handling complex business rules while engaging in natural conversations. The Road Ahead: Key Questions As AI agents continue to evolve, three critical questions for us all emerge: The next wave of AI innovation will be defined by how well we address these challenges. By building robust systems that balance autonomy with oversight, we can unlock the full potential of AI agents in the years ahead. 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

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February 28, 2025in AI Tools
The Hidden Risks of Over-Reliance on AI
14Feb

The Hidden Risks of Over-Reliance on AI

Are Marketers Trusting AI Too Much? How to Avoid Losing Your Strategic Edge AI tools have revolutionized how marketers approach research, content creation, and decision-making. However, an overreliance on these tools could undermine critical thinking and strategic planning, leaving marketers vulnerable in a fast-evolving landscape. Here’s how to balance the power of automation with human insight. The Rise of AI in Search and Marketing In late December, SEO consultancy Previsible shared a striking report: Google’s search dominance has plateaued and is now being challenged by AI-assisted search tools. These tools, such as ChatGPT, Claude, and Google’s own AI-enhanced search, are growing in popularity due to their ability to deliver contextually relevant and personalized results. Unlike traditional search, which relies on keyword matching, AI-driven search processes intent and context. This shift is reshaping how users find information and make decisions. How AI Is Changing User Behavior The increasing sophistication of AI tools brings both opportunities and risks. Users often trust AI-generated outputs without question, assuming they’re accurate and complete. Traditional search, by contrast, forces users to critically analyze and filter multiple sources. This blind trust in AI mirrors the concept of “System 1 thinking,” as described by Nobel Prize-winning psychologist Daniel Kahneman in Thinking, Fast and Slow. As AI models like ChatGPT operate primarily as “System 1 thinkers,” users risk adopting a similar approach, bypassing critical analysis in favor of convenience. The Hidden Risks of Over-Reliance on AI Younger marketers may be especially at risk of falling into this trap. Many are using AI tools like ChatGPT to summarize information or generate ideas, often without questioning the accuracy of the outputs. For B2B marketers, the allure of AI lies in its speed and perceived accuracy. However, this reliance on automation could lead to a generation of marketers who lack the ability—or inclination—to think strategically. The danger is clear: unchecked dependence on AI tools could foster a “groupthink” mentality, where creativity and critical thinking are sidelined. Without intervention, marketing departments risk becoming overly reliant on tools that were designed to enhance human efforts, not replace them. How Marketing Leaders Can Address This Threat To counter this trend, marketing leaders must actively promote the development of strategic skills. Here’s how: In a world increasingly driven by AI, marketers who can blend automation with strategic thinking will be best positioned for success. Using AI to Enhance, Not Replace, Strategic Thinking AI should empower marketers to make better decisions—not serve as the sole decision-maker. As one professor aptly put it, “Use AI to become a better student, not to be the student.” The key is balance. By combining the intuitive capabilities of AI with the deliberate, analytical approach of System 2 thinking, marketers can leverage technology without sacrificing creativity or strategy. In short, AI is a tool—not a replacement for human ingenuity. Those who recognize this distinction will thrive in an increasingly automated world. 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

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February 14, 2025in AI Tools, Google, Technology
pydanticai
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

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February 3, 2025in Salesforce
Agentforce Custom AI Agents
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

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January 22, 2025in AI Tools, Data, Enterprise, Salesforce
Generative AI Energy Consumption Rises
09Jan

Generative AI Tools

Generative AI Tools: A Comprehensive Overview of Emerging Capabilities The widespread adoption of generative AI services like ChatGPT has sparked immense interest in leveraging these tools for practical enterprise applications. Today, nearly every enterprise app integrates generative AI capabilities to enhance functionality and efficiency. A broad range of AI, data science, and machine learning tools now support generative AI use cases. These tools assist in managing the AI lifecycle, governing data, and addressing security and privacy concerns. While such capabilities also aid in traditional AI development, this discussion focuses on tools specifically designed for generative AI. Not all generative AI relies on large language models (LLMs). Emerging techniques generate images, videos, audio, synthetic data, and translations using methods such as generative adversarial networks (GANs), diffusion models, variational autoencoders, and multimodal approaches. Here is an in-depth look at the top categories of generative AI tools, their capabilities, and notable implementations. It’s worth noting that many leading vendors are expanding their offerings to support multiple categories through acquisitions or integrated platforms. Enterprises may want to explore comprehensive platforms when planning their generative AI strategies. 1. Foundation Models and Services Generative AI tools increasingly simplify the development and responsible use of LLMs, initially pioneered through transformer-based approaches by Google researchers in 2017. 2. Cloud Generative AI Platforms Major cloud providers offer generative AI platforms to streamline development and deployment. These include: 3. Use Case Optimization Tools Foundation models often require optimization for specific tasks. Enterprises use tools such as: 4. Quality Assurance and Hallucination Mitigation Hallucination detection tools address the tendency of generative models to produce inaccurate or misleading information. Leading tools include: 5. Prompt Engineering Tools Prompt engineering tools optimize interactions with LLMs and streamline testing for bias, toxicity, and accuracy. Examples include: 6. Data Aggregation Tools Generative AI tools have evolved to handle larger data contexts efficiently: 7. Agentic and Autonomous AI Tools Developers are creating tools to automate interactions across foundation models and services, paving the way for autonomous AI. Notable examples include: 8. Generative AI Cost Optimization Tools These tools aim to balance performance, accuracy, and cost effectively. Martian’s Model Router is an early example, while traditional cloud cost optimization platforms are expected to expand into this area. Generative AI tools are rapidly transforming enterprise applications, with foundational, cloud-based, and domain-specific solutions leading the way. By addressing challenges like accuracy, hallucination, and cost, these tools unlock new potential across industries and use cases, enabling enterprises to stay ahead in the AI-driven landscape. 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

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January 9, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake
Scope of Generative AI
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

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January 7, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Technology
26Dec

Why Build a General-Purpose Agent?

A general-purpose LLM agent serves as an excellent starting point for prototyping use cases and establishing the foundation for a custom agentic architecture tailored to your needs. What is an LLM Agent? An LLM (Large Language Model) agent is a program where execution logic is governed by the underlying model. Unlike approaches such as few-shot prompting or fixed workflows, LLM agents adapt dynamically. They can determine which tools to use (e.g., web search or code execution), how to use them, and iterate based on results. This adaptability enables handling diverse tasks with minimal configuration. Agentic Architectures Explained:Agentic systems range from the reliability of fixed workflows to the flexibility of autonomous agents. For instance: Your architecture choice will depend on the desired balance between reliability and flexibility for your use case. Building a General-Purpose LLM Agent Step 1: Select the Right LLM Choosing the right model is critical for performance. Evaluate based on: Model Recommendations (as of now): For simpler use cases, smaller models running locally can also be effective, but with limited functionality. Step 2: Define the Agent’s Control Logic The system prompt differentiates an LLM agent from a standalone model. This prompt contains rules, instructions, and structures that guide the agent’s behavior. Common Agentic Patterns: Starting with ReAct or Plan-then-Execute patterns is recommended for general-purpose agents. Step 3: Define the Agent’s Core Instructions To optimize the agent’s behavior, clearly define its features and constraints in the system prompt: Example Instructions: Step 4: Define and Optimize Core Tools Tools expand an agent’s capabilities. Common tools include: For each tool, define: Example: Implementing an Arxiv API tool for scientific queries. Step 5: Memory Handling Strategy Since LLMs have limited memory (context window), a strategy is necessary to manage past interactions. Common approaches include: For personalization, long-term memory can store user preferences or critical information. Step 6: Parse the Agent’s Output To make raw LLM outputs actionable, implement a parser to convert outputs into a structured format like JSON. Structured outputs simplify execution and ensure consistency. Step 7: Orchestrate the Agent’s Workflow Define orchestration logic to handle the agent’s next steps after receiving an output: Example Orchestration Code: pythonCopy codedef orchestrator(llm_agent, llm_output, tools, user_query): while True: action = llm_output.get(“action”) if action == “tool_call”: tool_name = llm_output.get(“tool_name”) tool_params = llm_output.get(“tool_params”, {}) if tool_name in tools: try: tool_result = tools[tool_name](**tool_params) llm_output = llm_agent({“tool_output”: tool_result}) except Exception as e: return f”Error executing tool ‘{tool_name}’: {str(e)}” else: return f”Error: Tool ‘{tool_name}’ not found.” elif action == “return_answer”: return llm_output.get(“answer”, “No answer provided.”) else: return “Error: Unrecognized action type from LLM output.” This orchestration ensures seamless interaction between tools, memory, and user queries. When to Consider Multi-Agent Systems A single-agent setup works well for prototyping but may hit limits with complex workflows or extensive toolsets. Multi-agent architectures can: Starting with a single agent helps refine workflows, identify bottlenecks, and scale effectively. By following these steps, you’ll have a versatile system capable of handling diverse use cases, from competitive analysis to automating workflows. 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

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December 26, 2024in Data
AI Agents Set to Break Through in 2025
20Dec

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. 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

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December 20, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Technology
AI Agents
19Dec

AI Agents Unveiled

A comprehensive research effort has led to the introduction of a simplified model designed to address two fundamental questions: Building upon this model, an additional practical question is examined: 1. Why Does It Matter? Understanding the concept of an “AI agent” can be challenging, particularly for individuals who simply use AI in their daily workflows. The distinction between AI agents, copilots, and assistants is critical in determining the nature of the AI tools being used for work or personal applications. Those seeking a technical breakdown may proceed directly to the “Agentic AI Features” section. For AI power users or professionals responsible for AI implementation within an organization, recognizing the emerging AI tools and their functionalities is essential. Similarly, individuals working at AI startups should understand their product’s positioning within the market and be aware of industry trends that may impact future development. The year 2025 is widely anticipated as the period when AI agents will become enterprise-ready and well-understood by the market. This development is viewed as part of a long-term trend: However, despite these forecasts, the term “AI agent” remains vague, requiring further clarification. 2. AI Agent Definitions A widely accepted definition from Gartner Innovation Insights (April 2024) states: “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.” This definition highlights five key capabilities, with autonomy serving as the distinguishing factor that separates AI agents from other software with similar functionalities. MarketsandMarkets expands upon this definition by adding two additional high-level characteristics: “AI agents operate within specific environments, interfacing with users, systems, or other agents, and are characterized by their capacity for adaptive learning, context-aware processing, and autonomous function across varied applications.” Autonomous agents are significant because they have the potential to function as employees or coworkers. Furthermore, their ability to collaborate with other AI agents fosters the development of human-AI teams capable of human-like teamwork. 3. AI Agents vs. AI Workflows vs. AI Copilots AI-driven software entities do not necessarily need to be fully agentic to be classified as AI agents. Many exist as semi-autonomous agents, possessing memory and goal-driven decision-making but lacking external tools, sensors, or multi-agent interaction capabilities (refer to Section 5 for specific examples). Currently, the distinction between AI agents and other AI tools is not universally defined. Instead, this differentiation exists across multiple dimensions, including decision types, action types, and other functional capabilities. The following sections explore these distinctions further. 3.1. Business Perspective: AI Workflows and Agents A 2024 article by Anthropic highlights an important distinction: For companies implementing AI tools, even basic AI workflows provide value. However, these workflows introduce challenges for developers and users alike. The evolution of agentic AI platforms could alleviate these challenges, enhancing automation capabilities. 3.2. Personal Perspective: AI Copilots and Agents From an individual user’s perspective, an AI copilot often suffices without requiring the full capabilities of an AI agent. Copilots support decision-making by offering context-specific recommendations and working collaboratively with users over multiple iterations. AI copilots exhibit characteristics such as: Capabilities such as autonomy and goal-oriented behavior define AI agents. The ability to interact dynamically with an environment—beyond simple information retrieval—further differentiates agents from copilots and assistants. 4. Agentic AI Capabilities and Features Chart The following distinctions emerge from the “agentic capabilities model”: One area of debate concerns memory. Some sources claim memory is exclusive to AI agents, while others argue that true copilots must possess memory to offer personalized assistance. This distinction is often influenced by business marketing strategies rather than purely technical considerations. 5. Mapping AI Tools to Agentic Capabilities AI tools vary widely in versatility. Some specialize in narrow tasks, while others serve broad use cases. 5.1. Specialized AI Tools Many widely used AI tools focus on specific tasks, such as: These tools function as AI-powered utilities rather than true AI assistants, copilots, or agents. 5.2. Advanced Versatile AI Assistants More versatile AI tools, such as ChatGPT, Claude, Gemini, and POE, enable broad conversations and contextual processing. Notably: The distinction between AI assistants, copilots, and agents will continue evolving as AI technology advances. Understanding these differences is crucial for businesses and users seeking to maximize AI’s potential in various 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

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December 19, 2024in Agentic AI, AI Tools, Data, Enterprise, Google, Technology
Google Prepares AI-Powered Jarvis Agent
18Dec

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business operations. 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

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December 18, 2024in Data, Enterprise, Google, Salesforce
Shift From AI Agents to AI Agent Tool Use
06Dec

Shift From AI Agents to AI Agent Tool Use

The focus of AI development is evolving—from creating autonomous AI Agents to expanding the tools they use, significantly boosting their capabilities and flexibility. Tool access, described and utilized through natural language, is now a critical factor in the functionality and reach of these agents, enabling them to tackle increasingly complex tasks. The Role of Tools in AI Agent Effectiveness AI Agents thrive in user-specific environments like desktops, where rich context enables them to perform tasks more effectively. Instead of just scaling model power, leading AI companies such as OpenAI and Anthropic are pivoting toward tool-enabled frameworks, allowing agents to interact directly with computer GUI navigation for multi-step workflows. This shift positions tools as essential components of AI ecosystems, bridging the gap between raw computational power and actionable user outcomes. OpenAI’s “Operator” and the Future of Autonomous Agents OpenAI is set to release Operator, an AI Agent designed to autonomously perform tasks such as coding and travel booking on a user’s computer. Available as a research preview in January, Operator is part of a broader industry trend toward Agentic Tools that enable seamless, multi-step task execution with minimal user oversight. This approach reflects a shift toward real-time AI capabilities, moving beyond model-centric enhancements to unlock practical, task-driven use cases for AI Agents. Anthropic’s Desktop AI Agent Anthropic is also advancing the field with a reference implementation for computer use, enabling rapid deployment of AI-powered desktop agents. This implementation allows users to leverage Claude, Anthropic’s AI model, in a virtual machine environment with powerful tools for GUI interaction, command-line operations, and file management. Key Features This system provides a controlled yet versatile environment for AI Agents to operate in a safe, flexible, and efficient manner. Technical Implementation To deploy Anthropic’s computer-use demo: bashCopy codeexport ANTHROPIC_API_KEY=%your_api_key% docker run -e ANTHROPIC_API_KEY=<Your Anthropic API Key Goes Here> -v $HOME/.anthropic:/home/computeruse/.anthropic -p 5900:5900 -p 8501:8501 -p 6080:6080 -p 8080:8080 -it ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest Tools Overview Each session starts fresh but maintains state within the session, enabling smooth task execution. The Bigger Picture AI Agents are no longer defined solely by their autonomous capabilities. Instead, their success now hinges on how effectively they utilize tools to extend their reach and flexibility. Whether it’s through GUI navigation, command-line interactions, or file management, tool access is transforming the way AI Agents deliver value to users. By focusing on tools rather than just AI model power, companies like OpenAI and Anthropic are building the foundation for a new era of AI-driven productivity. Expect to see more advancements in Agentic Tool design, as the emphasis shifts from autonomy to capability. 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

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December 6, 2024in Salesforce
AI for Product Builders
07Nov

AI for Product Builders

Practical AI for Product Builders: Beyond the Hype Cutting Through the White AI Noise AI dominates tech conversations—yet much of the discussion feels abstract. What’s often missing is a practical guide for integrating AI into real products. How can AI: ✔ Enhance user experiences?✔ Enable new capabilities?✔ Improve existing features (faster/cheaper/better)?✔ And—just as importantly—when shouldn’t you use it? As product and UX professionals, we solve user problems with the best tools available. AI expands that toolkit in surprising ways. This guide breaks down key techniques so you can confidently say: AI Basics: What We’re Really Talking About Large Language Models (LLMs) = Word Prediction Engines When people say “AI” today, they usually mean LLMs—the technology behind ChatGPT, Claude, Gemini, and others. How they work: Comparison: An huge team of forgetful interns. Each time you chat, you’re talking to a new intern who reads the previous conversation before responding. Key limitations: Moving Beyond Chatbots Chatbots were the first wave of AI products, but conversational interfaces aren’t always the best solution. Example: AI Image Editing Problem: You generate an image of a cat in a café but want to tweak one poster. ✅ GUI + AI (e.g., Photoshop Generative Fill) ❌ Chat-only (e.g., ChatGPT + DALL-E) Lesson: Direct manipulation (GUI) + AI > Chat-only interfaces for many use cases. The AI Toolbox: Key Techniques 1. LLM Prompting What: Basic text-in, text-out AI (like ChatGPT). Best for: Limitations: 2. Image Generation What: Models like DALL-E, Imagen, Midjourney create images from text. Best for: Key Insight: 3. Structured Output & Tool Use Problem: Raw LLM output is messy for apps. Solution: Force responses into predefined formats (e.g., JSON). Example: Tool Use: Lets LLMs “choose” next actions (e.g., search orders vs. ask clarifying questions). 4. Embeddings What: Convert text into numerical vectors (“meaning coordinates”). Why it matters: Use cases: 5. Retrieval-Augmented Generation (RAG) What: Combine LLMs with your own data. How it works: Benefits: When Not to Use AI Red Flags in AI Product Design 🚩 “We need an AI strategy” → Focus on user needs, not tech for tech’s sake.🚩 Sparkly “AI” buttons → AI should feel seamless, not tacked-on. Remember: Many “smart” features (e.g., Apple’s “Magic” tools) don’t require AI. The Path Forward AI enables new possibilities, but great products still solve real problems. Use these tools to: AI isn’t magic. But used thoughtfully, it can help build products that are. Next steps: 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

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November 7, 2024in AI Tools, Data, Enterprise, Technology
Consider AI Agents Personas
04Nov

Consider AI Agents Personas

Treating AI Agents as Personas: Introducing the Era of Agent-Computer Interaction The UX landscape is evolving. While the design community has quickly adopted Large Language Models (LLMs) as tools, we’ve yet to fully grasp their transformative potential. With AI agents now deeply embedded in digital products, they are shifting from tools to active participants in our digital ecosystems. This change demands a new design paradigm—one that views AI agents not just as extensions of human users but as independent personas in their own right. The Rise of Agent-Computer Interaction AI agents represent a new class of users capable of navigating interfaces autonomously and completing complex tasks. This marks the dawn of Agent-Computer Interaction (ACI)—a paradigm where user experience design encompasses the needs of both human users and AI agents. Humans still play a critical role in guiding and supervising these systems, but AI agents must now be treated as distinct personas with unique goals, abilities, and requirements. This shift challenges UX designers to consider how these agents interact with interfaces and perform their tasks, ensuring they are equipped with the information and resources necessary to operate effectively. Understanding AI Agents AI agents are intelligent systems designed to reason, plan, and work across platforms with minimal human intervention. As defined during Google I/O, these agents retain context, anticipate needs, and execute multi-step processes. Advances in AI, such as Anthropic’s Claude and its ability to interact with graphical interfaces, have unlocked new levels of agency. Unlike earlier agents that relied solely on APIs, modern agents can manipulate graphical user interfaces much like human users, enabling seamless interaction with browser-based applications. This capability creates opportunities for new forms of interaction but also demands thoughtful design choices. Two Interaction Approaches for AI Agents Design teams must evaluate these methods based on the task’s complexity and transparency requirements, striking the right balance between efficiency and oversight. Designing Experiences Considering AI Agents Personas As AI agents transition into active users, UX design must expand to accommodate their specific needs. Much like human personas, AI agents require a deep understanding of their capabilities, limitations, and workflows. Creating AI Agent Personas Developing personas for AI agents involves identifying their unique characteristics: These personas inform interface designs that optimize agent workflows, ensuring both agents and humans can collaborate effectively. New UX Research Methodologies UX teams should embrace innovative research techniques, such as A/B testing interfaces for agent performance and monitoring their interaction patterns. While AI agents lack sentience, they exhibit behaviors—reasoning, planning, and adapting—that require careful study and design consideration. Shaping the AI Mind AI agents derive their reasoning capabilities from Large Language Models (LLMs), but their behavior and effectiveness are shaped by UX design. Designers have a unique role in crafting system prompts and developing feedback loops that refine LLM behavior over time. Key Areas for Designer Involvement: This work positions UX professionals as co-creators of AI intelligence, shaping not just interfaces but the underlying behaviors that drive agent interactions. Keeping Humans in the Loop Despite the rise of AI agents, human oversight and control remain essential. UX practitioners must prioritize transparency and trust in agent-driven systems. Key Considerations: Using tools like agentic experience maps—blueprints that visualize the interactions between humans, agents, and products—designers can ensure AI systems remain human-centered. A New Frontier for UX The emergence of AI agents heralds a shift as significant as the transition from desktop to mobile. Just as mobile devices unlocked new opportunities for interaction, AI agents are poised to redefine digital experiences in ways we can’t yet fully predict. By embracing Agent-Computer Interaction, UX designers have an unprecedented opportunity to shape the future of human-AI collaboration. Those who develop expertise in designing for these intelligent agents will lead the way in creating systems that are not only powerful but also deeply human-centered. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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November 4, 2024in Data, Google
Scaling Generative AI
23Oct

Scaling Generative AI

Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
AI Agents and Digital Transformation
23Oct

Ready for AI Agents

Brands that can effectively integrate agentic AI into their operations stand to gain a significant competitive edge. But as with any innovation, success will depend on balancing the promise of automation with the complexities of trust, privacy, and user experience.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce
gettectonic.com