Multi-Agent Systems Archives - gettectonic.com
Speed to Launch of Agentforce
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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 3, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Salesforce Data Cloud, Technology0 Comments
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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 26, 2024in Data0 Comments

Empowering LLMs with a Robust Agent Framework

PydanticAI: Empowering LLMs with a Robust Agent Framework As the Generative AI landscape evolves at a historic pace, AI agents and multi-agent systems are expected to dominate 2025. Industry leaders like AWS, OpenAI, and Microsoft are racing to release frameworks, but among these, PydanticAI stands out for its unique integration of the powerful Pydantic library with large language models (LLMs). Why Pydantic Matters Pydantic, a Python library, simplifies data validation and parsing, making it indispensable for handling external inputs such as JSON, user data, or API responses. By automating data checks (e.g., type validation and format enforcement), Pydantic ensures data integrity while reducing errors and development effort. For instance, instead of manually validating fields like age or email, Pydantic allows you to define models that automatically enforce structure and constraints. Consider the following example: pythonCopy codefrom pydantic import BaseModel, EmailStr class User(BaseModel): name: str age: int email: EmailStr user_data = {“name”: “Alice”, “age”: 25, “email”: “[email protected]”} user = User(**user_data) print(user.name) # Alice print(user.age) # 25 print(user.email) # [email protected] If invalid data is provided (e.g., age as a string), Pydantic throws a detailed error, making debugging straightforward. What Makes PydanticAI Special Building on Pydantic’s strengths, PydanticAI brings structured, type-safe responses to LLM-based AI agents. Here are its standout features: Building an AI Agent with PydanticAI Below is an example of creating a PydanticAI-powered bank support agent. The agent interacts with customer data, evaluates risks, and provides structured advice. Installation bashCopy codepip install ‘pydantic-ai-slim[openai,vertexai,logfire]’ Example: Bank Support Agent pythonCopy codefrom dataclasses import dataclass from pydantic import BaseModel, Field from pydantic_ai import Agent, RunContext from bank_database import DatabaseConn @dataclass class SupportDependencies: customer_id: int db: DatabaseConn class SupportResult(BaseModel): support_advice: str = Field(description=”Advice for the customer”) block_card: bool = Field(description=”Whether to block the customer’s card”) risk: int = Field(description=”Risk level of the query”, ge=0, le=10) support_agent = Agent( ‘openai:gpt-4o’, deps_type=SupportDependencies, result_type=SupportResult, system_prompt=( “You are a support agent in our bank. Provide support to customers and assess risk levels.” ), ) @support_agent.system_prompt async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str: customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id) return f”The customer’s name is {customer_name!r}” @support_agent.tool async def customer_balance(ctx: RunContext[SupportDependencies], include_pending: bool) -> float: return await ctx.deps.db.customer_balance( id=ctx.deps.customer_id, include_pending=include_pending ) async def main(): deps = SupportDependencies(customer_id=123, db=DatabaseConn()) result = await support_agent.run(‘What is my balance?’, deps=deps) print(result.data) result = await support_agent.run(‘I just lost my card!’, deps=deps) print(result.data) Key Concepts Why PydanticAI Matters PydanticAI simplifies the development of production-ready AI agents by bridging the gap between unstructured LLM outputs and structured, validated data. Its ability to handle complex workflows with type safety and its seamless integration with modern AI tools make it an essential framework for developers. As we move toward a future dominated by multi-agent AI systems, PydanticAI is poised to be a cornerstone in building reliable, scalable, and secure AI-driven applications. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 10, 2024in AI Tools, Data, Generative AI0 Comments
AI Agents and Digital Transformation
18Oct

AI Agents and Digital Transformation

In the rapidly developingng world of technology, Artificial Intelligence (AI) is revolutionizing industries and reshaping how we interact with digital systems. One of the most promising advancements within AI is the development of AI agents. These intelligent entities, often powered by Large Language Models (LLMs), are driving the next wave of digital transformation by enabling automation, personalization, and enhanced decision-making across various sectors. AI Agents and digital transformation are here to stay. What is an AI Agent? An AI agent, or intelligent agent, is a software entity capable of perceiving its environment, reasoning about its actions, and autonomously working toward specific goals. These agents mimic human-like behavior using advanced algorithms, data processing, and machine-learning models to interact with users and complete tasks. LLMs to AI Agents — An Evolution The evolution of AI agents is closely tied to the rise of Large Language Models (LLMs). Models like GPT (Generative Pre-trained Transformer) have showcased remarkable abilities to understand and generate human-like text. This development has enabled AI agents to interpret complex language inputs, facilitating advanced interactions with users. Key Capabilities of LLM-Based Agents LLM-powered agents possess several key advantages: Two Major Types of LLM Agents LLM agents are classified into two main categories: Multi-Agent Systems (MAS) A Multi-Agent System (MAS) is a group of autonomous agents working together to achieve shared goals or solve complex problems. MAS applications span robotics, economics, and distributed computing, where agents interact to optimize processes. AI Agent Architecture and Key Elements AI agents generally follow a modular architecture comprising: Learning Strategies for LLM-Based Agents AI agents utilize various learning techniques, including supervised, reinforcement, and self-supervised learning, to adapt and improve their performance in dynamic environments. How Autonomous AI Agents Operate Autonomous AI agents act independently of human intervention by perceiving their surroundings, reasoning through possible actions, and making decisions autonomously to achieve set goals. AI Agents’ Transformative Power Across Industries AI agents are transforming numerous industries by automating tasks, enhancing efficiency, and providing data-driven insights. Here’s a look at some key use cases: Platforms Powering AI Agents The Benefits of AI Agents and Digital Transformation AI agents offer several advantages, including: The Future of AI Agents The potential of AI agents is immense, and as AI technology advances, we can expect more sophisticated agents capable of complex reasoning, adaptive learning, and deeper integration into everyday tasks. The future promises a world where AI agents collaborate with humans to drive innovation, enhance efficiency, and unlock new opportunities for growth in the digital age. AI Agents and Digital Transformation By partnering with AI development specialists at Tectonic, organizations can access cutting-edge solutions tailored to their needs, positioning themselves to stay ahead in the rapidly evolving AI-driven market. Agentforce Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 18, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Salesforce Copilots, Technology
Multi AI Agent Systems
15Oct

Multi AI Agent Systems

Building Multi-AI Agent Systems: A Comprehensive Guide As technology advances at an unprecedented pace, Multi-AI Agent systems are emerging as a transformative approach to creating more intelligent and efficient applications. This guide delves into the significance of Multi-AI Agent systems and provides a step-by-step tutorial on building them using advanced frameworks like LlamaIndex and CrewAI. What Are Multi-AI Agent Systems? Multi-AI Agent systems are a groundbreaking development in artificial intelligence. Unlike single AI agents that operate independently, these systems consist of multiple autonomous agents that collaborate to tackle complex tasks or solve intricate problems. Key Features of Multi-AI Agent Systems: Applications of Multi-AI Agent Systems: Multi-agent systems are versatile and impactful across industries, including: The Workflow of a Multi-AI Agent System Building an effective Multi-AI Agent system requires a structured approach. Here’s how it works: Building Multi-AI Agent Systems with LlamaIndex and CrewAI Step 1: Define Agent Roles Clearly define the roles, goals, and specializations of each agent. For example: Step 2: Initiate the Workflow Establish a seamless workflow for agents to perform their tasks: Step 3: Leverage CrewAI for Collaboration CrewAI enhances collaboration by enabling autonomous agents to work together effectively: Step 4: Integrate LlamaIndex for Data Handling Efficient data management is crucial for agent performance: Understanding AI Inference and Training Multi-AI Agent systems rely on both AI inference and training: Key Differences: Aspect AI Training AI Inference Purpose Builds the model. Uses the model for tasks. Process Data-driven learning. Real-time decision-making. Compute Needs Resource-intensive. Optimized for efficiency. Both processes are essential: training builds the agents’ capabilities, while inference ensures swift, actionable results. Tools for Multi-AI Agent Systems LlamaIndex An advanced framework for efficient data handling: CrewAI A collaborative platform for building autonomous agents: Practical Example: Multi-AI Agent Workflow Conclusion Building Multi-AI Agent systems offers unparalleled opportunities to create intelligent, responsive, and efficient applications. By defining clear agent roles, leveraging tools like CrewAI and LlamaIndex, and integrating robust workflows, developers can unlock the full potential of these systems. As industries continue to embrace this technology, Multi-AI Agent systems are set to revolutionize how we approach problem-solving and task execution. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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October 15, 2024in Artificial Intelligence, Data, Technology

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