Multi-Agent Systems Archives - gettectonic.com
AI Agents as Tools of Trust
03Apr

5 Attributes of Agents

Salesforce predicts you will have deployed over 100 AI Agents by the end of the year. What are they? What do they do? Why do you need them? Let’s explore the 5 key attributes of AI Agents. What Is an AI Agent? An AI agent is an intelligent software system that uses artificial intelligence to autonomously pursue goals and complete tasks on behalf of users. Unlike traditional programs, AI agents exhibit reasoning, planning, memory, and decision-making abilities, allowing them to learn, adapt, and operate with minimal human intervention. These agents leverage generative AI and foundation models to process multimodal inputs—such as text, voice, video, and code—enabling them to:✔ Understand and analyze information✔ Make logical decisions✔ Learn from interactions✔ Collaborate with other agents✔ Automate complex workflows From customer service bots to autonomous research assistants, AI agents are transforming industries by handling tasks that once required human intelligence. Key Features of an AI Agent Modern AI agents go beyond simple automation—they possess advanced cognitive and interactive capabilities: Feature Description Reasoning Uses logic to analyze data, solve problems, and make decisions. Acting Executes tasks—whether digital (sending messages, updating databases) or physical (controlling robots). Observing Gathers real-time data via sensors, NLP, or computer vision to understand its environment. Planning Strategizes steps to achieve goals, anticipating obstacles and optimizing actions. Collaborating Works with humans or other AI agents to accomplish shared objectives. Self-Refining Continuously improves through machine learning and feedback. AI Agents vs. AI Assistants vs. Bots While all three automate tasks, they differ in autonomy, complexity, and learning ability: Aspect AI Agent AI Assistant Bot Purpose Autonomously performs complex tasks. Assists users with guided interactions. Follows pre-set rules for simple tasks. Autonomy High—makes independent decisions. Medium—requires user input. Low—limited to scripted responses. Learning Adapts and improves over time. May learn from interactions. Minimal or no learning. Interaction Proactive and goal-driven. Reactive (responds to user requests). Trigger-based (e.g., chatbots). Example: How Do AI Agents Work? AI agents operate through a structured framework: Types of AI Agents AI agents can be classified based on interaction style and collaboration level: 1. By Interaction 2. By Number of Agents Benefits of AI Agents ✅ 24/7 Automation – Handles repetitive tasks without fatigue.✅ Enhanced Decision-Making – Analyzes vast data for insights.✅ Scalability – Manages workflows across industries.✅ Continuous Learning – Improves performance over time. The Future of AI Agents As AI advances, agents will become more autonomous, intuitive, and integrated into daily workflows—from healthcare diagnostics to smart city management. Want to see AI agents in action? Explore 300+ real-world AI use cases from leading organizations. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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April 3, 2025in Artificial Intelligence, Data, Generative AI, Salesforce0 Comments
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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 28, 2025in AI Tools
The Event-Driven Paradigm for Next-Generation AI Agents
24Feb

The Event-Driven Paradigm for Next-Generation AI Agents

The Infrastructure Imperative for AI Evolution The enterprise landscape stands at an inflection point where AI agents promise autonomous decision-making and adaptive workflows at scale. However, the critical barrier to realizing this potential isn’t model sophistication—it’s architectural. True agentic systems require: These requirements fundamentally represent an infrastructure challenge that demands event-driven architecture (EDA) as the foundational framework for agent deployment and scaling. The Three Waves of AI Evolution First Wave: Predictive Models Characterized by: These deterministic systems excelled at specialized tasks but proved rigid and unscalable across business functions. Second Wave: Generative Models Marked by breakthroughs in: However, these models remained constrained by: Third Wave: Agentic Systems Emerging capabilities include: This evolution shifts focus from model architecture to system architecture, where EDA becomes the critical enabler. The Compound AI Advantage Modern agent systems combine multiple architectural components: This compound approach overcomes the limitations of standalone models through: Event-Driven Architecture: The Nervous System for Agents Core EDA Principles for AI Systems Implementation Benefits Architectural Patterns for Agentic Systems 1. Reflective Processing <img src=”reflection-pattern.png” width=”400″ alt=”Reflection design pattern diagram”> Agents employ meta-cognition to: 2. Dynamic Tool Orchestration <img src=”tool-use-pattern.png” width=”400″ alt=”Tool use design pattern diagram”> Capabilities include: 3. Hierarchical Planning <img src=”planning-pattern.png” width=”400″ alt=”Planning design pattern diagram”> Features: 4. Collaborative Multi-Agent Systems <img src=”multi-agent-pattern.png” width=”400″ alt=”Multi-agent collaboration diagram”> Enables: The Enterprise Integration Challenge Critical Success Factors Implementation Roadmap Phase 1: Foundation Phase 2: Capability Expansion Phase 3: Optimization The Competitive Imperative Enterprise readiness data reveals: Early adopters of event-driven agent architectures gain: The transition to agentic operations represents not just technological evolution but fundamental business transformation. Organizations that implement EDA foundations today will dominate the AI-powered enterprise landscape of tomorrow. Those failing to adapt risk joining the legacy systems they currently maintain—as historical footnotes in the annals of digital transformation. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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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February 24, 2025in Data, Enterprise, Technology
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 3, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Salesforce Data Cloud, 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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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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December 26, 2024in Data
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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 19, 2024in Agentic AI, AI Tools, Data, Enterprise, Google, Technology

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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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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December 10, 2024in AI Tools, Data, Generative AI
Evaluating RAG With Needle in Haystack Test
31Oct

Agentic RAG

Agentic RAG: The Next Evolution of AI-Powered Knowledge Retrieval From RAG to Agentic RAG: A Paradigm Shift in AI Applications While Retrieval-Augmented Generation (RAG) dominated AI advancements in 2023, agentic workflows are now driving the next wave of innovation in 2024. By integrating AI agents into RAG pipelines, developers can build more powerful, adaptive, and intelligent LLM-powered applications. This article explores:✔ What is Agentic RAG?✔ How it works (single-agent vs. multi-agent architectures)✔ Implementation methods (function calling vs. agent frameworks)✔ Enterprise adoption & real-world use cases✔ Benefits & limitations Understanding the Foundations: RAG & AI Agents What is Retrieval-Augmented Generation (RAG)? RAG enhances LLMs by retrieving external knowledge before generating responses, reducing hallucinations and improving accuracy. Traditional (Vanilla) RAG Pipeline: Limitations of Vanilla RAG: ❌ Single knowledge source (no dynamic tool integration).❌ One-shot retrieval (no iterative refinement).❌ No reasoning over retrieved data quality. What Are AI Agents? AI agents are autonomous LLM-driven systems with: The ReAct Framework (Reason + Act) What is Agentic RAG? Agentic RAG embeds AI agents into RAG pipelines, enabling:✅ Multi-source retrieval (databases, APIs, web search).✅ Dynamic query refinement (self-correcting searches).✅ Validation of results (quality checks before generation). How Agentic RAG Works Instead of a static retrieval step, an AI agent orchestrates: Agentic RAG Architectures 1. Single-Agent RAG (Router) 2. Multi-Agent RAG (Orchestrated Workflow) Implementing Agentic RAG Option 1: LLMs with Function Calling Example: Function Calling with Ollama python Copy def ollama_generation_with_tools(query, tools_schema): # LLM decides tool use → executes → refines response … Option 2: Agent Frameworks Why Enterprises Are Adopting Agentic RAG Real-World Use Cases 🔹 Replit’s AI Dev Agent – Helps debug & write code.🔹 Microsoft Copilots – Assist users in real-time tasks.🔹 Customer Support Bots – Multi-step query resolution. Benefits ✔ Higher accuracy (validated retrievals).✔ Dynamic tool integration (APIs, web, databases).✔ Autonomous task handling (reducing manual work). Limitations ⚠ Added latency (LLM reasoning steps).⚠ Unpredictability (agents may fail without safeguards).⚠ Complex debugging (multi-agent coordination). Conclusion: The Future of Agentic RAG Agentic RAG represents a leap beyond traditional RAG, enabling:🚀 Smarter, self-correcting retrieval.🤖 Seamless multi-tool workflows.🔍 Enterprise-grade reliability. As frameworks mature, expect AI agents to become the backbone of next-gen LLM applications—transforming industries from customer service to software development. Ready to build your own Agentic RAG system? Explore frameworks like LangChain, CrewAI, or OpenAI’s function calling to get started. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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October 31, 2024in Data, Enterprise
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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 15, 2024in Artificial Intelligence, Data, Technology

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