5 Building Reliable AI Agents

 

This chapter covers

  • The limitations of standalone LLMs and how AI agents address these challenges
  • The core components of reliable agents, including memory systems, tool integration, and decision-making frameworks
  • Building and deploying agents with a focus on reliability, performance, and real-world applications

Imagine asking a cutting-edge AI to book a flight or manage your schedule. Despite its impressive ability to understand language, it can't perform real-world actions—leaving you to handle the details. This is where AI agents come in, transforming powerful but passive LLMs into active problem solvers.

Imagine asking an LLM to help book a flight. You say:

Find me a morning flight from New York to San Francisco for next Tuesday.

An LLM might respond with a general suggestion:

LLM

You could try Delta or JetBlue.

This is hardly helpful. The LLM understands the question but can’t connect to flight APIs, check live schedules, or book the ticket. It simply lacks the ability to interact with the real world.

Figure 5.1 Core differences between a Standalone LLM and AI Agent

An AI agent (figure 5.1) bridges the gap between LLMs and real-world systems. While an LLM provides reasoning and language, the agent adds the ability to:

5.1 Why Do We Need Agents?

5.1.1 Static Knowledge

5.1.2 Inability to Act

5.1.3 No Workflow Management

5.1.4 How AI Agents Solve These Challenges

5.1.5 Broader Applications of AI Agents

5.2 Core Components of Reliable Agents

5.2.1 Memory: Context that Sticks

5.2.2 Balancing Memory Systems with LangChain

5.2.3 Tool Integration: Connecting Agents to the Real World

5.2.4 Strategies to reduce hallucinations with tools

5.2.5 Decision Making: Making AI Agents Intelligent

5.3 Agentic RAG for Reliable Agents

5.3.1 Project: Building an Agentic RAG System with LangChain

5.3.2 Setting Up Dependencies

5.3.3 Building the Knowledge Base

5.3.4 Creating Interactive Tools

5.3.5 Configuring the Agent's Behavior

5.3.6 Implementing Transparent Decision Making

5.3.7 Managing Conversation Context

5.3.8 Making it Working in Production

5.4 Advanced Techniques for Preventing Hallucinations and Improving Accuracy in Agents

5.4.1 Cross-Referencing

5.4.2 Guardrails for security and preventing hallucinations

5.4.3 Reinforcement Learning from AI Feedback (RLAIF)

5.4.4 Transparency in Multi-Step Reasoning

5.5 Summary

5.6 References