Over the past few months, I’ve been exploring the world of agentic AI - systems where language models don’t just generate text, but reason, plan, and take action. This post serves as both an introduction and a roadmap to the complete series, sharing my thoughts on the key concepts, practical patterns, and how to get started building your own intelligent agents.
Multi-Agent RAG and Building Complete Systems
Standard RAG retrieves from a single source, but real problems often require information from multiple specialized domains. Multi-Agent RAG coordinates multiple retrieval specialists, each expert in querying specific data sources, then synthesizes their findings into coherent answers. In this final post of the series, I’ll explore Multi-Agent RAG patterns and bring together everything we’ve learned into complete, production-ready systems.
Multi-Agent Routing, State, and Coordination
When multiple agents work together, three challenges emerge: how do requests reach the right agent (routing), how does information flow between agents (data flow), and how do agents maintain a consistent view of the world (state coordination). In this post, I’ll explore patterns for managing these critical aspects of multi-agent systems.
6 Prompt Engineering Techniques Used by Top AI Engineers
Top engineers at OpenAI, Anthropic, and Google don’t prompt like most people do. They use specific techniques that turn mediocre outputs into production-grade results.
Here are 6 techniques that actually work, with templates you can steal and adapt for your own use.
Designing Multi-Agent Architecture - From Solo to Ensemble
A single agent can accomplish a lot, but complex real-world tasks often exceed what any one specialist can handle. Just as organizations divide work among departments, multi-agent systems distribute responsibilities across specialized agents that collaborate toward shared goals. In this post, I’ll explore how to design architectures where multiple AI agents work together effectively.
Agentic RAG and Agent Evaluation Strategies
Traditional RAG (Retrieval-Augmented Generation) follows a fixed pattern: query in, documents out, response generated. But what if the agent could decide when and how to retrieve? Agentic RAG gives agents control over their own knowledge acquisition. In this post, I’ll explore this dynamic approach to retrieval, then tackle the equally important question: how do we know if our agents actually work?
Connecting Agents to the World - External APIs and Data
An agent that can only process text is fundamentally limited. Real usefulness comes from connecting to external systems - fetching live data, querying databases, calling APIs, and triggering actions in the real world. In this post, I’ll explore how to build these connections, turning isolated language models into integrated systems that can actually get things done.
Agent State and Memory - Beyond Single Interactions
A stateless agent treats every interaction as its first - no memory of previous conversations, no awareness of ongoing tasks, no accumulated context. While this works for simple Q&A, real-world applications demand more. In this post, I’ll explore how to give agents memory through state management, enabling them to maintain context across interactions and handle complex multi-step workflows.
Extending Agents with Tools and Structured Outputs
Language models are impressive reasoners, but without tools they can only generate text. They can’t check real-time data, perform precise calculations, or interact with external systems. In this post, I’ll explore how to extend agents with tools through function calling, and ensure reliable outputs using Pydantic for structured data validation.
Evaluator-Optimizer and Orchestrator-Worker Patterns
Some tasks require iteration - generating, evaluating, and refining until quality standards are met. Others need dynamic orchestration - a central coordinator breaking down novel problems and delegating to specialists. In this post, I’ll cover two sophisticated patterns that enable these capabilities: the evaluator-optimizer loop and the orchestrator-worker architecture.