When you ask an LLM to “help with financial planning,” you might get a generic response that misses the nuance your situation requires. But what if you could transform that same LLM into a specialized Certified Financial Planner with 10 years of experience in debt management and retirement planning? That’s the power of role-based prompting, and it’s particularly valuable in financial services where precision and expertise matter.
Building Intelligent AI Systems - A Complete Guide to Agentic AI
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.
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.