Articles
In-depth guides and tutorials on building AI agent systems.
7 items
Prompt Engineering for Agent Roles: System Prompts That Scale
The craft layer of agent engineering — how to structure system prompts with Role, Goal, Format, and Constraints so your agent is reliable in production, not just in demos.
The ReAct Loop Unpacked: Reasoning + Acting in Practice
A rigorous treatment of the Thought → Action → Observation cycle — how it works at the execution level, where it breaks in production, and which alternatives exist and why.
RAG in Production: Chunking, Hybrid Search, and Agentic Retrieval
Chunking strategies, hybrid search, agentic retrieval loops, GraphRAG, and an honest answer to whether long-context models have made RAG obsolete — everything the memory article deferred.
Memory & State Management in LLM Agents
LLM agents are only as capable as their memory architecture. This guide breaks down the four memory tiers — in-context, external retrieval, episodic, and procedural — with implementation patterns and trade-off analysis for production systems.
Tool Use in LLM Agents: Patterns, Pitfalls, and Best Practices
Tool use transforms LLMs from text generators into action-capable agents. This guide covers function calling, tool design principles, error handling, and security considerations.
What Is Agent Engineering? A Comprehensive Introduction
Agent engineering is the discipline of designing, building, and operating AI systems that autonomously pursue goals. This guide covers the core concepts, architecture patterns, and why it matters now.
Multi-Agent Orchestration: Patterns and Trade-offs
Explore the primary patterns for coordinating multiple AI agents — sequential, parallel, hierarchical, and event-driven — with concrete trade-off analysis for each.