Engineering the next generation of AI agents
Deep-dive guides, precise glossary, curated tools, and the latest research — for engineers and researchers building autonomous AI systems.
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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.
The Agent Framework Landscape in 2025: A State of the Field
From LangGraph to AutoGen to Pydantic AI — the tooling ecosystem for building AI agents has exploded. Here is how the major frameworks compare.
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.
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View allPrompt 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.
Building a Research Agent That Doesn't Hallucinate: An Architectural Approach
A team implemented robust architectural patterns, multi-tiered memory, and advanced RAG strategies to build a research agent capable of generating factually grounded responses, drastically reducing hallucinations.
Tool Profile: Pydantic AI
A deep-dive into PydanticAI — the type-safe, Python-native agent framework from the Pydantic team. Covers its validation-first philosophy, async agent API, dependency injection system, and a direct comparison with LangGraph for a structured research task.
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.
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