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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Inside the Agentic AI Foundation (AAIF): Linux Foundation Projects Shaping the Agent Stack
AAIF is becoming the vendor-neutral standards layer for production AI agents. This roundup maps the protocols, projects, and working groups that matter for teams shipping agents at scale.
Agent Evaluation: How to Test What You Can't Fully Predict
Evaluation is the discipline that separates an agent that demos well from one that survives production. This piece builds the vocabulary — correctness, faithfulness, tool-call accuracy, trajectory evaluation — and the workflow behind it.
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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View allInside the Agentic AI Foundation (AAIF): Linux Foundation Projects Shaping the Agent Stack
AAIF is becoming the vendor-neutral standards layer for production AI agents. This roundup maps the protocols, projects, and working groups that matter for teams shipping agents at scale.
Agent Evaluation: How to Test What You Can't Fully Predict
Evaluation is the discipline that separates an agent that demos well from one that survives production. This piece builds the vocabulary — correctness, faithfulness, tool-call accuracy, trajectory evaluation — and the workflow behind it.
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.
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.
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