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A developer shares hard-won lessons from building AI agents: focusing on tool design over model choice, using small loops instead of giant prompts, logging agent context, adding guardrails early, and creating small evals to catch bugs.
The author argues that giving an AI agent fewer, coarser tools improves reliability and cost by reducing decision surface and preventing the model from confusing factual and simulated outputs.
The author built a Healthy Food MCP server and learned that agents perform better with many narrow, constrained tools rather than one flexible tool, emphasizing the need for a boring tool surface to reduce LLM hallucination.
A technical Twitter thread sharing principles for building high-performance vertical AI agents, using the Shortcut spreadsheet agent as an example. Key insight: treat context as a layered cache (L1/L2/L3) to minimize cost per task across the distribution.
awesome-harness-engineering is a curated list of resources on AI agent harness engineering (context management, tool design, verification loops, memory systems, etc.) from companies like OpenAI, Anthropic, Microsoft, and Meta, aimed at helping developers build reliable agent frameworks.
A comprehensive guide on building reliable AI agents, explaining core components of perception, decision logic, and action interface, with insights from a former Meta engineer.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.