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A practitioner weighs three designs for adding persistent knowledge-base spreadsheets to a chat agent's existing tools, leaning toward a dedicated analyze_knowledge_spreadsheet tool with clear routing descriptions and a routing test, while asking the community for missed approaches.
An MCP server developer shares lessons learned from agent failures, arguing that many apparent model mistakes were actually caused by poorly designed tool outputs, ambiguous options, and misplaced instructions. Each fix landed in the tool rather than the prompt.
The blog post explores the rationale and benefits of designing specialized tools for LLMs instead of allowing them to use general-purpose human tools, emphasizing how LLMs' unique requirements and capability restrictions could enhance their power.
Pi's new architecture is highly similar to that of the Maka tool, indicating that the agent harness layer is borrowing principles from database write-ahead logs and event sourcing, forming an engineering consensus.
The article argues that many AI agent tools are just 1:1 API wrappers, pushing branching logic into the LLM and causing failures. The author recommends task-shaped tools like upsert_contact that encapsulate search/create/update logic in code, pass known context, validate inputs, and return structured errors.
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.