What codebase practices actually make your agents better?
Summary
Explores which codebase practices can effectively improve the performance and reliability of AI agents.
Similar Articles
Coding with Agents
Coding with Agents explores how AI agents can assist developers in writing code, automating tasks, and improving productivity.
People running coding agents across real repos: what breaks after the agent writes the code?
This article discusses the practical challenges engineering teams face when adopting AI coding agents, such as task safety, context retrieval, output review, and coordination, and proposes a readiness model for evaluation.
AI Agents Testing before deploying to production
Discusses best practices for testing AI agents before deploying them to production environments.
Code execution with MCP: Building more efficient agents
This article from Anthropic explores how integrating code execution with the Model Context Protocol (MCP) can improve the efficiency of AI agents. It addresses challenges like token overload from tool definitions and intermediate results, proposing code execution as a solution to reduce latency and costs.
A developer shares insights on how to maximize AI agent capabilities, arguing that simpler setups and understanding core principles are more effective than complex harnesses and libraries.
A developer shares insights on how to maximize AI agent capabilities, arguing that simpler setups and understanding core principles are more effective than complex harnesses and libraries.