AI Engineering12 min read
Custom tools for coding agents: beyond the built-ins
How to design custom tools for coding agents that are not read, edit, or bash. The naming rules, the schema patterns, and the 3 custom tools that pay off.
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Tool Calling
Explore our latest articles and insights about Tool Calling.
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10 posts in total
AI Engineering10 min read
ripgrep for coding agents: fast code search at scale
Why ripgrep is the right code search tool for coding agents. The speed, the flags, and the wrapper that turns rg into a safe agent tool.
AI Engineering11 min read
Edit tool design: how coding agents modify files safely
How to design an edit tool a coding agent can use without wrecking files. The string-match rule, the dry-run pattern, and the 50-line safe edit function.
AI Engineering12 min read
Designing modular tool integrations for coding agents
How to structure tool integrations so an agent stays clean as it grows. The registry pattern, schema-first design, and the 80-line dispatcher that scales.
AI Engineering11 min read
bash tools for coding agents: safe shell execution
How coding agents run shell commands safely. The bash tool design, the denylist trap, and the sandbox that keeps rm -rf away from your home directory.
LLM Engineering12 min read
Agentic RAG with LangGraph: planning, rewriting, tool use
How agentic RAG uses LangGraph to plan, rewrite queries, and call tools. The graph, the state, and the nodes that turn single-pass RAG into reasoning.
AI Engineering12 min read
Build a coding agent with Claude: a step-by-step guide
How to build a coding agent with Claude in 100 lines of Python. Tool use, file editing, bash, and the loop that ties them together. No frameworks.
AI Engineering12 min read
The event loop inside a coding agent: how it thinks
How a coding agent loop actually works. The minimal while loop that turns a chatbot into Claude Code, and the 4 states every agent cycles through.
AI Engineering8 min read
Giving your LLM hands: a deep dive on tool calling
How giving works in production. Learn how to build agents that can interact with the real world using tool calling. Step-by-step guide to implementing func
AI Engineering8 min read
Structured output: making LLMs application-ready
Learn how to get reliable, structured outputs from LLMs using JSON mode and Pydantic tool calling. Transform LLMs from text generators into application-...