AI Infrastructure
Caching Agent Tool Calls (Not Just Prompts)
Prompt caching is solved. The bigger win for agents is caching tool-call results, and that is harder because tools have side effects. Here is how to do it safely.
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Deep explorations of agentic AI, RAG architectures, and production engineering.
64 articles
Prompt caching is solved. The bigger win for agents is caching tool-call results, and that is harder because tools have side effects. Here is how to do it safely.
How to build a RAG system that serves many customers without leaking data between them: silo vs pool vs bridge, namespaces vs metadata filters, and where isolation must hold.
How to stream LLM tokens, tool progress, and state updates from a LangGraph agent using the v2 stream API, with the custom writer pattern most people miss.
If you can already ship software, the way into AI engineering is not another tutorial. It is building five systems that force you to handle failure, measure quality, and run things in production.
Autonomy is great until the agent deletes the wrong file or emails the wrong list. Here is how DeepAgents pauses on sensitive tool calls and waits for a human, in TypeScript.
Skills give an agent deep, reusable capability without paying for it on every turn. Here is how DeepAgents loads them in layers, and how to write ones that get picked.
DeepAgents treats long-term memory as files the agent reads and writes, with backends deciding where they live. Here is how scoping, episodic memory, and consolidation actually work.
Subagents are the main lever for stopping a long task from drowning in its own context. Here is how DeepAgents implements them in TypeScript, and the mistakes that make them useless.
A tour of how DeepAgents assembles, compresses, and isolates context, with the TypeScript config for each layer. The framework does a lot automatically; this is what is happening and where you steer it.