Long-running AI agents all hit the same wall eventually, context rot, and LangChain just laid out five concrete techniques to deal with it.
Title: Context Engineering with Deep Agents: How To Remember Without Keeping Everything In Context
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🧩 The problem
As agents accumulate instructions, tool results, and files, they eventually exceed the model's context window, degrading reasoning quality, much like how humans lose track of earlier details under information overload.
💡 Five techniques
Offloading (moving large results to the filesystem), summarization (compressing older messages while writing them to file), skills (progressively loading full instructions only when relevant), subagents (isolating noisy work in separate context windows), and long-term memory (persisting learnings across sessions as markdown files) work together to manage context without discarding information.
🔧 Technical highlights
All five are implemented as middleware hooked into the agent loop, automatically offloading results over roughly 20,000 tokens and user messages over 50,000 tokens to the filesystem. Because memory lives as plain files in storage you control, learned behaviors survive even when you switch model providers.
📊 Use case
A market-research agent analyzing five competitors shows this in action: raw data exports, search results, and draft iterations pile up during the task, and context engineering keeps those intermediate artifacts from degrading reasoning quality in the final report.
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