Post
3432
Context Engineering for Code Agents: Why They Fail and How to Fix Them
Code agents don't fail because they can't code — they fail because their context turns into a junk drawer.
I wrote a practical survey covering the emerging discipline of context engineering for agentic hybrid applications: the techniques, papers, and architectural patterns that keep long-running code agents on track as their token windows fill up with tool logs, stale diffs, and repeated file dumps.
What's covered:
Why long context windows alone don't save you (position bias, distractor sensitivity)
Observation masking vs. LLM summarization — and when simple beats clever
Tool-output compression with approaches like LLMLingua-2
Trajectory reduction: pruning dead branches from agent history
Memory hierarchies: session → working set → notes → cross-session
How MCP and standardized tool interfaces reduce context debt
Dynamic context policies trained with RL (DeepMiner, MEM1)
Meta-agent CI loops for measuring regressions across agent configs
The core argument: the engineering challenge isn't "make the model smarter" — it's make the agent's context and verification smarter. That's where the real leverage is in 2026.
👉 Read the full post: https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
Code agents don't fail because they can't code — they fail because their context turns into a junk drawer.
I wrote a practical survey covering the emerging discipline of context engineering for agentic hybrid applications: the techniques, papers, and architectural patterns that keep long-running code agents on track as their token windows fill up with tool logs, stale diffs, and repeated file dumps.
What's covered:
Why long context windows alone don't save you (position bias, distractor sensitivity)
Observation masking vs. LLM summarization — and when simple beats clever
Tool-output compression with approaches like LLMLingua-2
Trajectory reduction: pruning dead branches from agent history
Memory hierarchies: session → working set → notes → cross-session
How MCP and standardized tool interfaces reduce context debt
Dynamic context policies trained with RL (DeepMiner, MEM1)
Meta-agent CI loops for measuring regressions across agent configs
The core argument: the engineering challenge isn't "make the model smarter" — it's make the agent's context and verification smarter. That's where the real leverage is in 2026.
👉 Read the full post: https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
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