Context Engineering for Code Agents: Why They Fail and How to Fix Them
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Context Engineering for Code Agents: Why They Fail and How to Fix Them
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Code agents don't fail because they can't code — they fail because their context turns into a junk drawer.
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Code agents don't fail because they can't code — they fail because their context turns into a junk drawer.
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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.
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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.
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What's covered:
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What's covered:
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Why long context windows alone don't save you (position bias, distractor sensitivity)
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Why long context windows alone don't save you (position bias, distractor sensitivity)
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Observation masking vs. LLM summarization — and when simple beats clever
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Observation masking vs. LLM summarization — and when simple beats clever
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Tool-output compression with approaches like LLMLingua-2
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Tool-output compression with approaches like LLMLingua-2
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Trajectory reduction: pruning dead branches from agent history
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Trajectory reduction: pruning dead branches from agent history
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Memory hierarchies: session → working set → notes → cross-session
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Memory hierarchies: session → working set → notes → cross-session
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How MCP and standardized tool interfaces reduce context debt
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How MCP and standardized tool interfaces reduce context debt
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Dynamic context policies trained with RL (DeepMiner, MEM1)
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Dynamic context policies trained with RL (DeepMiner, MEM1)
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Meta-agent CI loops for measuring regressions across agent configs
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Meta-agent CI loops for measuring regressions across agent configs
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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.
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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.
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👉 Read the full post: 
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👉 Read the full post: 
https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
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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
🔥
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Ujjwal Tyagi
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