LLM Agents Interview Questions, issue 19, Mar 15, 2026
The Monolithic Agent Trap
Senior AI Engineer interview at Google DeepMind, and the interviewer asks:
“Your monolithic coding agent is handling both repo-wide search and patch generation, but as the context window fills up, the patch quality tanks. How do you architect the agent loop to fix this degradation?”
Don’t say: “Just upgrade to a model with a 1M+ token context window, use RAG to aggressively filter the search results, or prompt the agent to self-summarize.”
When one ReACT loop handles both discovery and code generation, the reasoning trace becomes polluted and the model loses the signal needed to produce correct patches.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.