Most coding agents don’t fail because they can’t write code. They fail because they start with the wrong map.

I’ve been building SigMap, an open-source grounding layer for AI coding agents, and one assumption I had was wrong. I thought bigger context windows would solve most AI coding problems. But after testing with Claude Code, Cursor, Codex-style workflows, and local agents, I kept seeing the same bottleneck: The agent spent too much time trying to figure out the repo before it could do useful work. It would: search the codebase open random files follow imports guess where logic lived sometimes answer from the wrong file entirely So I started thinking of the problem differently. Instead of giving the agent more context, SigMap gives it a deterministic map: real files real functions/classes real line anchors ranked files for the task coverage validation groundedness checks after the answer MCP tools for on-demand lookup The current v8.9 benchmark snapshot: 97.0% average token reduction across 21 repos 88% hit@5 retrieval vs 13.6% random baseline 2.84 → 1.44 prompts per task 67.8% task success proxy across 90 tasks 0/21 GPT-4o overflow repos with SigMap, compared with 16/21 without it The new thing I’m most interested in is not “more autonomy.” It is less wasted context. Current SigMap can also expose this through MCP, so an agent can pull what it needs instead of loading the whole repo upfront. There is also a squeeze_output tool for compressing noisy stack traces, CI logs, and JSON payloads before they enter context. The core idea: repo → deterministic signature map → ranked context → validation → grounded answer