arXiv 2606.14066 introduces FastContext, a sub-agent architecture for Coding Agents that explicitly reserves 60% of the inference token budget for "code search" — looking up relevant code snippets in the repository. The result: 5.5% overall speedup on SWE-Bench, with no quality loss.
The "code search" insight: Coding Agents spend a large fraction of their inference tokens on "looking up code" — searching the repository, reading files, understanding the code structure. This "code search" overhead is often 40-60% of the total inference tokens, but it's necessary for the Agent to understand the codebase.
The FastContext sub-agent: a specialized 4B-30B model that handles the "code search" task. The sub-agent is trained to be highly efficient at code search — it uses a compressed code representation, and it returns only the relevant snippets (not the full files). The main Agent delegates all "code search" requests to the sub-agent, reserving 60% of the inference budget for the sub-agent's outputs.
The result: on SWE-Bench, FastContext-augmented Agents score 5.5% higher than the baseline (using the same total token budget). The biggest improvement is on large repositories (10K+ files), where the code search overhead is highest.
The bigger takeaway: "sub-agent specialization" is the right architecture for Coding Agents. The "one Agent does everything" approach is wasteful, and the "specialist sub-agent" approach is significantly more efficient. For the industry, this means Coding Agent vendors should invest in "code search" and "code understanding" sub-agents, not just bigger general models.