Reducing Token Waste and Hallucinations in Claude Agents with Fallow Settings
makedream1 de mayo de 2026
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Computing/Software
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If you've used AI agents in large monorepos, you realize it quickly. They indiscriminately read tens of thousands of files, draining your wallet while often producing context-blind garbage code. Before blaming the agent's intelligence, we need to look at what we are feeding it. Here is a concrete guide on how to use Fallow—a Rust-based code analysis tool—to make agents selectively read only the "truly critical code."
Throwing an entire codebase at an agent is irresponsible. When there is too much information, models suffer from the "Lost in the Middle" phenomenon, where they forget content in the center of the context. You must physically limit the scope the agent explores using Fallow's indexing features.
.fallow.json in the project root. Jam **/dist/**, **/tests/**, and legacy packages into the exclude array. Reducing noise is the priority.rules section, set the high-complexity threshold to around 15. This serves as a mechanism to force the agent to prioritize scanning modules with high cognitive complexity.strictBoundaries option. This prevents the disaster of the agent ignoring package boundaries and tangling dependencies.This setup alone drastically reduces the number of files the agent reads. In practice, blocking unnecessary file reads can save over 40% in API costs.
Don't let the agent find problems by reading code line-by-line. That's a waste of money. It is much faster and more accurate to feed it a summary of structural data pre-calculated by Fallow.
Run fallow audit --format json > audit_report.json in the terminal to extract architecture violations and complexity reports. Insert this JSON data directly into Claude's context window or reference it in a CLAUDE.md file. Simply add to the system prompt: "Before making edits, check the verdict and complexity scores in the report, and start working from modules with the lowest scores."
Developers don't need to explain everything in detail. The agent will begin surgery on the most decayed code first, following the priorities organized by the data.
Static analysis alone cannot tell if code is actually running. "Zombie code"—where references exist but nothing actually calls it—is the primary culprit in monorepos. As Meta's SCARF framework proved, safe deletion is only possible when you combine static analysis with dynamic coverage.
Collect V8 coverage data (NODE_V8_COVERAGE) and then run Fallow's runtime-sync feature. This will produce a list of "functions that have static references but haven't been executed once in the past month." Give this list to the agent and have it request approval for deletion. You'll hear the justification from the agent's mouth: "This function has no call records for 6 months, so it is safe to delete."
Just because the code written by an agent runs immediately doesn't mean you should merge it recklessly. You must check if it harms readability in the long run. Fallow calculates a Maintainability Index (MI) by combining Halstead complexity and the number of logical paths.
Add a fallow audit --base main --format json step to your GitHub Actions. Simply make the build fail if the health_score drops below 70.
This single gate saves senior developers over 2 hours of review time every week. Instead of cleaning up after the agent, you only need to review the design of high-quality code already verified by the machine. In collaboration with agents, the difference in productivity comes down to how coldly you employ these deterministic tools.