Brief context
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Original paper
The executive brief below is grounded in the source paper and linked back to the arXiv abstract.
Enterprise AI agents route user queries to specialized skills by matching queries against natural language skill descriptions. When two skills share overlapping descriptions, the routing LLM misroutes queries, a failure we term skill collision. As agents scale to dozens of skills, manually tuning descriptions to maintain routing accuracy becomes a significant engineering bottleneck. We deploy an automated description optimization pipeline on a production enterprise group chat agent (9 skills, 372 regression cases). The pipeline produces descriptions averaging 79.2% F1, matching manually tuned descriptions at 79.4% F1 (average per-skill difference -0.20%, within the 0.78% multi-seed noise floor), while reducing per-skill engineering effort from 120 minutes to 3.8 minutes (32 times speedup). We then examine which pipeline components actually drive this match. Systematic ablation on both the production system and ToolBench (16k tools) reveals that a single LLM rewrite using any available false-positive and false-negative cases captures most of the available improvement. Other design choices we tested (iteration budget, feedback signal composition, dual editing of confused pairs, and training set size) each affect final F1 by less than 0.5%. Description optimization addresses skill collisions caused by overlapping descriptions but cannot resolve cases where two skills intended scopes genuinely overlap. We identify a diagnostic (a large train-validation F1 gap) that flags the latter cases for architectural rather than text-level intervention.
Executive brief
A short business-reader brief that explains why the paper matters now and what to watch or do next.
Why this is worth your attention
Agent rollouts often bog down in a mundane bottleneck: keeping skill and tool descriptions distinct enough that the system routes work to the right place. This paper shows that, in one production enterprise chat agent, a single LLM rewrite using examples of misrouted queries matched manual tuning while cutting per-skill effort from two hours to under four minutes. If the result holds across broader deployments, agent teams can turn routing maintenance into a lightweight test-and-rewrite workflow; the caveat is that rewriting descriptions fixes confusing wording, not genuinely overlapping product scope.
- If you run agent skills or tools, the practical move is not a complex optimizer first; it is a regression set of routed examples, a list of false positives and false negatives, and one rewrite pass. The paper’s production result says that may get you manual-level routing quality with a fraction of the engineering time.
- A credible agent platform should be able to show the misrouted examples used to improve skill descriptions and explain whether it is doing a simple rewrite or selling unnecessary iteration machinery. In this paper, most extra pipeline choices moved F1 by less than about half a point.
- The paper separates fixed-pool routing from retrieval-based routing, and the same description choices can behave differently in each. Before standardizing an onboarding process, teams should know whether the agent chooses among a small known skill set or retrieves from a large tool catalog.
- If training performance jumps but validation routing does not, the issue may be that two skills genuinely claim the same work. That is a product or architecture problem—merge, split, reorder, or add a clarifying decision step—not a wording problem.
- The strongest real-world evidence is one production group-chat agent with 9 skills and 372 synthetic test cases, backed by larger ToolBench experiments with their own label and description caveats. Treat this as a strong workflow finding, not a guarantee that every agent catalog can be fixed by rewriting descriptions.
Evidence ledger
The strongest claims in the brief, along with the confidence and citation depth behind them.
Automated LLM rewriting matched manually tuned routing descriptions in the production agent while sharply reducing per-skill tuning time.
A single rewrite using error examples captures most of the benefit; added optimization complexity showed little marginal value in the reported tests.
Fixed candidate routing and retrieval-based routing require different optimization assumptions.
Description rewriting cannot solve genuine scope overlap between skills; those cases need architectural or product intervention.
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