FlyRoute: Self-Evolving Agent Profiling via Data Flywheel for Adaptive Task Routing

2026-05-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

FlyRoute is a self-evolving agent profiling framework designed to address the issue of static agent capability descriptions in enterprise multi-agent systems. It continuously updates agent profiles by leveraging a data flywheel mechanism. This process involves dispatching queries, quality-gating successful interactions into an agent's success store, periodically distilling this evidence into refined capability descriptions, and feeding these into an LLM router alongside BM25-retrieved examples. To ensure data efficiency, FlyRoute employs an uncertainty-driven exploration policy that prioritizes under-profiled agents for plausible queries, avoiding redundant evidence collection. Evaluated on a proprietary enterprise developer-support dataset, FlyRoute boosted a zero-shot LLM router's accuracy from 72.57% to 78.04% with just five seed queries per agent. After processing 7,211 training queries, accuracy further increased to 89.83%, marking a 17.26 percentage point gain over the zero-shot baseline and 11.79 percentage points over cold start, with improvements across four expert domains.

Key takeaway

For AI Architects designing multi-agent systems, you should prioritize dynamic agent profiling over static descriptions. Implementing a self-evolving framework like FlyRoute, which learns from live interactions and uses uncertainty-driven exploration, will significantly enhance routing accuracy and adaptability. This approach ensures your system's router remains current with evolving agent behaviors, reducing manual profile maintenance and improving overall system performance, as demonstrated by a 17.26pp accuracy gain.

Key insights

Continuously updating agent profiles from live interactions significantly improves task routing accuracy in multi-agent systems.

Principles

• Agent capabilities evolve; static profiles fail.
• User interactions are best for profiling.
• Uncertainty-driven exploration boosts efficiency.

Method

FlyRoute employs a data flywheel: dispatch, quality-gate successes, distill evidence into learned descriptions, and inject into an LLM router, guided by uncertainty-driven exploration.

In practice

• Use LLM-as-Judge for quality gating.
• Initialize agents with 5 seed queries.
• Periodically distill profiles (e.g., every 20 accepts).

Topics

• Multi-Agent Systems
• Task Routing
• Agent Profiling
• Data Flywheel
• LLM Routers
• Uncertainty Exploration

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.