Trust and Reputation Routing
Maintain a per-agent reputation score updated from outcome quality and peer feedback, and route new tasks preferentially to high-reputation agents.
Problem
Static routing wastes the platform's most valuable signal: track record. Agents that have historically produced good outcomes get the same allocation as agents that have repeatedly failed. New tasks are routed to the wrong agents because routing ignores past evidence. Without a reputation layer, the platform cannot learn from outcomes; bad agents stay in rotation and good agents are under-used.
Solution
For each agent maintain a reputation score updated after each task from outcome signals (deterministic success, user rating, peer review by another agent). Route new tasks by sampling weighted by reputation, with a small exploration term for newcomers (cold-start). Decay reputation over time so stale records don't dominate. Surface reputation scores in operator dashboards. Distinct from a router LLM (which picks once per request based on intent): reputation routing is statistical and longitudinal.
When to use
- Multiple candidate agents per task with varying historical quality.
- Outcome signal is observable (deterministic, user rating, peer review).
- Cold-start exploration is tunable and acceptable.
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