MCP Dev Summit Bengaluru 2026

Scenario: Deploy an agent to production. Works 90% of time in testing. Month later: Claude model updates. Success rate drops to 70%. Why? Model change altered how tools are ranked.
You can't see this. You have no control. Your agent silently degraded.
This talk identifies sources of divergence:

Temperature/sampling: Agent with temp 0.7 calls Salesforce 60% of time. Temp 0 calls it 95%.
Model version: Claude 3.5 favors Salesforce (in training data). Opus 4.5 favors email (newer training). Same task, different choices.
Context truncation: Tool listed first in window = primacy bias (70% called). Tool listed last = recency bias (30%).
Tool schema order: Tools listed alphabetically vs. semantic order (query before create) changes success rate 25%.
Schema verbosity: Detailed descriptions make tools more likely to be selected than sparse ones.

Then proposes solution: Agent fingerprinting. Create deterministic test suite capturing baseline behavior. Before deploying new model/agent version: run fingerprint suite. If success rate drops 10%+, alert. Don't deploy.