Confidence Gating: Stopping Runaway AI Agents Before They Reach Production
About this session
When AI agents move from sandbox chat windows into production systems, they gain the ability to execute real-world mutations: calling APIs, writing to databases, and spending enterprise budgets. The engineering risk isn't just that non-deterministic models hallucinate; it’s that teams treat agents like trusted internal systems instead of untrusted clients.
This session provides a production-ready playbook for applying proven backend patterns to gate agentic tool calls. We will break down a tactical, three-lane decision model, Proceed, Abstain, and Escalate, to evaluate an agent's intent based on confidence scores and blast radius. Attendees will leave with a concrete architectural framework and working code to implement structural schema validation, token-bucket rate limiting, and automated fallback gates that keep systems predictable under any model behavior.
Speaker
Key takeaways
- Learn treating agents as untrusted clients
- Learn preventing runaway agent loops