AI agent quality gate
An automated checkpoint that blocks bad agent output before it reaches production — graded against your policy, not a vibe.
Why agents need a gate
AI agents produce work faster than any team can review it. Without a checkpoint, unreviewed output ships — and because most models approve their own work, “the agent said it’s fine” is no safeguard. A quality gate puts a policy-bound decision between the agent and production, at machine speed and machine cost.
The four-band decision
The gate grades each output with OtterScore — a hostile-by-default critic — and returns one of four decisions your pipeline branches on:
ship— meets the policy; accept it.route_to_fix— close; send it back with located flaws and concrete upgrades.quarantine— hold for a named human to approve.block— fails the policy; must not reach production.
Add it to your pipeline
Call the gate after the agent produces work and before you ship it. If it comes back route_to_fix or block, loop the flaws back to the agent and re-grade until it passes. Canonical contract: /llms.txt.
produce -> grade -> branch on band: ship -> accept + record audit evidence route_to_fix -> feed flaws back to the agent, regenerate, re-grade quarantine -> hold for named human approval block -> reject; do not ship
Bring your own rubric/acceptance policy so the gate enforces your bar. Every verdict is recorded as signed evidence, so “why did this ship?” always has an answer. MCP server (no install): https://mcp.seaotter.ai/mcp.
Gate, don’t just measure
A score you read after the fact is a dashboard; a gate is enforcement. The point is that block actually stops the work — inline, before it reaches production — and the decision is bound to your policy, not to a friendly model’s mood.
Frequently asked questions
What is an AI agent quality gate?
A quality gate is an automated checkpoint between an AI agent and production. Before agent output is shipped, the gate grades it against an explicit acceptance policy and returns a decision: ship, route to fix, quarantine, or block. It replaces 'the model said it's fine' with a policy-bound, auditable verdict.
How is a quality gate different from a test suite?
Tests check deterministic, pre-written assertions. A quality gate evaluates open-ended agent output — code, text, documents, decisions — against acceptance criteria a hostile critic applies, including the trajectory the agent took. You use both: tests for what you can assert, a gate for everything you can't.
What are the four bands?
ship (meets the policy, accept it), route_to_fix (close — send it back with located flaws and concrete upgrades), quarantine (hold for named human review), and block (fails the policy, must not reach production). The score maps to a band, and your pipeline branches on it.
How do I add a gate to my agent pipeline?
Call the eval API (or hosted MCP server) after your agent produces work and before you ship it. If the band is route_to_fix or block, feed the returned flaws back to the agent, regenerate, and re-grade until it clears the bar. Every verdict is recorded as signed audit evidence.
Related: AI agent evaluation · LLM-as-a-judge · evaluate AI-generated code · live demo.