What is an AI Quality System?
An AI Quality System is the set of checks, people and evidence a team uses to decide whether a new version of an AI agent is safe to ship. For agents that write data, it runs the agent's key tasks before each release, checks the records in connected systems, and keeps the checks current as the agent changes.
What it includes
A working AI Quality System for an agent with write access has four parts:
- Release gates. The agent's key tasks, run before each release, with a pass/fail check on the result in the connected system. See what is an AI agent release gate.
- The usual errors. Checks for wrong data, a false "done", duplicate writes, partial writes and regressions since the last release.
- Evidence. A trace, a recording and steps to reproduce for each failure, so a person can decide whether the version ships.
- Upkeep. Prompts, models and tools change. The checks have to change with them, or they slowly stop covering what the agent does.
How it differs from evals and observability
Evals score outputs on a dataset. Observability records what happened in production. An AI Quality System uses both as inputs, but it gives the release-gate verdict and evidence; your team makes the release decision: is this version safe to ship, based on what it actually writes? More in AI agent release gates vs observability and evals.
In Agentiqa
Agentiqa describes itself as an AI Quality System for production AI agents. It has two parts: release gates, which run the agent's key tasks before each release, and AI Quality Manager, which compares each release with the previous one and proposes test updates and new tests that a person on your team approves.
On the blog
- What is an AI agent release gate?
- What is a quality gate for AI agents?
- AI agent production readiness checklist
Related terms
AI agent regression testing · AI agent evals vs testing · Hallucinated success · All terms
FAQ
No. Eval tools score outputs on a dataset. An AI Quality System runs the agent's key tasks before a release and checks what it wrote in connected systems, so your team can decide whether the version is safe to ship.
A person, usually the engineering lead who owns the agent, together with the process owner for high-risk tasks. The checks give that decision evidence; they don't replace it.
Less so. For agents that only answer or draft text, evals and observability cover most of the risk. The case is strongest for agents that write into business systems.
Try it on your own agent
Describe a task in plain words. Agentiqa does it step by step and tells you if it worked. Every failure comes with step-by-step reproduction.
20 runs a month, no credit card.
Your agent writes into ERP, CRM or billing for customers? See Enterprise.