After a model update, the agent cuts an order ID short.
order record
expectedORD-EXAMPLE-004817
savedORD-EXAMPLE-004817
ID mismatchIndependent arbiter for your AI agents integrated into mission-critical systems
Autonomous Release Gates for Production AI Agents
For AI agents with write access to customer ERP, CRM and billing. Your tests pass. Your customer's data breaks.
Why
An agent can save wrong data and still say it worked
Illustrative example
A server error makes the agent retry and create the invoice twice.
invoice writes
POST /invoices502 · retry
INV-EXAMPLE-01071,240.00
INV-EXAMPLE-01071,240.00
Saved twiceThe agent replies "done", but nothing was saved.
agent reply vs. record
Done.
records saved0
Nothing savedMost tests only check what the agent replies. We also check what it saved.
What we do
What Agentiqa does
Our engineers set up checks on what your AI agent changes in ERP, CRM, billing and other business systems.
Before every release, the checks run the agent through its key tasks, including server errors, timeouts and cut-off IDs. They confirm the right record was saved once, with the right IDs and amounts. They also try to misuse the agent, for example with prompt injection. If a check fails, the release stops.
Agent
Task
Record
Gate
- Right record
- IDs and amounts
- Saved once
Failed check → release stopsRelease stopped
How it works
First gates live within 7 minutes
- Map
the systems your agent changes
- Check
the riskiest tasks first
- Run
the checks before every release
- Every failure comes with a trace and steps to reproduce.
In production at Lio
“Running them daily, we proactively catch regressions across all our systems and agents. In the first week alone, Agentiqa surfaced five real production bugs we'd never have caught without a full-time QA team.”
Vladyslav MelnychukAI Software Engineer, LioLio · AI agents for enterprise procurement · Series A led by a16z ↗
AI agents that change data in ERP, CRM and billing can save wrong data while every test passes: a cut-off ID, an invoice created twice, a “done” with nothing saved. Agentiqa checks what was saved before every release and stops the release if it’s wrong. We also check whether the agent can be misused, for example through prompt injection.
A quality gate for AI agents, which Agentiqa calls a release gate, is a check that runs your agent’s key tasks before a release. If it fails, the release stops and you get a trace and step-by-step reproduction.
Observability tools show what your agent did after release. Our checks run before the release, so a wrong change is caught before it reaches your customer.
AI agents in production that change data in ERP, CRM, billing and other business systems, where a wrong record costs more than a wrong answer.
First gates are live within 7 minutes. We map the systems your agent changes, check the riskiest tasks first and run the checks before every release.
Our engineers build and run them. When one fails, you get a trace and step-by-step reproduction.
The audit is free. Pricing for the managed checks is on request. Developers can start free with Community Edition (20 runs a month) or use Pro at $39 a month.
Mutation Risk Audit
Find out where your agent could save wrong customer data.
Free, 20 minutes.