Agentic QA: AI agent testing from senior AI pod teams

Ryz tests AI agents with dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. Agentic QA goes beyond checking a single answer: the pod builds scenario suites that drive your agent through full multi-step tasks, checks every tool call and action it takes, and blocks releases that break them. Every engineer comes from the top 1% of the tens of thousands we interview, and they work on US business hours.

What we build

An agent can give a perfect final answer after calling the wrong API, or take an irreversible action on a misread instruction. Testing has to cover the path, not just the output. Typical deliverables:

• Scenario suites. End-to-end tasks written from real user goals (refund an order, reschedule a delivery, file a ticket), each with the expected sequence of steps, allowed tools and the correct end state.
• Tool-call assertions. Checks that the agent called the right tool, with valid arguments, in a sensible order, and never called a tool it was not allowed to use for that user.
• Sandboxed environments. Mock or staging versions of your APIs, databases and third-party systems, seeded with test data, so agents can take real actions in tests without touching production.
• Simulated users. Scripted and model-driven user personas that push agents through clarifying questions, changed minds, ambiguous requests and hostile inputs.
• Safety and permission tests. Prompt-injection cases hidden in documents and tool outputs, attempts to exceed a user's permissions, and checks that approval steps fire before risky actions.
• Release gates. Scenario pass rates per task type run in CI on every prompt, model, tool or workflow change, with failing traces linked in the pull request.
• Production replay. Real agent sessions (redacted) replayed against new versions to catch behavior changes before rollout.
• Classic automation where it belongs. API and UI tests for the deterministic parts of the product around the agent, so the agent suite only covers what actually needs it.

How an engagement works

What happens after you contact us: We review the agents you run or plan to launch, the tools they can call, and where a mistake would cost money or trust. If the systems are sensitive, we'll sign your NDA first. Then you get the pod members' names with a scoped plan and price.

1. Talk. We map the agent's tasks, tools and permissions, and agree which actions need an approval or must never happen.
2. Match. We propose a pod scoped to your stack, usually a tech lead, a QA automation engineer with agent experience and a backend engineer for sandboxes, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos of pass rates and the failures found.
4. Grow. You extend coverage to new agents and tools, or the pod hands the suite to your QA and platform teams with runbooks.

Week 1 is mapping and a first sandbox: the agent's tools stubbed or pointed at staging, and the first scenarios written from real tasks. Month 1 typically brings a scenario suite covering the main tasks, tool-call assertions and a CI gate. Month 3 is about depth: simulated users, safety and permission suites, production replay and pass-rate dashboards per task. Actual pace depends on scope, environment access and your onboarding.

The stack our teams work in

How we catch the failures agents actually have

Agents rarely fail the way traditional software does. The patterns we test for:

• Right answer, wrong path. The final message looks correct but the agent skipped a lookup or called a tool twice. Step-level assertions catch what output-only checks miss.
• Loops and dead ends. Agents that retry forever or stall after a tool error. Scenarios include tool failures and timeouts, with limits on steps and spend.
• Unsafe actions. Deleting, paying, emailing or changing records without the approval your policy requires. Tests assert that approval gates fire and that blocked actions stay blocked.
• Injected instructions. Text inside a document, web page or tool result that tries to redirect the agent. Safety suites include injected content across every input channel.
• Silent regressions. A model or prompt update that improves one task and breaks another. Gates fail on per-task drops, not just the overall pass rate.
• Flaky results. The same scenario passing and failing on different runs. Critical scenarios run several times, and thresholds account for variance.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month, mostly mid-level to senior. Shapes we often see for agent testing:

• Agent QA pod: a tech lead plus 2 senior engineers (QA automation and backend). 3 × $10,000–$15,000 = about $30,000–$45,000 per month (the lead may price above that range, quoted per team).
• Build and test pod: testing folded into a pod that is also building the agents, around 6 senior engineers. 6 × $10,000–$15,000 = about $60,000–$90,000 per month.
• Single engineer on your team: one senior QA automation engineer with agent experience, working with your existing team.

You get a scoped plan, a price and the names of the people who would do the work before anything starts.

Dedicated team or staff augmentation?

Choose a dedicated AI pod when you need agent testing designed and running across several agents or a risky launch. Choose staff augmentation when your team owns the agents and needs a senior engineer to build out the suite with them. Our AI agent development pods ship agents with this kind of testing from the start, and MCP server development covers the tools agents call. Production agents our pods have shipped include an AI driver-support agent; see more case studies.

When Ryz isn't the right fit

If you need a crowd of manual testers for a short burst, a crowdtesting service fits better. If you want a packaged testing platform with no engineering work on your side, buy one directly. Our pods build and run agent testing as engineering work on your team.

Related

• LLM evaluation and observability
• AI agent development services
• Test automation services
• QA testing services
• Hire AI agent developers
• AI agent development companies

FAQ

What is agentic QA?

Agentic QA is quality assurance for AI agents: software that plans multi-step tasks and calls tools on its own. It tests the whole path an agent takes, including which tools it calls, with what arguments, and whether it respects approvals and permissions, not just whether its final answer reads well.

How is testing an AI agent different from testing a chatbot?

A chatbot mostly produces text, so you can grade its answers. An agent takes actions in your systems, so a test has to check the actions and their order, run in a sandbox where those actions are safe, and cover failures like tool errors, loops and injected instructions.

Do we need to rebuild our agent to test it this way?

No. The pod wraps your existing agent with a harness, points its tools at a sandbox or staging environment and adds scenario tests in your CI.

How much does it cost?

Typical Ryz cost is $7,000 to $15,000 per engineer per month, so a three-person agent QA pod runs about $30,000 to $45,000 per month. You get a scoped plan, a price and names before you start.

How fast can work start?

After the scoping call we propose a team with names and a price. Most of the timeline depends on scope, environment access and your onboarding.

Questions we didn't answer? Email info@ryzlabs.com.