From assistant to teammate
A chat assistant waits to be asked. An agent takes a goal, plans the steps, uses tools and checks its own work. For QA, that means an agent can open the app, explore a new feature, write the tests, run them and come back with a pull request and a summary.
The difference between a useful agent and a risky one is design: a narrow job, the minimum tools it needs, clear stopping points and a human who approves what it produces. That's what we build.
Agents we build
Exploration agent
Navigates a feature in a real browser and maps pages, flows and locators.
Test-writer agent
Turns approved test cases into code in your framework, then runs it.
Reviewer agent
Checks test pull requests for weak assertions, hard waits and missing cases.
Triage agent
Reads CI results overnight and files explained, deduplicated bugs.
Release-check agent
Runs a smoke pass on a deploy and posts a go or no-go summary.
Orchestrator
Coordinates several subagents across a ticket, from cases to merged tests.
How we keep agents safe
- One job per agent: Small, focused agents are easier to test, trust and improve.
- Least-privilege tools: Each agent gets only the MCP tools and permissions its job needs.
- Hooks as guardrails: Automated checks block secrets, production targets and unsafe commands.
- Human checkpoints: Approval before test code is generated and before anything merges.
- Evaluated like software: Agents have their own test sets, so changes don't quietly make them worse.
What you get
- Working agents: Subagent definitions or SDK code in your repo, ready to run.
- CI integration: Agents wired into your pipeline with clear triggers and outputs.
- Runbook: How to run, monitor, improve and switch off each agent.
- Evaluation set: Scenarios that prove each agent still does its job.
Where it fits
Claude agents sit on top of your test framework and CI. We often pair them with Claude skills, so agents follow the same standards your team does. Related: QA automation, Claude-assisted testing and our AI in QA blog.