LABS
Let's talk

QANTUM LABS / QA & AI

QA AI agents with clear tasks and reviewable results

A QA AI agent can help propose tests or investigate failures when its task is well defined. We design the workflow, its limits and how to measure its value for your QA professionals with you.

Talk to QAntum Labs

Which QA tasks to evaluate with AI agents

We start with tasks that allow review: suggesting scenarios from a requirement, organizing evidence or proposing failure hypotheses. We define which information the agent can use and which output format helps the team check its proposal.

For example, an agent analyzing a failed test can receive the step, error and a bounded trace. Its answer should separate observations from hypotheses and identify checks that could confirm them. The failure's cause still needs evidence from the system.

Permissions, tools and professional review

We design access to repositories, environments and tools according to the task. Reading evidence and modifying code have different consequences. We agree which actions need approval, which information can reach a provider and how activity is recorded.

For a workflow proposing test cases, review should check their relationship to the requirement, preconditions and expected outcome. For actions involving Jira or Azure DevOps, the destination and content should be checked before information is published or modified.

How to measure an AI testing agent

We compare the assisted workflow with reference examples and the team's current process. We measure valid proposals, relevant coverage, introduced errors and review time. Savings also depend on the effort needed to correct a convincing but incorrect suggestion.

In RunTrail, AI investigation can generate reports on explicit action with a configured provider. Reports support human review. Projects involving agents with greater autonomy need their own scope and evaluation.

Questions about QA and AI.

Can an AI agent replace a QA professional's review?

Our approach includes professional review and acceptance criteria. Autonomy is assessed by task, impact and evidence of reliability; each team needs to agree who is accountable for quality decisions.

Does RunTrail fix code and open pull requests autonomously?

Currently, AI investigation produces reports for review when enabled and configured. Autonomous fixes and pull request creation require additional capabilities and are outside that current workflow.

QA for artificial intelligence you can evaluate

Artificial intelligence applications need quality criteria adapted to their behavior. We help define evaluations for LLMs, RAG systems and agent workflows using representative cases and reviewable evidence.

Bring this strategy to your project.

Tell us how your team works, which tools you use and which risks you need to address. We can discuss the scope of a consulting or evaluation engagement together.

Talk to QAntum Labs