Agentic quality engineering

Agentic quality engineering for real‑world software.

We use AI agents to explore your product, understand where its quality gaps are, and build a QA platform that evolves with it.

Talk to me on LinkedIn How we work
auth checkout orders api webhooks admin agent path finding

01The problem

Why testing keeps falling behind

Most products are changing faster than anyone can validate them. The usual answers create their own drag.

  • Coverage can't keep up.

    Products change faster than people can check them, and conventional automation costs real engineering effort to build and keep alive.

  • Feedback arrives late.

    The distance between a change and credible evidence that it is safe is measured in days, not minutes.

  • Automation resists change.

    When every product change means repairing tests, QA becomes friction against the roadmap.

  • Security sits apart from quality.

    Basic security validation happens occasionally and elsewhere, instead of continuously alongside everything else.

  • Test suites become script piles.

    Nobody treats the QA capability as a product, so it ends up undocumented, unobservable and hard to evolve.

  1. Better understanding
  2. Broader coverage
  3. Faster feedback
  4. Greater confidence
  5. Safer innovation

Available now

Agentic QA Assessment & Exploration

Take a defined product or product area. Our agents explore it, learn how it actually behaves, compare that to what your documentation and requirements say, map the critical paths, and surface defects, inconsistencies and risks. You get a grounded picture of the current quality state and a clear view of what automation would actually pay off.

What you get

  • A behavioural map of the product area, as the agents found it
  • Findings: defects, inconsistencies, and gaps between implementation and requirements
  • Critical paths and risk areas, ranked
  • A recommendation on what to build next, if anything

Scope and depth are agreed per engagement. This is not a fixed package.

02Where it can lead

A QA platform built around your product

A tailored agentic QA platform is not a tool you install. It is engineered around your product, your environment and your delivery pipeline, and it grows with them. Which capabilities exist for you depends on what your product needs.

  • 01Critical-path and regression testing
  • 02Test strategy and test-case generation
  • 03Continuous exploratory testing
  • 04Product-behaviour knowledge base
  • 05Test data generation and management
  • 06Requirements integration: Jira, Confluence, your docs
  • 07Environment provisioning and isolation
  • 08API, UI and system adapters
  • 09Basic security-oriented testing
  • 10Reporting and CI/CD integration
  • 11UX-oriented assessment later

Early on, the work is intelligence-heavy. Over time, what agents keep doing becomes cheaper, deterministic automation, and the agents move to operating and maintaining it.

03How we work

Explore, then build only what pays off

  1. ExploreAgents learn the product by using it and reading what exists.
  2. AssessFindings, risks and critical paths, with a recommendation.
  3. DesignThe shape of a QA capability that fits your product and pipeline.
  4. BuildTests, tooling, data, adapters and workflows, engineered as a product.
  5. IntegrateInto your environments, delivery process and reporting.
  6. EvolveThe capability changes as the product changes.

Not every engagement runs the whole sequence. Some stop at Assess.

Configurable autonomy within explicit boundaries

Three levels, chosen per workflow to match your risk tolerance.

Supervised

Agents propose. People approve every action.

Bounded

Agents run agreed workflows in agreed environments. People handle exceptions.

Autonomous

Established workflows run on their own. People handle exceptions, decisions and change.

04Working together

What an engagement needs

Where practical, product-specific data, knowledge and artefacts stay in infrastructure you control. Deployment details are agreed per engagement.

  • A defined QA problem and what a good outcome looks like.
  • Access to a suitable environment to run against.
  • Somewhere for results and knowledge to live, in your tooling or ours.
  • Nice to have: documentation, requirements, existing tests. Not required; agents can learn by exploration when documentation is thin.
  • Willingness to adjust process where it helps. That is part of the engagement, not a surprise.
Emerging AI techniques, applied with software‑engineering discipline.

Models, agent frameworks and tooling will change, probably within the lifetime of your engagement. What stays constant is the method: how agents learn a product, find risk, build and validate tests, and turn expensive exploration into reliable automation.

We only take engagements where agentic technology genuinely matters. If your problem is better solved by a conventional QA team, we will say so.

Have a product this could apply to?

No sales team, no booking form. Send a message and we'll talk about your product.

Message me on LinkedIn