Goals instead of scripts
Scripted automation is precise but brittle: it checks exactly what it was told and breaks when the app changes. Testing agents are given an objective, such as "complete checkout as a returning customer", and work out the steps, which makes them good at exploration and resilient to change.
They are not a replacement for deterministic regression tests. We use agents where flexibility pays off and keep fast, scripted tests where precision matters, then make sure both report into the same place.
What we build
Exploratory testing agents
Agents that roam new features, try edge cases and log what they find.
Goal-based smoke checks
Plain-language journeys run after each deploy, resilient to layout changes.
Self-healing pipelines
Agents that repair broken tests and open pull requests for review.
CI and production monitors
Agents that watch runs and metrics and explain what changed.
AI agent evaluation
Test suites for your own LLM features: accuracy, refusals, tool calls and cost.
Observability
Tracing agent runs with tools like Langfuse so every decision is reviewable.
How we make agents trustworthy
- Pick the right model and framework: We recommend based on your stack, data rules and budget, not vendor loyalty.
- Deterministic where it matters: Critical assertions stay scripted; agents handle exploration and adaptation.
- Full traces: Every step, tool call and decision is recorded for review.
- Evaluation before rollout: Agents are scored on known scenarios before they're trusted with new ones.
- Clear limits: Sandboxed environments, test data only and human approval for changes.
Good starting points
- Post-deploy smoke agent: A goal-based check of key journeys after every release.
- Exploratory sweeps: Agent sessions on new features before they reach regression.
- Nightly triage: An agent that explains failures before the team logs on.
- LLM feature evaluation: A test harness for the AI features in your own product.
Where it fits
Agentic testing complements your existing automation. We usually start with one agent on one workflow, measure it against your current process and expand from there. Related: QA automation, Claude-assisted testing and our AI in QA blog.