Goal intake
Translate release risk, user journeys, and business rules into a bounded test mission.
QA ALIGN Agentic QA Workflow
Give QA agents goals. Keep release authority governed.
Move beyond isolated test generation. QA ALIGN designs the full workflow: agents plan, execute, observe, diagnose, and maintain tests while deterministic evidence and human approval control what reaches production.
Private assessment · bounded access · no autonomous production changes
Goal Plan Execute Evidence Approve
The distinction that matters
An agent that writes scripts is a feature. A governed QA workflow is a system.
Speed without authority boundaries, assertions, observability, and approval gates produces faster uncertainty. The commercial advantage comes from autonomous work that still earns trust.
Full operating model
The agent workflow, end to end
Each stage has a contract. No stage silently promotes its own output.
Agent planning
Generate scenarios, edge cases, data needs, and assertions under explicit constraints.
Controlled execution
Run deterministic browser and API checks with scoped credentials and isolated state.
Evidence capture
Preserve traces, screenshots, logs, classifications, and the exact reason for failure.
Governed decision
Apply risk thresholds and human approval gates before evidence can affect release.
Maintained system
Propose repairs, revalidate assumptions, and keep every change auditable.
Evidence before confidence
Agents may act. Evidence must still prove the result.
QA ALIGN separates execution from judgment. A run produces structured evidence; governance decides whether that evidence is trustworthy enough to influence a release.
- Traceable test intent and assertion ownership
- Failure classification with supporting artifacts
- Risk thresholds that produce GO / WARN / BLOCK
- Human approval for sensitive or uncertain outcomes
Readiness assessment
Eight controls determine whether agentic QA is ready to scale
01 Agent authority
What may the agent read, change, retry, or approve?
02 Assertion strategy
Which outcomes prove the user journey actually worked?
03 Data boundaries
Can test data expose production, customer, or privileged information?
04 Prompt security
Can page content or external input redirect agent behavior?
05 Evidence contract
Does every run create inspectable, attributable artifacts?
06 Release gates
Which decisions require deterministic rules or human approval?
07 Exception handling
What happens when the agent is uncertain or the system drifts?
08 Auditability
Can your team reconstruct what the agent saw, did, and decided?
Assessment output
Know what agents can own now, what needs controls, and what must stay human.
BLOCK
Unsafe authority, weak evidence, or uncontrolled data.
WARN
Useful automation exists, but governance must be added before scale.
GO
Bounded agents can operate inside verified controls and approval gates.
What your team receives
A decision-ready plan, not a trend report
The review turns your current automation, AI usage, release controls, and evidence model into an implementation sequence engineering leaders can inspect.
- Agentic QA readiness score
- Authority and permission boundary map
- Assertion and edge-case coverage review
- Prompt-injection and untrusted-input risk review
- Evidence and observability gap analysis
- Human approval and release-gate design
- Prioritized 30 / 60 / 90-day engineer plan
Start with readiness
Build the workflow before autonomous testing becomes autonomous risk.
We will map the first safe agent boundary, the evidence it must produce, and the approval gate it must pass.
Request the readiness review