The fastest way to create an Agentic QE architecture is to draw a box for every testing activity and label each one an agent. The better approach starts with the decisions those agents are expected to improve.

The easiest thing to build is another agent

Agentic AI has created a new pattern in Quality Engineering: take an activity, attach an agent to it, and call the result an agentic workflow.

Requirement agent. Test-generation agent. Automation agent. Defect agent. Performance agent. Reporting agent.

There is nothing inherently wrong with specialization. The danger is mistaking a collection of automated workers for an intelligent assurance system.

The architecture should begin with the decision, not the agent.

Start by identifying decision boundaries

Before choosing an agent, ask what decision the quality system is expected to improve.

Should a change receive deeper testing? Is the current evidence sufficient? Which failure deserves investigation first? Is a model response acceptable? Should a release proceed? Is an autonomous remediation safe to execute?

These are decision boundaries. Once they are explicit, we can determine what evidence is required, what reasoning is appropriate and where human authority must remain.

An agent should own a bounded responsibility

A useful agent is not simply a chatbot with a job title. It should have a clear scope of authority.

For example, a Change Risk Agent might be allowed to classify change exposure and recommend validation priorities. It should not silently approve release readiness. A Defect Intelligence Agent may correlate failure patterns and propose likely causes. It should not close a production defect simply because the agent’s confidence is high.

The stronger the autonomy, the stronger the need for boundaries.

Shared context matters more than agent count

If every agent constructs its own version of truth, orchestration becomes a relay race built on inconsistent context.

Agentic QE therefore needs a shared evidence layer: requirements, code-change context, dependency models, test history, defect history, telemetry, business criticality, model evaluations and policy.

Dynamic retrieval can help, but retrieval alone is not enough. The system also needs freshness, provenance, permissions and a way to distinguish authoritative evidence from useful but weak context.

Orchestration should expose disagreement

Most workflow diagrams assume the agents agree. Real intelligence systems should expect disagreement.

A Risk Agent may classify a change as high exposure. A Coverage Agent may report strong regression evidence. A Runtime Agent may flag degraded telemetry. A Release Agent must now reconcile conflicting evidence.

That conflict is not a failure of orchestration. It is the reason orchestration is valuable.

The system should preserve the disagreement, explain the competing signals and identify what additional evidence could resolve it.

Provenance is the difference between autonomy and theater

An agent that produces a recommendation without showing how it arrived there creates the appearance of intelligence but weakens governance.

Every meaningful agent decision should carry provenance:

  • What evidence was used?
  • How current was it?
  • Which policy or rule applied?
  • What confidence was assigned?
  • Which evidence was missing?
  • What action was taken?

Without that chain, humans are left reviewing output rather than governing reasoning.

Human-in-the-loop is too vague

“Human-in-the-loop” has become a comfortable phrase because it sounds safe. But it does not specify what the human actually controls.

A mature design should define the human role explicitly.

Humans may set policy, approve risk thresholds, override recommendations, authorize high-impact actions, review contested evidence, investigate low-confidence reasoning, and audit autonomous outcomes.

The goal is not to insert approval clicks everywhere. The goal is to place human authority where consequence and uncertainty justify it.

Measure decision quality, not generated artifacts

If an Agentic QE program is measured by the number of generated tests, scripts or agents, it will optimize for visible activity.

Better measures are harder but more meaningful:

  • Did risk-based prioritization improve defect discovery relevance?
  • Did investigation time decrease without increasing false conclusions?
  • Did the system identify evidence gaps earlier?
  • Did release decisions become more consistent and explainable?
  • Did autonomous actions remain within policy?

A simple architecture principle

I would design Agentic QE in five layers:

Evidence: the facts and signals the system can use.

Context: domain, dependency, change and policy understanding.

Specialized reasoning: agents with bounded responsibilities.

Orchestration: coordination, challenge, escalation and shared state.

Decision & governance: confidence, provenance, human authority and action limits.

The agents sit in the middle. They are not the architecture.

Agentic QE becomes useful when it changes the operating model

The meaningful leap is not from manual work to generated work. It is from disconnected activities to coordinated reasoning.

That requires us to stop asking, “What agent can we build for this task?”

Instead, start asking:

What quality decision are we trying to improve, and what evidence, reasoning and authority does that decision require?

Once that is clear, the right agents become much easier to design.