Who Independently Assures AI Agents?
AI agents reason across steps, call tools, use enterprise context and take action on their own. Conventional QE was not built to independently prove that behavior — and QE itself has to keep pace with the agents it's checking. We think that's QE's next mandate.
AI Behavior Has Become a QE Problem.
Increasingly capable agents reason across steps, use tools and touch enterprise systems in ways conventional regression testing was never designed to evaluate.
QE Now Has Two Jobs — and They Are Not the Same.
One governed Harness. Two different missions: independently assure AI systems, and use governed agents to compress the QE lifecycle itself.
Assure AI Systems
Independently test systems where "correct" is not always binary.
- Evaluate non-deterministic model and agent behavior, not just expected outputs
- Test groundedness, hallucination, bias, safety and policy adherence
- Preserve human review and release gates where judgment still matters
Accelerate the SDLC
Use governed agents to remove QE work from the critical path.
- Orchestrate test planning, design, automation, test data and execution
- Generate and maintain Playwright / Selenium / API scripts
- Self-heal tests, optimize regression and accelerate RCA
Autonomy Without a Harness Is Just Uncontrolled Automation.
Individual agents are easy to create. The enterprise problem is proving how they behave — what context they use, what they're permitted to do, and how outcomes are measured.
Reusable QE Agent Skills
Atomic, reusable QE agents for eval engineering and AI-behavior validation, plus test generation and automation — embedded directly in your IDE. No vendor lock-in.
25+ Skills · Onboarding in 2–3 DaysOrchestration + Context
The governed layer: project-specific context, centralized Agent Skills, RBAC and integration with Jira, GitHub, Azure DevOps and Jenkins through an MCP layer.
Context + Governance + SDLC IntegrationHuman Gates + QE Architecture
QE architects fluent in both disciplines — automation for SDLC acceleration, and eval engineering for AI QE — because AI-led QE fails if either is missing.
QE Architects + AI-Ready EngineeringWe Will Separate Evidence From Ambition.
Apexon has measured outcomes from QE automation and modernization engagements. AI-system assurance is the newer discipline — we'll demonstrate it live rather than manufacture a case-study number.
Targeted regression-suite automation reached 100% in six months, with a 45% productivity gain, in an energy engagement.
A digital-banking engagement delivered 40% lower automation-led QE cost and 60% lower test-maintenance effort.
A regulated U.S. financial-services engagement reached compliant synthetic test-data coverage and cut testing cycle time 40%.
Four Things You Can Take Back to Your Team.
An AI assurance framework — groundedness, permissions, policy and escalation.
Two scorecards: behavioral reliability for AI; cycle time and economics for Agentic QE.
A reference Harness architecture spanning context, skills, RBAC and human gates.
A governance checklist for scaling autonomous QE and releasing agentic systems.
For Leaders Accountable for AI Assurance and Engineering Quality.
Primary: QE and Engineering leaders who own automation coverage, cycle time and release confidence. Also relevant: AI Platform, Product, Security and Governance leaders who need independent evidence that agentic systems behave as intended — and CTOs accountable for release velocity and AI product risk.
60 Minutes. Two Missions. One Governed Harness.
Built and Presented by QE Practitioners.
Reserve Your Spot
Wednesday, September 23, 2026 · 11:00 AM ET · Virtual · 60 minutes. Recording sent to every registrant, live or not.
- Live AI-QE evaluation on a real agentic workflow
- Agentic QE demo + measured client outcomes
- Live Q&A with Apexon's QE engineering leadership
FAQ
Before you register
No. We will show our architecture because that is the easiest way to make the discussion concrete, but the session is designed to stand on its own. Bring your own QE problem, challenge the approach, and take whatever is useful back to your team.
Yes — sent to every registrant, whether or not you attend live.
Because the value is not the individual agent. The harness supplies reusable QE skills, enterprise context, tool access, orchestration, governance, human gates and measurement. It is what allows agentic QE to move from isolated engineer productivity to a repeatable operating model.
Using AI to test means orchestrating agents to reduce the effort and cycle time of QE work. Testing AI means evaluating the behavior of models and agents themselves — groundedness, hallucination, bias, safety, tool use and non-deterministic outputs. They share a harness, but they require different test logic and different success measures.
QE, automation and engineering leaders accountable for productivity, automation coverage, cycle time, release confidence and QE cost — plus AI and agentic product leaders who need a QE discipline that actually understands non-determinism, not a regression suite repurposed for something it wasn't built for.
