Testing & Evaluation in the Agentic Age: Watch the Recording
Building an AI agent is only half the challenge. Knowing whether you can trust it in production is where the real work begins.
The recording of our AI Expert Circle session, Testing & Evaluation in the Agentic Age, is available. The session explores a fundamental shift in how we need to think about quality: from testing whether software produces the expected output to continuously evaluating agent behavior and building evidence that supports greater autonomy.
What we covered
The session looks at several practical building blocks for testing and governing GenAI and agentic solutions:
- Golden reference datasets: establishing evaluation baselines that make it possible to compare the behavior of prompts and agents consistently.
- Repeatable evaluation: comparing different prompts, contexts, and agent variants rather than relying on one-off testing.
- Multi-signal evaluation: combining human feedback and SME annotations with technical metrics, process outcomes, and business signals.
- Silent evaluation: collecting production evidence without introducing operational risk before increasing an agent's autonomy.
- Human-in-the-Loop patterns: using human involvement as part of a deliberate progression from assisted toward more autonomous operation.
- Governance and monitoring: continuing evaluation beyond go-live rather than treating testing as a one-time release gate.
Three key takeaways
1. Agentic AI changes what “testing” means.
Traditional pass/fail testing works well when an application is deterministic. GenAI and agentic systems can produce multiple plausible outcomes from the same input, so evaluation needs to consider performance and behavior over time rather than validating only one expected answer.
2. Trust should be based on multiple forms of evidence.
No single metric tells you whether an agent is ready. The session explores combining SME judgment, human feedback, technical measures, process outcomes, and business signals to create a stronger basis for rollout and governance decisions.
3. Autonomy should be earned through evidence.
Silent evaluation, SME annotations, Human-in-the-Loop patterns, and ongoing monitoring provide ways to gather evidence before expanding autonomy.
From testing prompts to governing behavior
Perhaps the most important shift is this: the objective isn't simply to prove that an agent worked in a test. It is to establish an ongoing evidence loop that helps us understand when, where, and under which conditions we can trust it.
If you are building agentic solutions, leading delivery teams, or defining AI governance approaches, watch the recording for practical techniques and reusable patterns for designing, validating, monitoring, and governing AI systems.
Continue the conversation
If you’re working through similar questions around testing, evaluation, Human-in-the-Loop, or governing increasingly autonomous agents, we’d love to hear what you’re encountering in practice.
And if you’d like to discuss how these approaches might apply to your own use cases, please reach out to me or Glen Aronson. We’re always happy to exchange ideas, compare approaches, and learn from what others are seeing in the field.