Validating agentic behavior when “correct” isn’t deterministic (opens in new tab)
Agentic systems such as GitHub Copilot cloud agent can complete tasks through multiple valid action sequences, making traditional deterministic tests unreliable. Timing changes, loading screens, and UI differences often produce false negatives even when the agent achieves the correct result. The post proposes an independent “Trust Layer” that validates essential outcomes and convergent behavior rather than rigid step-by-step execution paths. ## Challenges of Agent-Driven Validation - An agent may adapt to network delays or changing UI conditions and still complete its task successfully. - Conventional CI tests can fail when execution no longer matches a recorded script or expected assertion timing. - This creates a trust gap: - **False negatives:** successful tasks are reported as failures. - **Fragile infrastructure:** rendering, timing, and environment noise affect test results. - **Compliance trap:** valid behavioral variation is mistaken for regression. - Agent correctness should focus on reliably achieving essential outcomes, not reproducing an identical sequence of actions. ## Why Traditional Testing Breaks Down - **Assertion-based tests** require manually specifying every expected check and often omit valid alternative paths. - **Record-and-replay tools** are highly sensitive to timing and rendering differences. - **Visual regression tests** compare screenshots without understanding semantic meaning or the broader workflow. - **ML-based oracles** need large training datasets and generally provide little explanation for their decisions. - All four approaches assume correctness means following a stable sequence of observable states, which does not fit autonomous agents. ## Essential, Optional, and Convergent Behavior The proposed approach distinguishes between behavior that determines success and behavior that merely reflects environmental variation: - **Essential states:** Required milestones, such as reaching a VS Code “Search Results” screen. - **Optional variations:** Incidental states, including loading spinners or decorative UI changes. - **Convergent paths:** Different action sequences—such as using a keyboard shortcut or a menu—that eventually reach the same result. - A loading screen may appear in one run and not another, but the appearance of search results is what establishes success. ## Dominator Analysis The post connects this model to **dominator relationships** from compiler theory: - In a control-flow graph, node A dominates node B when every path to B must pass through A. - Applying dominator analysis to agent execution traces can identify: - Mandatory states - Optional states - Points where different execution paths converge - This produces a minimal and explainable definition of correctness instead of relying on every recorded step. ## Graph-Based Execution Modeling - Agent behavior should be represented as a graph rather than a linear script. - Graphs capture branching paths, optional states, and convergence points. - This structure provides a foundation for lightweight, explainable validation in GitHub Actions and other CI environments. A reliable validation system for agents should test whether essential outcomes occurred and whether critical invariants held, while ignoring harmless differences in timing, rendering, and execution order. This outcome-oriented Trust Layer can reduce false failures and make agentic workflows more dependable in production CI pipelines.