Descriptions:
Andi Partovi, CTO and co-founder of Various AI, makes a rigorous case at AI Dev SF 2026 that conventional testing methods—golden datasets, unit tests, static evaluation sets—are structurally inadequate for autonomous, action-based AI agents, and that simulation environments are the necessary replacement. The argument is grounded in control theory: unlike deterministic software or fully observable game-playing agents, real-world AI agents operate in Partially Observable Markov Decision Processes (POMDPs), where environment state is hidden, user intent is unknown, and correct behavior is context-dependent.
Partovi outlines three core properties that make agents untestable with traditional methods: non-determinism (the same input can produce different outputs), interactivity (tests must simulate back-and-forth with external systems, not just match static input-output pairs), and dynamic labels (whether an action is correct depends on what the environment does in response). He illustrates this with a supply chain sourcing agent that negotiates over email and a financial agent that correctly refuses a transaction when authentication fails—cases where a golden dataset would either miss the right answer or penalize intelligent behavior.
The session closes with a breakdown of what constitutes a well-designed simulation environment: realistic user personas including adversarial ones, faithful tool and service replicas, and support for running many interactions at scale to account for non-determinism. Partovi frames the analogy simply: simulation environments are the Matrix for AI agents—a place to make mistakes safely before those mistakes reach production.
📺 Source: DeepLearningAI · Published May 22, 2026
🏷️ Format: Deep Dive
