Multi Agent Testing: How to Verify What AI Agents Do

Multi-agent testing evaluates AI systems in which multiple agents route requests, call tools, delegate work, and alter real systems, focusing on failures at the seams between agents rather than only on the quality of final responses. Such systems can fail through incorrect routing, lost constraints during handoffs, silent tool-call failures, policy violations, loops, concurrent write collisions, and confident but inaccurate summaries, making transcripts insufficient evidence of success. Effective testing covers individual agents, routing and delegation, handoffs, and end-to-end effects, while using outcome-based criteria such as whether a refund, notification, audit record, or other external artifact actually exists instead of requiring a fixed execution path. Tests should grade criteria against observable evidence including tool-call records, filesystem changes, and produced artifacts, with separate pass, fail, and unable-to-verify outcomes so pass rates do not include unobserved assumptions. Adversarial scenarios, including prompt injection, instruction overrides, tool misuse, policy-boundary probes, and data-exfiltration attempts, should be included from the outset and treated as security findings when successful. In CI, tests should run in controlled staging environments, distinguish infrastructure failures from agent defects, and gradually establish thresholds for unverifiable results. TestMu AI’s Agent Assurance is presented as an evidence-based platform that derives scenarios from code, evaluates autonomous agents by their real effects, reports criterion-level outcomes, and separately supports quality evaluation for conversational agents.

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