Multi-Agent Testing
Multi-agent testing checks a system where several AI agents work together — each with its own role, tools, and reasoning loop — to confirm the group actually completes the task correctly, not just that each agent behaves well in isolation. Most failures happen at the handoffs between agents, not inside any single one.
A single well-tested agent can still produce a broken multi-agent system, because the failure modes are different: agents can talk past each other, duplicate work, get stuck waiting on a peer that never responds, or silently drop context when a task is handed off. Testing one agent in isolation catches none of this.
In practice this means running the full agent team on realistic multi-step tasks and inspecting the transcript, not just the final answer — did the researcher agent actually hand its findings to the writer agent, or did the writer agent hallucinate them instead? Teams often build a small set of "known good" multi-agent transcripts as a regression baseline, since the same task can succeed via wildly different (and not equally acceptable) agent-to-agent paths.
Nondeterminism compounds fast here — with N agents each making independent model calls, the number of possible interaction paths grows combinatorially, so coverage has to lean on sampling many runs of the same scenario rather than a single pass/fail check.
