How to test multi-agent systems

Multi-agent AI systems can fail even when each individual agent passes isolated tests because coordination problems such as incomplete handoffs, shared-state conflicts, duplicated work, ownership loops, and unsupported final responses emerge only across the full workflow. Effective evaluation therefore requires representative, controlled tasks with resettable tools and shared state, verifiable final outcomes, and separate scoring for task completion, handoff completeness, state integrity, tool use, efficiency, safety, and termination. Testing should operate at agent, handoff, and system levels, using real upstream payloads and contracts that validate required fields, provenance, and formats before downstream agents act. Connected end-to-end traces that capture routing, model outputs, tool calls, state writes, and errors enable teams to identify the first decision responsible for a failed outcome, including across services through propagated trace context. Since valid systems may follow different action sequences, evaluations should enforce required dependencies and final-state constraints rather than a single fixed trajectory while detecting redundant actions, unsafe behavior, loops, and excess steps. Repeated trials reveal consistency issues hidden by single runs, and experiments should isolate changes to prompts, models, tools, or orchestration while retaining shared datasets and configuration metadata. The approach recommends enforcing high-signal evaluations in CI, monitoring sampled production traces, and converting confirmed production failures into regression cases so coordination defects become permanent release checks.

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