AI agents can be unpredictable. Your control layer shouldn't be.

In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.

This makes multi-agent systems fundamentally different from single-agent applications. Looking at each agent independently, or simply checking the final answer, does not tell us whether the collaboration itself worked.

The solution is to evaluate what happens between agents. We need to see how information moves through the workflow, how agents hand off results, how shared state changes, and whether different agents actually provide independent reasoning.

This article presents a practical approach to evaluating these interactions and using the results to build stronger engineering controls. The goal is not to make LLMs deterministic, but to make their unpredictable behavior visible, measurable, bounded, and recoverable.

Why agent collaboration needs evals

Large language models (LLMs) are inherently nondeterministic. In a multi-agent system, this uncertainty does not disappear. One agent generates information, another agent consumes and interprets it, and its output may then become the context for the next agent. Any falsely generated or interpreted content propagates and amplifies across the entire workflow.