Single-agent evals miss handoff failures. When Agent A passes bad context to Agent B, both look fine individually. You need conversation-level tracing that attributes failures to the right node.
In a single-agent system, the agent that produced the bad output is the agent that caused the problem. In multi-agent systems, that's often wrong. The agent producing the bad output may be responding correctly to bad input from an upstream agent.
End-to-end evaluation says “the conversation failed.” Per-agent evaluation says each agent did fine individually. Neither tells you where the actual problem is.
Each mode requires a different diagnostic approach and a different fix.
Agent A collects important information from the user. When it hands off to Agent B, some or all of that context is lost. Agent B asks the user to repeat themselves or proceeds without critical information.
The end-to-end conversation fails, but it's unclear which agent caused the failure. Agent B gives a bad response - but only because Agent A passed it incorrect context. Blaming Agent B leads to fixing the wrong thing.
A small error in one agent compounds through the chain. Each subsequent agent makes reasonable decisions based on bad input, but the end result is completely wrong.
Multiple agents act on the same request without coordination. One agent resolves the issue while another escalates it. Or two agents give the user conflicting information.
| Single-agent eval | Multi-agent attribution | |
|---|---|---|
| What gets scored | Final output only | Each agent's contribution independently |
| Failure attribution | "The agent failed" | "Agent A's context handoff failed at turn 3" |
| Handoff quality | Not measured | Scored for completeness and accuracy |
| Root cause | Often misattributed to the last agent in the chain | Traced to the originating agent |
| Fix target | The agent that produced the bad output | The agent that caused the bad output - which may be different |
Every agent's contribution is recorded - including the handoff payloads between agents. Each turn is tagged with which agent handled it.
Each agent is scored on its own performance - including handoff quality. Did it pass complete context? Did it route correctly? Did it handle the task given its input?
When a downstream agent fails, the system checks: did it fail because of its own behavior, or because of bad input from upstream? Attribution goes to the originating agent.
The fix targets the agent that actually caused the failure. Testing runs the full multi-agent flow to catch cascading effects before deployment.
Single-agent evals grade the final output. In a multi-agent system, the final output is the result of a chain of agent interactions. When the output is wrong, single-agent evals can't tell you which agent in the chain caused the failure. Worse, they often attribute the failure to the last agent - which is usually the one that produced the output, not the one that caused the problem.
Context loss happens when information collected or generated by one agent doesn't fully transfer to the next agent in the chain. This can be explicit (the handoff payload doesn't include all relevant fields) or implicit (the receiving agent doesn't interpret the context correctly). It's the most common failure mode in multi-agent systems and the hardest to detect with end-to-end evaluation.
Conversation-level tracing scores each agent's contribution independently - including the quality of handoffs between agents. When a downstream agent fails, the system traces back through the chain to determine whether the failure originated in the downstream agent's behavior or in the context it received. This prevents fixing the wrong agent.
Yes. Simulation testing replays the full multi-agent flow with synthetic personas, including handoffs between agents. This lets you test changes to one agent's behavior and see how it affects the entire chain - catching cascading effects that single-agent testing misses.
First, diagnose whether the handoff failure is a context completeness issue (information not passed) or a context interpretation issue (information passed but misunderstood). Then generate a targeted fix - either to the sending agent's handoff format or the receiving agent's context processing. The fix is simulation-tested across the full chain and production-verified.
Connect your agents and let Converra trace, attribute, and fix failures across your entire multi-agent chain.
Start for free