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A multi-agent pipeline can fail even when each agent follows its own instructions correctly: the sender may consider its work complete while the receiver expects a verified artifact. The fix is to make the handoff contract explicit—what must be delivered, how completion is checked, what evidence accompanies it, and what happens when the check fails. A fluent “done” message is a claim, not proof that the overall objective has been met.
How two agents can both be right and the pipeline still fail
Each agent works from its own role, context, and instructions. A producing agent might return a summary because its task says to report findings. The next agent may need the underlying data, a passing test, or a documented state change before it can continue. If the pipeline never specified which of those outputs constitutes completion, the agents can meet their local expectations while the overall task remains unfinished.
This is a missing contract at the handoff. The sender needs to know what it must produce; the receiver needs to know what it can rely on; and the system needs an explicit test for whether the end objective has been met. The mismatch is one useful way to understand multi-agent failures, not a claim that it is the single most common cause in every deployment.
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What the multi-agent failure study establishes
Cemri and coauthors’ 2025 paper, “Why Do Multi-Agent LLM Systems Fail?”, introduces MAST, a taxonomy of 14 failure modes grouped into specification and system design failures, inter-agent misalignment, and task verification and termination. The authors analyzed 1,642 annotated execution traces from seven multi-agent systems working on coding, math, and general-agent tasks. Their initial taxonomy work examined more than 150 traces from five frameworks.
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Within that study’s observed traces, 12.4% exhibited the failure mode called “not recognizing task completion.” That figure is not a rate for all deployed agent systems, nor does it directly measure agents disagreeing about the definition of “done.” The study supports the broader point that design, coordination, and verification can all contribute to failures; it does not rank incompatible completion criteria as the universal top failure mode.
The authors report a Cohen’s kappa of 0.88 for agreement among human annotators during taxonomy development. That describes consistency in how annotators applied the taxonomy, not the reliability of the systems themselves. The taxonomy is presented as a foundational, non-exhaustive step, and similar symptoms can have different causes. A stalled or incomplete output therefore needs diagnosis from the execution trajectory and handoffs rather than an automatic assumption that the same mismatch occurred every time.
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Write a contract for every agent handoff
For each transition, specify the parts of the contract that the receiving stage needs to proceed without guessing:
- Deliverable: Name the required output shape and contents. For example, require a structured result with the relevant findings and a link or identifier for the artifact, rather than an unconstrained status message.
- Acceptance conditions: State observable criteria for completion, plus what counts as incomplete or blocked. “Analyze the change” is vague; “return the changed files and report whether the specified test passes” is more checkable.
- Evidence: Identify what the next stage can inspect—such as source material, tool results, test output, or a recorded state change. Distinguish evidence from the agent’s summary of that evidence.
- Ownership: Define which agent may propose completion and which component verifies it. A worker can report that its step is finished without being the authority that marks the whole workflow complete.
- Failure route: Say what to do when evidence is missing, contradictory, or inconclusive: request clarification, retry a bounded repair, or escalate to a human or controller.
These are implementation recommendations consistent with the study’s failure categories, not a universally tested recipe. The paper discusses strategies such as clear role and task definitions, standardized communication protocols, cross-verification, comprehensive verification, and unit-test generation. It also cautions that reliability may require changes to the system itself, not only a better prompt.
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Separate the completion claim from the completion check
A useful workflow treats an agent’s completion message as a proposed status and checks the required evidence independently. If an agent says a task passed, the verifier should be able to inspect the relevant test result or state—not merely repeat the same claim. This reduces the risk that one unsupported assertion becomes both the deliverable and its proof.
Keep execution traces that show intermediate outputs, acceptance decisions, and handoffs. When an end result is wrong or absent, inspect where the expected artifact stopped matching the contract: Was the deliverable missing? Did the receiver apply a different acceptance rule? Was evidence available but ignored? Did the workflow declare completion too early or fail to recognize it? The trajectory helps distinguish these causes, which can look alike in a final response.
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For LLM application evaluation and tracing, LangSmith’s evaluation documentation describes evaluation workflows. A tool can help organize checks and runs, but it does not replace explicit acceptance criteria or establish that a particular pipeline is reliable.
Choose a verification approach by its trade-offs
There is no single architecture established as best by the cited study. Compare options against the demands of the workflow:
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- Acceptance clarity: Can the criteria be checked from the handoff artifact, or do they depend on interpreting a vague summary?
- Verification independence: Can the check inspect evidence beyond the producing agent’s own completion statement?
- Traceability: Can a failure be localized to a particular stage or handoff?
- Overhead: What additional latency, model calls, and maintenance does verification add?
- Recovery behavior: Does a failed check lead to a defined repair, clarification, or escalation rather than an ambiguous retry?
The appropriate balance depends on the task and the cost of an incorrect completion. A lightweight check may suit low-risk steps; work that changes important state needs criteria and evidence strong enough for someone else—or another system component—to verify.
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