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A multi-agent pipeline can break even when every agent follows its own instructions correctly: one hands off an artifact it considers complete, while the next expects more evidence, verification, or work. The practical fix is to define a contract for each handoff—what must be delivered, how completion is checked, and what happens when the check fails.
How two capable agents can still leave a task unfinished
Each agent operates from its own role, context, and instructions. The first may interpret “complete” as producing a draft or making a recommendation. The next may treat that output as an input that still needs tests, sources, or a change to system state. If nobody specifies which interpretation governs the handoff, both agents can meet their local expectations while the overall task remains incomplete.
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A fluent status message does not establish that the required artifact exists or that acceptance conditions have been met. “Done” needs to refer to something observable, not just an agent’s confidence or summary.
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Cemri and coauthors’ 2025 study, “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 spanning coding, math, and general-agent tasks. Their initial taxonomy work examined more than 150 traces from five frameworks.
Within the study’s observed traces, “not recognizing task completion” appeared in 12.4%. That figure concerns the study’s task-completion failure category; it is not a measured rate of agents disagreeing about the meaning of “done,” nor a population-wide estimate for all multi-agent systems. The paper reports Cohen’s kappa of 0.88 for agreement among human annotators during taxonomy development—not for system reliability.
The taxonomy is a starting point, not an exhaustive list. Similar incomplete outcomes can have different causes, so a team diagnosing a failure should inspect the trajectory and the specific handoff rather than assume every shortfall reflects the same kind of misalignment.
Write a contract for every handoff
Before connecting agents in sequence, make the sender’s obligations and the receiver’s expectations explicit. A useful handoff contract includes:
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Deliverable: Specify the required artifact, format, and contents. For example, require a patch plus changed-file list rather than a statement that the code was fixed.
- Acceptance conditions: State observable criteria for completion, as well as what counts as incomplete or blocked. “Tests pass” is more actionable when the required test suite and results are identified.
- Evidence: Name the sources, tool results, tests, or state changes the receiver can inspect. Separate evidence from the sending agent’s interpretation of it.
- Ownership: Clarify which agent may propose that a stage is complete and which component verifies that claim. A proposal to finish is not the same as an accepted result.
- Failure route: Define what happens when evidence is missing, contradictory, or inconclusive: request clarification, send the artifact back for repair, retry within limits, or escalate.
These are implementation recommendations informed by MAST’s categories, not a universally tested recipe. The paper identifies clear role and task definitions, cross-verification, standardized communication protocols, comprehensive verification, and unit-test generation as possible strategies. It also cautions that reliability may require system-level changes, not just revised prompts.
Verify the result independently of the completion claim
For consequential work, make verification a distinct stage with its own criteria. The verifier should inspect the artifact or state that matters—for example, run the specified tests or confirm that a requested change exists—rather than treating the previous agent’s summary as proof. Where independent checking is impractical, record that limitation instead of implying the result was verified.
Evaluate intermediate stages as well as final responses. Preserve execution traces, record what each agent received and returned, and compare completion claims with the evidence available at the time. This makes it easier to locate whether the contract was unclear, the evidence was absent, the receiver misunderstood the handoff, or the final objective was never checked. It is practical guidance consistent with MAST’s verification and termination category, not a guarantee that a particular workflow will be reliable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose safeguards by their trade-offs
No single architecture is established as best for every workload. Compare potential safeguards against the actual failure risk and operating cost:
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- Verification independence: Can the check inspect evidence beyond the sending agent’s own account?
- Traceability: Can a failure be tied to a particular stage or handoff?
- Overhead: What latency, additional model calls, and maintenance does the safeguard add?
- Recovery behavior: Does a failed check lead to clarification, repair, or escalation rather than silent continuation?
Evaluation and tracing tools can help teams inspect runs. For example, LangSmith’s evaluation documentation describes evaluation workflows for LLM applications. A tool can support measurement and inspection, but it does not replace explicit acceptance criteria or establish that a particular multi-agent system is correct.
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