How to tell when a team of AI agents is failing together

A multi-agent AI system can fail even when each agent seems to be doing its job: information can be lost at hand-offs, shared state can drift, or agents can loop without making progress. A new VentureBeat article sets out a practical way to inspect those failures, by evaluating the workflow between agents rather than judging only the final answer.

Watch Desk analysis

What happened

The article argues that developers should trace the full workflow: which agents run and in what order, what they receive and return, how work is delegated, what changes in shared state, and how tools, retries and termination decisions behave. The trace should also record latency, token use and cost.

That makes it possible to check more than whether the system returned a result. Suggested measures include workflow completion and valid termination rates, hand-off quality, deadlocks, loops and abandoned runs. At each hand-off, teams can check whether relevant information reached the next agent, whether its meaning was preserved and whether the receiving agent’s response was supported by what it was given.

Key findings

• Trace the whole workflow
  Record agent order, inputs, outputs, hand-offs, shared-state changes, tool results and retries.
• Measure progress, not activity
  Track completion, iterations, deadlocks, loops and valid termination to spot workflows that are busy but stuck.
• Check hand-offs
  Test whether the next agent receives the relevant information and uses it faithfully.
• Validate shared state
  Check required fields, protected values, conflicting facts and stale information after each update.
• Look for shared blind spots
  Agents built on the same foundation model may reinforce a mistake rather than independently catch it.

Why it matters

A successful API response or polished final answer does not show where a multi-agent workflow went wrong, or whether it went wrong at all. Traces and hand-off checks give engineering teams evidence about the failure point, rather than leaving them to blame whichever agent happened to speak last.

This is also a useful distinction for anyone evaluating agent products: “the task completed” is not the same as “the agents collaborated reliably”. The article’s framework is a practical proposal, not evidence that any particular system has passed these checks.

Our read

The best idea here is also the least glamorous: inspect the joins. Multi-agent systems add more places for information to change, disappear or circulate indefinitely, so testing agents one by one is not enough. If you build or buy these systems, start by tracing hand-offs and shared state, then add measures that show whether work actually reaches a valid end. A dashboard that reports activity but not progress is just a very colourful way to be surprised.

What to watch

• Whether agent frameworks make full workflow traces and hand-off data easier to capture.
• Whether teams publish comparable measures for completion, loops and valid termination.
• Whether evaluations test independence between agents, rather than assuming another agent will catch the first one’s mistakes.

Discussion spark: For an AI workflow involving several agents, what should count as the minimum proof that they are collaborating reliably: full hand-off traces, independent checks, or successful end-to-end outcomes?

Watch Desk is operated by WittyWires as an independent cross-cutting AI news tracker. It does not speak for the organisations or people it covers.