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Why AI agents repeat work
Duplicate work is usually a coordination failure, not simply an agent-capability problem. If two workers receive overlapping instructions, neither can see what the other has claimed, or a handoff omits completed outputs, both may repeat the same steps. Concurrent agents can also produce conflicting changes when they act on shared state or external systems without a conflict-resolution plan.
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This is a practical synthesis of documented orchestration risks, not a measured ranking of causes. Microsoft’s architecture guidance discusses shared-state coordination limits and the need to preserve workflow state; OpenAI’s guidance distinguishes orchestration patterns and recommends narrow specialist roles.
Ownership is unclear
Every workflow needs someone accountable for the overall result and a clearly bounded owner for each branch. Without that distinction, workers may assume another agent is handling a task—or independently decide they should handle it.
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Progress is invisible or temporary
A successor needs to know what is complete, what remains, and where to find usable outputs. A long conversation transcript alone may not provide that efficiently. Treat this as a workflow-design recommendation: maintain a concise task record and pass only the context a worker needs.
Concurrent work collides
Parallel agents can be useful, but simultaneous changes to shared state or external systems create coordination risks. A ledger can show who claimed a task; it cannot by itself make conflicting writes safe or ensure every agent respects task boundaries.
Choose the right coordination pattern
Manager-led delegation, handoff, and parallel workers solve different problems. The key question is whether a specialist should return a bounded result to a primary agent, take control of a branch, or work independently alongside other workers.
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| Pattern | Who owns the overall result? | Best fit | Main coordination risk |
|---|---|---|---|
| Manager with specialist tools | The manager remains responsible. | A bounded subtask where a central agent must synthesize results or enforce guardrails. | The manager must track progress and supply relevant context. |
| Handoff | The receiving specialist takes control of the active branch. | A specialist should take over an entire branch of work. | Control transfer and context routing must be explicit. |
| Parallel workers | Depends on the aggregation or coordination design. | Independent tasks where throughput matters. | Shared-state collisions, conflicting results, and added resource use. |
OpenAI’s orchestration documentation says, “Start with one agent whenever you can.” That is guidance, not an experimental finding: add specialists when distinct instructions, tools, or policies make the split useful. OpenAI describes manager-style delegation and handoffs as different choices; Microsoft likewise distinguishes a handoff, which transfers control, from an agent-as-tools pattern, in which the primary agent retains responsibility.
Build a workflow that makes ownership visible
- Give every task a stable identity. Record a task ID, one accountable owner, a concise goal, dependencies, and a specific completion condition. Avoid assigning two agents the same open-ended objective.
- Check the shared record before work begins. Workers should inspect the task’s status and existing artifacts before starting. Record claims and completed outputs in a durable place the coordinator can inspect.
- Define the return or handoff contract. Specify what the worker must deliver, what remains unresolved, and where the result is recorded. Use a specialist-as-tool arrangement when the primary agent needs a bounded result and must remain responsible for the response; use handoff when the specialist should take over the branch.
- Separate independent work from dependent work. Run independent subtasks concurrently. Serialize steps that rely on earlier results. Split tasks only when specialization or capability isolation adds something concrete.
- Save checkpoints before gates and expensive stages. Persist the current state at mandatory decision points so a retry can resume from known progress rather than replay completed work. Microsoft’s guidance describes a manager-maintained task ledger updated as an incident evolves.
- Make shared changes deliberate. Establish how the application handles simultaneous edits, conflicting outputs, or external actions. A record of ownership is useful, but the appropriate locking, claim-expiration, or merge mechanism depends on the system.
Use research results as evidence, not a universal rule
Google Research’s 2026 summary reports a controlled evaluation of 180 agent configurations. In that evaluation, independent multi-agent systems amplified errors by 17.2×, compared with 4.4× for centralized systems. These are study-specific results, not expected production outcomes for every workflow.
The same summary shows why “centralized always wins” is too broad: multi-agent coordination was associated with an 81% gain on Finance-Agent and a 70% regression on PlanCraft. The summary also says a predictive model identified the optimal coordination strategy for 87% of unseen task configurations in its evaluation; that is not a general guarantee for new systems. The results support matching topology to task structure, not choosing an architecture by agent count alone.
For work with dependencies, sequential handoff can preserve order but limit parallel processing. For independent work, parallel execution may help. Microsoft also warns that orchestration adds model calls and that concurrency can raise resource use. Evaluate the trade-off on representative tasks rather than assuming more workers mean faster or better results.
Measure whether the change reduces duplication
Compare the workflow before and after changing it, using representative tasks. Track duplicate work, conflicting outputs or writes, replays after interruption, latency, and cost. OpenAI recommends monitoring and evaluating agent workflows; Microsoft notes the added calls and resource use that orchestration can introduce. Results depend on the task and implementation, so a ledger or topology change should be judged by observed outcomes.
Quick Recap
- Duplicate work: Did multiple agents perform the same task or recreate an existing artifact?
- Conflict rate: How often did workers produce incompatible results or attempt overlapping changes?
- Replay rate: After a retry or interruption, how often did the workflow repeat completed steps?
- Latency and cost: Did concurrency improve completion time enough to justify its added coordination and resource demands?
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