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To keep AI agents from repeating work or overwriting one another, give each task a unique ID and one owner, make task status and progress visible, limit what each agent can change, and require a supervisor to verify and reconcile results before committing them. Use isolated contexts for independent work and share only the context needed when agents depend on one another. Coordination is a control problem: the system needs rules for assigning work, sharing progress, handling changes, and deciding when the combined result is ready.
Why do AI agents duplicate work or conflict?
Multiple agents can work in parallel, but parallelism alone does not coordinate them. If two agents cannot see who owns a task, both may do it. If they can edit the same state without a version check, one may silently erase the other’s update. And if their results reach a shared workflow without review, incompatible assumptions can become a single, unreliable answer.
Anthropic describes multi-agent orchestration as a coordinator directing agents that work in isolated contexts, then receiving and combining their results. That makes coordination more than task assignment: the orchestrator must decide what to delegate, what information to provide, how to collect the outputs, and how to verify the finished work.
The IEEE survey of multi-agent tool-sharing hazards identifies four useful failure modes to design against: lost updates, dirty reads, duplicated side effects, and divergent shared state. These can occur in systems that use different frameworks or models; they are consequences of weak coordination rules, not evidence that one particular agent topology always fails.
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What coordination rules prevent duplicate work?
Make ownership, status, and progress explicit before agents begin. A task should have one current owner, a unique identifier, and a visible state that changes as work proceeds.
Assign a specific, testable task
Decompose the goal by deliverable or question, not by vague role. “Find evidence for the two disputed claims and return the sources and uncertainties” is easier to assign and verify than “research the topic.” Each task needs one owner, bounded inputs, a defined output format, and a completion test. Record dependencies so an agent does not begin work that depends on an unfinished result.
Require a claim before work starts
Use a task queue in which work moves through three visible states: open, claimed, and done. An agent must claim an open task before acting on it. DeLM uses this ownership-and-status approach; its purpose is to make clear both what is being worked on and who is responsible.
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Keep a compact, typed progress log
Use an append-only log rather than relying on agents to reconstruct progress from long conversation histories. DeLM’s example uses three entry types:
- FACT: an evidence-backed finding, with a reference to the relevant file, query, test, or citation.
- FAIL: an approach that was tried and disproven, so another agent need not repeat it.
- DONE: a completed artifact or task, with enough information to locate and review the output.
Keep entries short and durable. Copying whole conversations into shared state makes it harder to find the current facts and decisions. The log should show what changed and where supporting material lives, not become a second transcript.
What should a shared task record contain?
A task record should tell an agent what it may do, what it must return, and how another agent can decide whether the work is complete. A practical record can use fields like these:
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task_id: research-014
description: Verify the two claims about the release behavior
owner: agent-2
status: claimed
inputs: product documentation; issue 218
output_schema: claim, evidence, source, uncertainty
dependencies: research-009
write_scope: research-014 result only
evidence_required: source reference for each claim
expires_at: 2026-10-26T15:00:00ZUse unique IDs for tasks and for important shared resources, such as files, records, or external actions. The description above is an illustrative record, not a universal schema; adapt the fields to the queue and work being coordinated. In particular, write_scope should state what an agent is allowed to change, and evidence_required should make the completion check meaningful.
When ownership changes, record the claim, expiry, and reclaim in the shared state. If an agent’s lease expires, preserve any artifact it produced, but do not silently treat it as current. A reviewer can decide whether to accept it, resume the task, or ask the new owner to redo the work.
Should agents share context or work in isolation?
Neither full isolation nor unrestricted sharing is always right. Akka recommends choosing context boundaries based on the task: isolate agents when independent reasoning is useful, and selectively share context when their work is interdependent.
Use isolated contexts for independent work
When agents investigate separate questions or produce alternative solutions, give each a bounded input set and let it reason independently. This can reduce the chance that an early answer anchors every later agent. The trade-off is that agents may miss relevant discoveries made elsewhere, so the coordinator must combine their results and check whether they relied on incompatible assumptions.
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Share interfaces and necessary facts for dependent work
When one task depends on another, provide the relevant shared facts, decisions, and interfaces—not an assumption that every agent automatically sees every private message. Akka’s guidance distinguishes coordination through shared context from merely running agents in the same session. Explicitly say which findings are authoritative, which are provisional, and where the latest artifact can be found.
For example, an agent that implements a component may need the approved interface and the reviewer’s accepted requirements, but not every exploratory discussion that preceded them. If a later change affects that interface, publish the change in the shared log and identify dependent tasks that need review.
How should you prevent agents from overwriting work or repeating side effects?
Give every agent a bounded write scope and make shared-state changes reviewable. Prefer append-only events, immutable artifacts, or versioned records; when mutable state must be updated, require a compare-and-swap operation or equivalent version check before committing.
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Protect shared state with versions
Read the version an artifact had when the agent began editing, then require that version to remain current at commit time. If someone else has changed it, reject the stale write and reconcile the two candidate versions. This prevents a late update from silently replacing newer work. Use a lock or lease only for the smallest critical section that truly needs exclusive access; broad locks can block otherwise independent work.
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Retries are not a guarantee of exactly-once effects. A timeout may happen after an external action succeeded but before the agent received confirmation, leaving it uncertain whether to try again. Where a tool or service supports it, attach a stable request key and deduplicate repeated requests at the tool boundary. Record the action and its outcome so a retry can check whether the intended effect already occurred.
Test for the IEEE survey’s failure modes: whether writes get lost, whether an agent reads uncommitted or stale data, whether retries repeat side effects, and whether agents develop different views of shared state. Versioning and idempotency do not replace review, but they make mistakes detectable and recovery more controlled.
Which coordination pattern should you use?
Choose the pattern based on task dependencies and how much agents need to influence one another. No topology is universally best; each exchanges one kind of risk for another.
| Situation | Pattern | Why it fits | Main risk |
|---|---|---|---|
| Known dependency chain | Sequential workflow | Clear order and recovery points | An early mistake can propagate |
| Independent subtasks | Delegative parallelism | Parallel speed with isolated reasoning | Poor decomposition or weak synthesis |
| Interdependent review | Collaborative team | Agents can critique and refine one another’s work | Early convergence and coordination overhead |
| Broad exploration with shared artifacts | Emergent or shared-state coordination | Many contributions can accumulate | Unpredictable selection and conflicts |
Akka’s documentation describes coordination as dividing work and defining how agents share context, with a supervisor applying the pattern and deciding when the combined work is complete. That framing is useful whichever pattern you choose: the design still needs a completion rule and a way to handle disagreement.
Is centralized or decentralized coordination better?
A centralized supervisor is easier to audit, bound, and reproduce. A decentralized system lets agents publish progress directly to peers, which can be useful when work is numerous and updates are incremental. The choice depends on whether your priority is controlled integration or direct peer coordination—and whether your shared-state rules are strong enough for the latter.
Central supervisor
In a centralized setup, a supervisor delegates tasks, receives results, and controls synthesis and completion. Anthropic documents subagents and dynamic workflows, including isolated contexts and programmatic result combination. The supervisor can enforce task limits and acceptance checks, but relaying work through one point can add delay and create an integration bottleneck.
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Decentralized shared queue and context
DeLM describes agents asynchronously claiming tasks, publishing updates, and correcting one another through a shared queue and context. That can suit a large set of incremental tasks, but it depends on reliable ownership, state consistency, conflict rules, and criteria for choosing among competing results. Decentralization does not remove the need for a final gate when commitments have real consequences.
For either design, assign a supervisor or designated reviewer responsibility for the final decision. The supervisor does not need to perform every task; it needs to ensure that claims are supported, overlaps are resolved, and the accepted state passes the agreed checks.
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Do not merge conflicting outputs just because one arrived first or sounds more confident. Pause the affected commit, preserve both candidates, compare their evidence and scope, and validate the chosen or merged result before publishing it.
- Detect overlap. Compare task IDs, resource IDs, and artifact versions to find competing work or stale writes.
- Stop conflicting commits. Keep both candidate artifacts intact while the disagreement is reviewed.
- Compare the claims. Check evidence quality, assumptions, scope, and freshness. A claim outside the task’s stated scope is not a resolution to the original question.
- Assign a decision-maker. Have the supervisor or designated reviewer choose a result, merge compatible parts, or request rework.
- Re-run acceptance checks. Test the reconciled artifact against the original completion criteria, including dependent tasks where relevant.
- Record the decision. Add the resolution and rationale to the shared log so another agent does not recreate the same conflict.
If two agents changed the same object, stop integration long enough to rebase or reconcile their work, then repeat validation. Do not let fluent wording substitute for evidence, and do not discard a candidate until the review has established why it is being rejected.
What should the supervisor verify before accepting results?
Ask every agent to return its assumptions, uncertainty, evidence, and changed artifacts along with its conclusion. The supervisor should compare overlapping claims, resolve contradictions by evidence quality, and run the task’s acceptance checks before updating shared state.
- Coverage: Does the output answer the assigned question and satisfy its stated schema?
- Traceability: Can each material claim be tied to evidence or an artifact another reviewer can inspect?
- Consistency: Do related outputs agree on key facts, definitions, versions, and scope?
- Freshness: Was the result produced from current inputs, or has a changed dependency made it stale?
- Integrity: Are the referenced artifacts the ones actually reviewed, with no unapproved shared-state changes?
- Completion: Have acceptance tests passed, and are remaining uncertainties or failures recorded?
Akka places sequencing, retries, and completion decisions with the supervisor. That makes the final verification boundary a natural place to reject contradictions rather than passing them downstream as settled facts.
How do you keep a multi-agent workflow from running indefinitely?
Bound execution before work begins. Set maximum iterations, deadlines, token and tool budgets, and escalation rules for blocked, expired, or ambiguous tasks. Without these limits, a supervisor may keep delegating in response to uncertainty without improving the result.
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More agents also mean more calls, latency, context management, and failure paths. Akka notes that a model-driven supervisor is less predictable than a fixed sequence. Prefer a fixed workflow when dependencies and checks are known; allow dynamic delegation when the task genuinely requires discovery, but cap the extra exploration and specify when unresolved issues go to a human reviewer.
Define a stop condition the system can check. It might require all assigned tasks to be done, dependencies to be current, acceptance checks to pass, and no unresolved critical contradiction. If a required check fails, the next action should be explicit—retry within a limit, reopen a task, or escalate—rather than an open-ended request to “keep improving.”
How can you tell whether coordination is working?
Track both coordination failures and the cost of preventing them. Compare the multi-agent workflow with a single-agent baseline on representative tasks; speed alone is not useful if the system creates more rework or commits unsupported results.
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- Contradiction rate and unresolved-claim rate
- Stale-result rate and rework after integration
- Latency, token cost, tool cost, and retry count
- Side-effect deduplication count and recovery success
- Final acceptance-test pass rate
Interpret these metrics together. For example, a low duplicate rate does not show that agents are producing correct work, while a high deduplication count may indicate that idempotency is catching retries—or that the workflow is retrying too often. Use the measures to find where task definition, ownership, context, or verification needs adjustment.
There is no established cross-framework statistic for a universal reduction in duplicate work, ideal agent count, or universally superior coordination topology. Stanford University’s DeLM project reported up to 17.5 points more accuracy than its strongest baseline and up to 2.49× faster than the harness it builds on, on the project’s selected long-horizon benchmarks. Those are project-reported, setup-specific results, not a guarantee that a given coordination design will achieve the same outcome. On selected Terminal-Bench 4.0 tasks, the project reported an average Codex latency of 26.91 minutes for its baseline task subset; that figure describes that benchmark subset, not a general workflow target.
Frequently Asked Questions
How many AI agents should I use for one task?
There is no established universal best agent count. Add agents only when the work can be divided into useful, bounded tasks and the expected benefit outweighs the extra coordination, latency, and verification effort.
What should I do if an agent stops responding after claiming a task?
Let its claim expire under the queue’s lease rule, then record a reclaim or escalation rather than quietly starting duplicate work. Preserve any artifact it produced and have the new owner or reviewer decide whether it is current before relying on it.
Can AI agents safely make changes to the same file?
They can propose changes to the same file, but concurrent commits need a version check and a reconciliation step. If the system cannot detect stale writes, assign one writer and have other agents submit reviewable patches or recommendations.
Why do agent retries sometimes create duplicate actions?
A tool request can succeed even if the agent never receives its confirmation, so the agent may retry without knowing the first action happened. A stable request key and deduplication at the tool boundary can prevent repeated effects when the tool supports them.
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