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Coordinate multiple AI agents with explicit task contracts, unique task IDs, atomic ownership claims, visible statuses, a compact shared progress log, limited write permissions, and a supervisor-controlled verification gate. Isolate agents when they are solving independent problems; share only the context and interfaces needed when their work depends on one another. Treat every external action as retryable: use idempotency keys, version checks, and recovery rules so a timeout cannot create a second charge, deployment, or record.
This approach turns a group of language models into a controlled workflow rather than a set of competing chat sessions. It also gives you a way to detect overlap, stop conflicting commits, decide which evidence is stronger, and measure whether parallel execution actually beats a single agent.
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Multi-agent orchestration is not simply asking more models the same question. One coordinator must decide what to delegate, what each worker may see and change, how results return, and when the combined work is complete. Anthropic describes this as agents working in isolated contexts under a coordinating process; the coordinator supplies the decomposition and combines the outputs.
Without those controls, several predictable failures appear:
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- Duplicate work: two agents investigate or edit the same item because neither can see that the other has started.
- Lost updates: a later write replaces a valid earlier change without checking the version it read.
- Dirty reads: an agent bases a decision on a half-written artifact or an unverified claim.
- Duplicated side effects: a retry sends the same message, creates a second ticket, or runs a deployment twice.
- Divergent shared state: agents cache different versions of a plan and continue from incompatible assumptions.
- Unresolved contradictions: a fluent answer is accepted because nobody is assigned to compare evidence and reject inconsistencies.
The IEEE survey of multi-agent tool sharing identifies these hazards as system-design problems, not merely model-quality problems. Better prompts can improve individual work, but only a protocol can control ownership, state, and commitment.
How should I define and split the work before starting agents?
Write the final outcome, constraints, acceptance tests, and stop condition before spawning workers. Decompose by a concrete artifact or question, not by vague roles such as “researcher” or “helper.” Each subtask should have one owner, a bounded input set, an output format, and a test that determines whether it is complete.
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State what the finished system must deliver, what it must not change, and how a reviewer will verify it. For example, “produce a migration plan that preserves all customer IDs, includes rollback steps, and passes the schema-diff check” is testable; “analyze the database” is not.
Choose boundaries that minimize overlap
Split work along stable boundaries such as files, API endpoints, customer segments, benchmark cases, or decision questions. Give one agent responsibility for each boundary. If two perspectives are valuable, assign separate read-only analyses and reserve the final artifact for a synthesizer rather than letting both agents edit it.
Declare dependencies and stop conditions
Record which tasks must finish first and what evidence unlocks the next task. A task can stop when its output schema is filled, required tests pass, and no unresolved assumption remains. A deadline or iteration limit prevents an agent from continuing to search after the decision has already become adequate.
What should a task contract contain?
A task contract makes ownership and completion machine-checkable. A practical record contains the following fields:
| Field | Purpose | Example |
|---|---|---|
task_id |
Stable identifier used in claims, logs, and references | schema-migration-042 |
description |
One bounded question or artifact to produce | Compare old and new customer tables |
owner |
Agent or service currently responsible | agent-3 |
status |
Lifecycle state visible to every participant | open, claimed, or done |
inputs |
Files, records, prompts, or upstream task IDs permitted as input | snapshot:v18 |
output_schema |
Required shape and fields of the result | JSON with findings, evidence, risks |
dependencies |
Tasks that must be complete or approved first | schema-inventory-041 |
write_scope |
Objects the agent may create or modify | Report section only; no production writes |
evidence_required |
Sources, tests, or observations needed for acceptance | Schema diff and row-count check |
expires_at |
Lease deadline after which another worker may reclaim the task | ISO-8601 timestamp |
Keep the contract separate from the agent’s private conversation. The coordinator can validate the record, route results, and audit changes without storing every intermediate thought.
How do agents claim work without doing the same task?
Use a queue in which an agent atomically changes a task from open to claimed and records its identity. The claim operation must fail if another agent has already changed the state. A database transaction, compare-and-swap operation, or lease service can provide that guarantee.
- Read an eligible task whose dependencies and inputs are ready.
- Attempt an atomic update that matches
status = openand assignsowner,claimed_at, andexpires_at. - Proceed only when the update reports success. A zero-row update means another worker won the claim.
- Renew the lease while work continues, or mark the task
donewith its output and evidence before expiry. - If the lease expires, mark the previous attempt
staleand allow a new claim. Do not silently treat the old result as current.
The Stanford DeLM project uses this open/claimed/done model so agents can asynchronously take available work and see who owns it. A lease handles crashes, but it does not prove that an abandoned worker stopped writing; enforce the write scope and version checks as a second line of defense.
What belongs in a shared progress log?
Use an append-only log with short, typed entries rather than copying complete conversations into shared state. A compact log lets every agent see what has been established, what failed, and which artifacts are complete without consuming its entire context window.
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FACT entries
Record an evidence-backed observation, its scope, and a reference to the file, query, test, or citation that supports it. Include the version or timestamp when the fact can change.
FAIL entries
Record a disproven approach and the condition that ruled it out. “Parser A failed on nested arrays in fixture set v4” is useful; “this did not work” is not.
DONE entries
Record the completed artifact, validation result, and any remaining follow-up. A done entry should point to an immutable output or versioned record.
The DeLM project describes this task queue and shared context as a way for agents to see current work and failed approaches, reducing the chance that a peer repeats them. Keep entries small, typed, and append-only; store large documents separately and reference their versions.
When should agents share context, and when should they be isolated?
Isolation is best for independent reasoning or alternative solutions; selective sharing is necessary when one agent must react to another’s intermediate result. Akka’s guidance treats context boundaries as a design choice: a supervisor can give each worker a private context, then merge typed outputs, or expose a narrow shared interface for interdependent tasks.
Use isolated contexts for parallel alternatives
Give each agent the same frozen inputs and ask for an independent analysis when you want to reduce correlated mistakes, compare designs, or obtain a second opinion. Return structured results containing assumptions, evidence, uncertainty, and proposed changes. The agents should not edit one another’s working notes.
Use selective sharing for a dependency chain
Publish only the facts and interfaces the next step needs. For example, a schema agent can expose table names and constraints to a query-planning agent without exposing unrelated customer data or private deliberations. Version the shared snapshot so a downstream worker knows exactly which state it read.
Do not assume a shared session is universal
Frameworks differ in whether agents see private messages, tool outputs, or context updates. Explicitly pass the approved facts and artifact IDs; never rely on an implicit assumption that every worker has seen every conversation.
How can I prevent agents from overwriting each other?
Give every agent a declared write scope and require a version check before committing mutable state. Prefer append-only events, immutable artifacts, or new versions over in-place edits. When in-place mutation is unavoidable, use compare-and-swap (CAS) or an equivalent conditional update.
Use versioned writes
An agent reads object version 18 and submits its change with “apply only if version is still 18.” If another agent has committed version 19, the write fails and the coordinator can rebase, merge, or request a fresh analysis. Never auto-resolve a failed version check by overwriting the newer object.
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Make external actions idempotent
Attach a unique request key such as task_id + operation + input_version to every side effect. The receiving service stores completed keys and returns the original result for a retry instead of executing the operation again. This protects against duplicated invoices, notifications, records, and deployments when a response is lost.
Keep critical sections small
Locks or leases are appropriate around the smallest commit operation, not around an agent’s entire reasoning process. Long-held locks increase latency and make a crashed worker block everyone else. Perform analysis outside the lock, then validate and commit quickly.
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Let workers write candidate artifacts to their own namespace. Only the supervisor or an explicitly authorized merger may update the shared, consequential object. This preserves both alternatives when a conflict appears and prevents a premature answer from becoming system state.
Which coordination pattern fits my workload?
Choose the topology based on dependencies, update frequency, and the cost of a wrong commitment. The following patterns cover the common cases:
| Situation | Recommended pattern | Why it fits | Main risk |
|---|---|---|---|
| Known dependency chain | Sequential workflow | Each stage has a clear input, output, and recovery point | An early mistake propagates downstream |
| Independent subtasks | Delegative parallelism | Workers run in isolated contexts and reduce elapsed time | Poor decomposition or weak synthesis creates inconsistent outputs |
| Interdependent review | Collaborative team | Agents can critique and refine one another through controlled updates | Early convergence and coordination overhead |
| Broad exploration with shared artifacts | Emergent/shared state | Many incremental contributions accumulate in a common workspace | Unpredictable selection and more state conflicts |
Akka defines the core idea this way: “Multi-agent coordination divides work among agents and defines how they share context. A supervisor applies a coordination pattern and decides when the combined work is complete.” Treat that supervisor decision as a required completion gate, not an optional summary.
Should coordination be centralized or decentralized?
A centralized supervisor is easier to audit, bound, and reproduce. It can assign tasks, enforce schemas, serialize high-risk commits, and run one final acceptance check. Anthropic documents dynamic workflows in which subagents work in isolated contexts and a program combines their results; the trade-off is relay delay and a possible integration bottleneck.
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A decentralized queue lets agents see progress directly and claim work asynchronously. The DeLM design combines a shared queue with shared context so agents can publish updates and correct one another. This can suit large streams of incremental tasks, but it requires stronger consistency rules, conflict detection, and selection criteria.
Use decentralization when tasks are numerous, updates are frequent, and the shared-state protocol is mature. Keep a supervisor or final gate for decisions with financial, security, legal, or production consequences. A hybrid is common: decentralized discovery and execution, centralized approval of the committed result.
How should a supervisor synthesize and verify results?
The supervisor should combine typed outputs, compare overlapping claims, evaluate evidence quality, and reject contradictions before changing shared state. Do not let the first fluent response win by default.
- Validate each output against its declared schema, task scope, and input version.
- Group claims that address the same object, requirement, or decision.
- Compare evidence, assumptions, geographic or version scope, and freshness for each competing claim.
- Choose, merge, or reject candidates using an explicit decision rule; request rework when evidence is insufficient.
- Run the acceptance tests against the proposed combined artifact.
- Commit a new immutable version and publish the decision, rationale, and rejected alternatives in the progress log.
For high-impact work, have a separate reviewer agent or human perform the final check. The supervisor owns sequencing, retries, and completion; workers should not be allowed to declare the global task complete merely because their local task passed.
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What is a reliable protocol for resolving conflicting agent answers?
Stop conflicting commits first, preserve both candidates, and resolve the disagreement using evidence and scope rather than confidence or writing style.
- Detect overlap. Compare task IDs, resource IDs, artifact versions, and requested write scopes. A conflict exists when two candidates modify the same object or make incompatible claims about it.
- Freeze the commit. Prevent either candidate from replacing the shared version. Store both artifacts with their input versions and timestamps.
- Compare the basis. Check source quality, test results, assumptions, applicability, and freshness. A result for one software version or region must not silently override a result for another.
- Assign a decider. A supervisor or designated reviewer chooses, merges, or sends the task back for targeted rework. The decider should state why one evidence set outranks the other.
- Revalidate. Run the acceptance tests on the merged state, including regression checks for unaffected objects.
- Publish the rationale. Add the decision and its scope to the append-only log so future agents do not recreate the same dispute.
How do I bound cost, latency, and runaway execution?
Set limits before execution: maximum iterations per task, a wall-clock deadline, token and tool budgets, retry counts, and escalation conditions. Stop a worker when it has produced the required schema and passed the completion test, even if it could continue generating alternatives.
More agents add model calls, context management, latency, and failure paths. Akka notes that a model-driven supervisor is less predictable than a fixed sequence, so use deterministic orchestration for known workflows and reserve dynamic delegation for work whose uncertainty justifies it. Route a task to a human or a higher-capability reviewer when confidence remains low after the allowed attempts.
Which metrics show whether coordination is working?
Compare the multi-agent system with a single-agent baseline on representative tasks. Track these measures over the same workload:
- Duplicate-task rate: fraction of tasks performed by more than one worker without an intentional second opinion.
- Double-claim percentage: claims that two workers believed they owned.
- Contradiction rate: incompatible claims detected before commitment.
- Stale-result rate: outputs based on an expired lease or superseded input version.
- Integration rework: work redone after synthesis or review.
- Unresolved-claim count: tasks left without an owner or final decision.
- Latency: elapsed time from ready state to accepted artifact.
- Token and tool cost: usage per accepted outcome, not merely per agent run.
- Retry and deduplication counts: failed attempts and side effects safely collapsed by request keys.
- Recovery success: percentage of crashed or expired tasks completed without corrupting shared state.
- Acceptance-test pass rate: final quality measure against the original contract.
There is no independent, cross-framework statistic proving a universal duplicate-work reduction or a universally best agent count. Performance depends on the task, model, orchestration implementation, and evaluation set.
What do current benchmark figures actually prove?
The Stanford University DeLM project reported, in 2026, results up to 17.5 percentage points more accurate than its strongest baseline and up to 2.49 times faster than the harness it builds on, using the project’s selected long-horizon benchmarks. Those are project-reported, setup-specific figures, not guarantees for every workflow.
On its selected Terminal-Bench 4.0 task subset, the same project reported an average Codex latency of 26.91 minutes for the baseline subset. That number describes the stated benchmark setup and should not be treated as a general prediction for your agents, tools, or network.
What should I do when coordination fails in production?
Two agents keep claiming one task
Inspect the claim transaction and confirm it tests the prior status atomically. Add a unique constraint or conditional update on the task state, then record rejected claims for monitoring.
A worker finishes after its lease expired
Mark its result stale and prevent it from writing to the current artifact. Have the supervisor compare it with the replacement result; retain it as evidence only if its input version is still relevant.
One agent reads changes another has not committed
Publish only committed snapshots to downstream workers. Include the snapshot ID in every task input and reject results that cite an unavailable or superseded version.
A retry created a duplicate side effect
Move deduplication to the tool boundary, where the request key is checked before execution. Reconcile any existing duplicates, then require the key on all future retries.
The synthesizer chooses a polished but weak answer
Require evidence fields and acceptance tests in the output schema. Add an independent reviewer or deterministic rule for tie-breaking, and log why the selected claim outranked alternatives.
Agents converge too early on the same mistake
Run independent analyses with isolated contexts and deliberately different prompts or assumptions. Merge only after each result includes its own evidence and uncertainty; do not expose one candidate’s conclusion before the others have finished.
What is a practical end-to-end implementation?
A small implementation can follow this sequence without adopting a particular framework:
- Create the outcome contract, acceptance tests, task records, and write scopes.
- Insert tasks as
openwith immutable input snapshot IDs. - Let workers claim tasks through an atomic status transition and lease.
- Have workers write candidate artifacts and append FACT, FAIL, or DONE entries; prohibit direct edits to the final object.
- Renew leases only while progress is genuine, and mark expired attempts stale.
- Send typed outputs to a supervisor that checks scope, versions, evidence, and schema.
- Run the conflict protocol whenever resource IDs or claims overlap.
- Commit the merged artifact with CAS against the version the supervisor reviewed.
- Execute acceptance tests and publish the decision and rationale.
- Export metrics and compare them with a single-agent baseline before increasing parallelism.
This arrangement keeps discovery parallel while making commitment deliberate. It also leaves an audit trail that explains who owned each task, which state they read, why a result was accepted, and how a failure was recovered.
Frequently Asked Questions
How many AI agents should I run for one project?
There is no fixed optimum. Start with the smallest number that can cover genuinely independent subtasks, then increase parallelism only when latency falls without raising contradiction, rework, or side-effect errors.
Can a shared document replace a task queue?
A document can hold notes, but it usually lacks atomic claiming, leases, and machine-checkable status transitions. Use a queue or database for ownership and keep the document as a versioned artifact or human-readable view.
How do I protect sensitive data when agents collaborate?
Give each task the minimum fields and tools needed, and pass references to restricted records instead of copying raw data into shared context. Log access decisions and enforce permissions at the tool boundary, not only in prompts.
What happens if the supervisor crashes during synthesis?
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Is a human required to resolve every agent disagreement?
No. Deterministic rules can settle conflicts when evidence, version scope, and tests clearly favor one result. Escalate only disagreements that remain material after those checks or that affect high-consequence actions.
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