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Safe multi-agent systems use three separate controls: least-privilege access for every agent, an explicit budget for the complete run, and a ceiling on simultaneously executing work. Configure all three before enabling delegation. A list of 1,000 defined agents is not the same as 1,000 running agents; a tool restriction is not a data-loss-prevention filter; and a vendor quota may apply to a call, run, session, credential, or account. Confirm the unit and the behavior at the limit before treating any number as a safeguard.
The three control layers
Multi-agent workflows multiply exposure. A coordinator can fan out to several specialists, each specialist can call tools, and retries or loops can continue after the original request appears complete. The practical control model is:
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- Access: each agent receives only the tools, servers, data sources, and credentials required for its role.
- Spend: the orchestrator records cumulative model and tool usage and stops or requests approval at a defined threshold.
- Parallelism: the workflow limits simultaneous branches and prevents unplanned fan-out.
These controls are related but not interchangeable. A concurrency limit can reduce cost without preventing an agent from using an over-privileged credential. A tool allowlist can reduce risk without stopping an expensive loop. A provider’s managed-agent count can describe configuration capacity rather than active execution.
Apply the controls in that order when designing a new workflow: define the job boundary, identify the minimum capabilities, set the spending envelope, and then decide how much independent work can run at once. This avoids using a cheap but unsafe shortcut, such as granting broad access because a worker is temporary or raising concurrency simply to meet a deadline.
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Start with an agent-to-capability matrix
Before writing delegation code, document what each role may access. Treat the matrix as a deny-by-default policy, not as an inventory of everything available in the environment.
| Agent role | Tools and data it needs | Capabilities to exclude | Credential design |
|---|---|---|---|
| Researcher | Search or retrieval tools and approved document stores | Repository writes, messaging, billing, deletion | Read-only identity with narrow scopes |
| Code reviewer | Read access to selected repositories and test results | Merge, deploy, secret administration | Repository-specific, short-lived token where supported |
| Editor | Draft storage and formatting tools | Publishing, payment, account changes | Workspace identity limited to draft locations |
| Operator | Only the production actions explicitly required | Unrelated systems and destructive operations | Separate identity, approval required for writes |
For every row, record the server or connector name, allowed operations, data scope, credential owner, expiration, and human approver. Revisit the matrix when a prompt, tool, or downstream API changes. A child thread may inherit a session credential whose permissions are broader than the agent-specific tool list, so inspect the effective identity rather than trusting the visible configuration alone.
Make the matrix operational by assigning an owner for each permission. Specify who approves a new scope, how quickly a token is revoked, and which log proves that revocation occurred. Include temporary exceptions with an expiry time; otherwise a one-time troubleshooting grant can become a permanent capability.
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Anthropic’s managed-agent documentation defines tools and MCP servers per agent. That lets a researcher receive a GitHub MCP server while another role receives none; do not declare every server on the coordinator and assume prompts will enforce separation. Google recommends trusted tools, least-privilege credentials, short-lived tokens where available, and only the scopes you are willing to grant. Google also warns that an agent can use any credential available to it, making credential placement as important as the tool list.
Inspect indirect access as well. A retrieval connector may expose a broader folder than the role needs, and a shell or generic HTTP tool may bypass a carefully selected connector. Restrict hostnames, repository paths, methods, and file locations where the platform permits it. Keep secrets out of prompts and tool results, and redact sensitive fields in logs.
Separate read paths from write paths
Create distinct read and write tools even when they reach the same service. A read tool should not accept an arbitrary method name that can be changed into a write. Use separate service identities, repositories, buckets, or API scopes when possible. Keep destructive operations—deletes, permission changes, purchases, deployments, sends, and publication—out of general-purpose agents unless the workflow genuinely requires them.
For a permitted write, constrain the target as well as the verb. An editor might update a draft record but not change its owner; an operator might deploy one service but not alter account permissions. Validate parameters in the orchestration layer rather than relying on the model to follow a natural-language rule.
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Put a human checkpoint immediately before an irreversible or externally visible action. The approval request should show the target, proposed operation, material parameters, and the evidence or draft that produced it. Do not ask a reviewer to approve an opaque “run tools” step.
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ChatGPT Workspace Agents documents app write actions as defaulting to Always ask, with app-dependent custom or Never ask settings. Use an automatic setting only for genuinely reversible, low-impact actions. Connector Action Constraints can narrow which actions an agent may request, but they do not filter the data returned by a permitted connector action. Therefore, an action constraint is not a data-loss-prevention control: sensitive results still need access controls, redaction, and retention rules.
A practical approval sequence
- Let the agent prepare a proposed change in a non-production or draft location.
- Validate the target, scope, and diff in the orchestrator.
- Present a human-readable approval card with the exact write operation.
- Issue a short-lived or single-use authorization after approval, if the platform supports it.
- Execute once, record the result, and revoke or expire the authorization.
Test denied approvals as well as accepted ones. A safe system should cancel the pending branch, not silently retry with another credential or ask a different agent to perform the same write.
Approval is a control on execution, not on the correctness of the proposal. Show enough context for a reviewer to detect a wrong account, oversized recipient list, unexpected price, or destructive parameter. For high-impact actions, require two-person approval or a second automated policy check, and make the approval token bind to the exact operation so it cannot be reused for a changed request.
Bound concurrency and fan-out
Choose a ceiling for simultaneously running work, then enforce it at the coordinator and, where possible, at the worker pool. Set a lower limit than the platform maximum when downstream rate limits, reviewer capacity, or budget make that prudent. Also cap total branches per run and recursion depth; otherwise a small concurrency window can still process an unbounded queue.
Provider limits are different units
| Platform or framework | Documented parallelism or count | What the number means | Important qualification |
|---|---|---|---|
| OpenAI Agents API multi-agent sessions | max_concurrent_subagents default of 6 |
Concurrent subagents, excluding the coordinator | Delegation can be disabled; the documentation reviewed does not establish a spending cap or permission-scoping feature. |
| Anthropic Managed Agents | Up to 25 concurrent threads | Simultaneously running threads | The coordinator may call multiple copies of a roster agent; treat 25 as a platform maximum, not a recommended workload size. |
| Google Gemini Managed Agents | Up to 1,000 managed agents | Defined managed-agent capacity | This is not evidence that 1,000 agents can run concurrently. |
Google ADK ParallelAgent |
No numeric cap established in the cited material | Concurrent branches for the listed subagents | The builder selects the parallel set; branches do not automatically share history or state, and result order may be nondeterministic. |
| ChatGPT Workspace Agents | Not established by the cited page | Not stated | Do not infer a multi-agent execution cap from its write-approval controls. |
Verify whether a vendor’s limit is per API request, orchestration run, session, credential, tenant, or account. Ask what happens when it is reached: queueing, rejection, cancellation, pause, or partial completion. Record that behavior in your runbook.
Do not compare these figures as if they measured the same resource. OpenAI’s six is a default active-subagent setting excluding the coordinator. Anthropic’s 25 is a documented concurrent-thread maximum. Google’s 1,000 is a managed-agent count. A framework’s branch count can be entirely application-controlled. Keep separate dashboards for each quantity.
Parallelize only independent work
Good candidates include separate document reviews, independent retrievals, and isolated test suites. Do not parallelize branches that update the same record, rely on one another’s output, or compete for a non-transactional resource unless you add explicit coordination.
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Google ADK’s ParallelAgent starts its listed subagents concurrently in separate branches. Conversation history and state are not automatically shared, and results may arrive in nondeterministic order. Pass immutable inputs to each branch, write outputs to uniquely identified locations, and use a parent aggregation step that validates every result. If branches need shared state, design a store with locking or transactions rather than assuming implicit context sharing. Google notes that graph-based workflows supersede templated workflows starting with ADK 2.0 for Python and Go; check the current framework guidance when choosing a new orchestration style.
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When output order matters, attach sequence numbers or task IDs and sort during aggregation. When one branch’s result is a prerequisite for another, use a staged workflow instead of placing both in the same parallel set. A little less parallelism is usually safer than compensating for races with ad hoc retries.
Implement a bounded worker pattern
- Compute the set of eligible tasks and reject tasks beyond the run’s maximum fan-out.
- Place tasks in a queue with a run identifier and idempotency key.
- Allow no more than the configured worker count to claim tasks at once.
- Apply per-task timeouts and retry limits; retries must consume the same run budget.
- Release the slot on success, failure, cancellation, or timeout, and record the terminal state.
- Have the coordinator aggregate only completed, validated outputs.
Keep a separate count for configured agents, queued tasks, active threads, completed tasks, and retries. Mixing these counters is a common source of accidental fan-out.
Set a deadline for the entire run as well as each task. Otherwise a queue can remain active indefinitely as workers finish and replace one another. If a worker dies, make its lease expire and ensure that reprocessing the task is safe.
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Control cumulative spending
Fan-out multiplies model calls and tool use, while loops and retries can multiply them again. Track usage for the whole run, not only the coordinator’s first request.
What the provider evidence establishes
- Google describes managed-agent usage as pay-as-you-go, based on model tokens and tool usage. Its Gemini Agents overview says a typical single interaction may consume 100,000 to 3 million tokens. This is Google’s broad estimate, not a price, invoice prediction, or cross-platform average.
- An Anthropic search result describes a session budget that pauses runs when reached, but that behavior was not visible in the opened orchestration page. Verify the current API, scope, pause semantics, and resume behavior before depending on it.
- The OpenAI multi-agent documentation reviewed for this article does not document a spending cap. Do not infer one from its concurrency setting.
- The cited Google ADK and Workspace Agents material does not establish a spend cap.
Add an orchestration-side budget
Regardless of vendor features, maintain a run ledger containing run ID, parent and child agent IDs, model, input and output tokens, tool calls, tool-specific charges, retries, elapsed time, and estimated cost. Define at least two thresholds:
- Soft threshold: pause new fan-out and request approval to continue.
- Hard threshold: reject new model or tool work and cancel or drain active branches according to your safety policy.
Use a conservative estimate when a provider’s final charge arrives later. Reserve budget for retries and the final aggregation step. If a tool has an independent billing meter, count it separately instead of hiding it inside token estimates.
A useful ledger records both measured usage and forecast usage. For each active branch, estimate remaining calls from its retry allowance and expected aggregation work. If the forecast crosses the soft threshold, stop admitting new tasks even when the hard threshold has not been reached. This prevents a burst of already-queued work from consuming the remaining allowance.
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Observability and interruption tests
Logs should make a multi-agent run reconstructable without exposing secrets. Capture:
- agent creation, delegation, start, completion, cancellation, and timeout events;
- tool requests, arguments after sensitive-field redaction, approvals, denials, and connector responses;
- active-thread count, queue depth, retry count, and fan-out depth;
- token usage, tool charges, budget thresholds, and the decision that stopped work;
- credential or policy version used for each action.
Build dashboards around cumulative run usage and active work, not just averages. Alert on unusual branch counts, repeated denied writes, long-lived credentials, and spend acceleration.
Correlate every child event to a parent run and task identifier. Record timestamps for admission, start, first tool call, completion, and cancellation so you can distinguish provider latency from queueing. Keep policy and credential versions with the event; otherwise a later audit cannot explain why an action was allowed.
Run failure drills
- Start enough independent tasks to hit the concurrency ceiling. Verify that excess work queues or fails predictably.
- Ask an agent to perform a restricted write. Verify that it receives a denial or approval request and cannot substitute another credential.
- Force the soft and hard budget thresholds. Confirm whether active work pauses, finishes, or is cancelled, and whether the ledger records the event.
- Interrupt the coordinator while children are running. Confirm that orphaned workers stop or expire.
- Retry a completed task. Verify idempotency so the same external write is not performed twice.
Run these tests with the same identities, connector settings, quotas, and network paths used in production. Documentation can describe a limit while an SDK, region, edition, or account policy changes its practical behavior.
How to evaluate a platform or framework
Use a control-focused checklist instead of comparing headline agent counts.
- Tool scope: Is access configured per agent, per session, or inherited through shared credentials?
- Credential scope: Can each role use a separate identity and short-lived token?
- Approval granularity: Can you require approval for all writes, selected actions, or only specific targets?
- Budget scope: Is the cap per call, run, session, credential, project, or account? What happens at the threshold?
- Concurrency unit: Does the number describe active subagents, threads, jobs, or merely defined agents?
- State behavior: Are contexts isolated, explicitly shared through a store, or implicitly shared?
- Observability: Can you see child tool calls, permission events, usage, and interruption status?
- Lifecycle: Is the workflow API current, or does the framework identify a legacy/template approach that is being replaced?
Ask vendors or inspect SDK behavior for edge cases: whether a cancelled child still consumes usage, whether a queued task counts toward concurrency, whether a resumed session repeats tool calls, and whether account-level quotas override a local setting. These details determine the real safety envelope.
Prefer the platform whose controls match the failure you need to prevent. A high managed-agent count is useful for organization but says nothing about safe concurrency; a write-approval prompt is useful for change control but says nothing about returned sensitive data; and a token estimate is useful for planning but not a hard budget.
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- Every agent has an explicit role, tool allowlist, data scope, and credential owner.
- Unused MCP servers, connectors, and write methods are removed rather than merely undocumented.
- Read and write identities are separated, with short-lived credentials where available.
- External writes have an approval gate or a narrowly defined, reversible exception.
- The orchestrator enforces maximum fan-out, active concurrency, recursion depth, retries, and per-task timeouts.
- Independent branches receive explicit inputs and write to isolated locations; shared state uses designed locking or transactions.
- A run ledger records model tokens, tool usage, retries, and estimated charges for every child.
- Soft and hard spend thresholds are tested, including pause, cancellation, and resume behavior.
- Provider limits are documented with their unit and scope, not copied as generic “agent limits.”
- Failure drills demonstrate that denied writes, interrupted coordinators, and exhausted budgets cannot create duplicate or unauthorized actions.
Frequently Asked Questions
How do I limit what AI agents can access?
Create a role-to-capability matrix and give each agent only the required tools, servers, data locations, and narrowly scoped credentials. Separate read and write identities, prefer short-lived tokens, and inspect the effective credential inherited by child threads.
How do I stop an agent from making an unapproved change?
Keep risky writes behind an approval gate. Show the exact target, operation, parameters, and supporting draft or evidence, then issue a short-lived authorization bound to that operation. Test that denial cancels the branch instead of triggering a retry with another identity.
How do I cap the cost of a multi-agent run?
Track model tokens, tool charges, retries, and elapsed work for every child in a run ledger. Set a soft threshold that stops new fan-out and a hard threshold that rejects further work or drains active branches. Verify whether vendor limits apply per call, run, session, credential, project, or account.
How many agents should I run in parallel?
Use the lowest ceiling that meets the workload and downstream capacity. OpenAI documents a default of six concurrent subagents excluding the coordinator, while Anthropic documents up to 25 concurrent threads. Google’s limit of up to 1,000 managed agents describes configured agents, not simultaneous executions.
Do parallel agents share conversation history automatically?
Not necessarily. Google ADK’s ParallelAgent runs branches separately; history and state are not automatically shared, and result order may be nondeterministic. Pass explicit inputs, use isolated output locations, and aggregate results in a parent step.
Is a connector action restriction enough to prevent data leakage?
No. ChatGPT Workspace Agents connector constraints narrow which actions an agent may request, but they do not filter data returned by an allowed action. Use least-privilege data access, redaction, and retention controls as separate safeguards.
What should happen when a concurrency or budget limit is reached?
Define the behavior explicitly and test it: queue, reject, pause, cancel, or partially complete. Record the event and ensure queued or resumed work cannot repeat completed external actions.
Can I rely on a provider’s advertised agent count as a safety limit?
No. Confirm the measured unit. A managed-agent count may describe definitions, whereas a concurrency setting describes active threads or subagents. Keep configured agents, queued tasks, active workers, retries, and completed tasks as separate counters.
The Bottom Line
Safe multi-agent operation comes from three explicit boundaries: minimum necessary access, a run-level spending envelope, and bounded concurrent execution. Scope tools and credentials per role, require approval for consequential writes, parallelize only independent work, and verify every provider limit’s unit and threshold behavior. Then prove the controls with interruption, denial, race, and budget-exhaustion tests using production-equivalent identities and quotas.
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