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Safe multi-agent systems use three separate controls: per-agent permissions, a human gate for consequential writes, and an explicit concurrency ceiling. Add run-level usage accounting because provider limits are not interchangeable: six concurrent subagents, 25 concurrent threads, and 1,000 configured agents describe different things. The design below shows how to narrow access, bound fan-out, stop runaway spend, and test the behavior before production.
Model the workflow as three independent control planes
Delegation makes one request expand into many model calls, tool calls, credentials, and downstream jobs. A secure design therefore answers three different questions:
- Access: Which tools, servers, data sources, and identities can this specific agent use?
- Spend: How much model and tool usage may this run accumulate, and what happens at the threshold?
- Parallelism: How many child tasks may be active at once, and what state may they share?
Do not treat an agent roster, a concurrency setting, an account quota, and a budget as synonyms. Record the unit and scope of every limit: call, run, session, credential, account, thread, job, or configured definition.
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Scope tools and credentials to each agent
Start with a role-to-capability matrix before enabling delegation. Anthropic’s managed-agent documentation defines tools and MCP servers per agent; its example gives a GitHub MCP server to a researcher while leaving it off other agents. Google likewise advises using trusted tools and scoping permissions to the minimum required. In practice, the agent’s tool list and the identity behind those tools are separate controls: a child may have a narrow declared tool set but still inherit a credential with broad authority.
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Build a role-to-tool matrix
| Agent role | Typical allowed capabilities | Keep out unless specifically required |
|---|---|---|
| Researcher | Read-only search, document retrieval, issue or repository read access | Production credentials, deletion, publishing, payment, deployment |
| Analyst | Read-only data queries and an isolated calculation environment | Raw secrets, write-capable database roles, unrestricted network access |
| Writer or editor | Draft storage and approved content sources | Direct publishing, outbound messaging, customer records |
| Release operator | Only the deployment or ticket actions needed for one change | Broad administrator credentials and unrelated production systems |
Use the matrix to remove unused MCP servers, connectors, and functions rather than merely telling an agent not to call them. Give each role a separate service identity where possible. Prefer short-lived tokens, narrow scopes, and regular rotation. Google specifically warns that an agent can use any credential available to it, so inspect inherited environment variables, shared session tokens, mounted files, and delegated identities.
Separate read and write identities
Create read-only credentials for discovery and analysis. A write-capable identity should be available only to the small set of agents that actually perform changes, preferably for a short-lived operation. Keep destructive operations—delete, publish, send, purchase, permission change, and deployment—behind a distinct connector action or service account. This makes an accidental tool call fail closed instead of becoming a production incident.
Check the effective permission, not just the configuration
- List every tool and MCP server configured for each agent.
- Resolve the credential used by each tool, including inherited session or worker credentials.
- Compare the credential’s scopes with the role matrix and remove excess privileges.
- Run a negative test: ask the agent to invoke a tool it should not possess and confirm that the request is rejected before execution.
- Repeat the test in a child thread, because delegation may change which identity or environment is inherited.
Put approval gates around risky writes
Read access and external writes have different failure costs. Require a person or a narrowly defined policy gate before an agent sends a message, edits a shared record, publishes content, buys something, deletes data, changes permissions, or deploys code.
ChatGPT Workspace Agents documents a useful default: app write actions start at Always ask, with custom or Never ask choices depending on the app. Use an equivalent approval state in other systems rather than assuming a tool declaration is enough. An approval event should show the intended action, target, parameters, requesting agent, identity, and a link or identifier for the originating run.
Use action constraints narrowly
Connector action constraints can restrict which operations an agent may request. They do not necessarily filter the data returned by a permitted action. For example, allowing a read action with a constrained operation does not make the returned records safe for every downstream consumer. Treat action policy and data-loss prevention as separate layers: constrain the operation, then apply output filtering, redaction, or destination controls where sensitive data is involved.
Make approvals auditable and resumable
- Record an immutable approval or denial event with timestamp, approver, and policy version.
- Expire approvals quickly; do not let an approval for one target authorize a later target.
- Bind the approval to exact arguments such as repository, record ID, recipient, amount, or deployment version.
- Define what happens to sibling tasks while one child waits: pause the branch, cancel it, or continue only with read-only work.
- Test rejection and timeout paths, not only successful approval.
Set a concurrency ceiling and control fan-out
Use a platform-native concurrency setting when it exists, then choose a lower application limit if downstream APIs, human review capacity, or budget require it. The unit matters:
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| Platform or framework | Documented limit or behavior | What it does not mean |
|---|---|---|
| OpenAI Agents API multi-agent sessions | max_concurrent_subagents defaults to six concurrent subagents, excluding the coordinator, when delegation is enabled. Delegation can also be disabled. |
It is not a spend cap, a total-agent-count limit, or a guarantee that six workers are appropriate for your workload. |
| Anthropic Managed Agents | Up to 25 concurrent threads; a coordinator can call multiple copies of a roster agent. | It does not establish a safe budget or authorize every tool available to the coordinator. |
| Google Gemini Managed Agents | Up to 1,000 managed agents. | This is a maximum number of managed agent definitions, not 1,000 simultaneously running executions. |
Google ADK ParallelAgent |
Listed subagents run concurrently in separate branches. | It does not automatically share conversation history or state, and result arrival order may be nondeterministic. |
| ChatGPT Workspace Agents | The cited workspace-agent material does not establish a multi-agent execution cap. | Do not infer a concurrency limit from its write-approval settings. |
These figures are provider-specific documentation, not a cross-platform comparison. Verify whether your exact cap applies to a model call, agent run, thread, session, worker process, or account. Also check whether the coordinator counts toward the limit; OpenAI’s documented default explicitly excludes it.
Bound fan-out at every level
A top-level coordinator that starts eight workers, each of which starts eight more, can create 64 descendants before accounting for retries. Set both a maximum number of direct children and a maximum depth. Add a global active-task semaphore in your orchestration layer so nested delegation cannot bypass the top-level ceiling.
global_active_limit = 6
max_children_per_agent = 3
max_delegation_depth = 2
on_start(task):
acquire(global_active_limit)
reject_or_queue_if_depth_exceeded(task)
on_finish(task):
release(global_active_limit)The pseudocode is an application-side pattern, not a provider API. Your implementation should also cap retries, apply exponential backoff to rate limits, and release the semaphore when a child fails, times out, or is cancelled.
Parallelize only independent work
Use parallel branches for tasks that do not depend on one another, such as reviewing separate documents or retrieving unrelated records. If branches must coordinate, design that coordination explicitly with a shared store, locking, version checks, or a parent aggregation step. Google ADK states that ParallelAgent branches do not automatically share history or state and that results may arrive in nondeterministic order. Give every result a stable task ID and have the parent sort or reconcile results rather than relying on arrival order.
Google notes that graph-based workflows supersede templated workflows starting with ADK 2.0 for Python and Go. For a new ADK design, use the current graph-oriented approach and confirm the version-specific lifecycle before copying an older template.
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Fan-out, retries, long contexts, and tool loops multiply usage. Google describes Gemini managed-agent usage as pay-as-you-go from model tokens and tool use, and gives a broad estimate of 100,000 to 3 million tokens for a typical single interaction. That is a provider estimate, not a price and not a universal cost prediction; measure your own workflow.
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| Provider or layer | Spend evidence | Safe interpretation |
|---|---|---|
| Google Gemini Managed Agents | Pay-as-you-go based on model tokens and tool usage; a typical single interaction is described as 100k–3M tokens. | Track actual token and tool consumption. The range is too broad to serve as an invoice estimate. |
| Anthropic Managed Agents | A search result describes a session budget that pauses runs when reached, but the opened orchestration documentation did not confirm the API details. | Verify current budget scope, request format, pause behavior, and resume semantics before depending on it. |
| OpenAI multi-agent documentation reviewed here | No spending cap is documented on the cited multi-agent page. | Implement accounting and stopping in your orchestration layer unless current product documentation confirms another control. |
| Google ADK and Workspace Agents sources reviewed here | No spend cap is established by the cited material. | Use application-side accounting and provider billing alerts as separate safeguards. |
Implement an application-side budget ledger
For every run, persist:
- Run ID, parent run ID, agent role, model, and provider.
- Input and output tokens, cached tokens when reported, and tool-call counts.
- Tool name, target system, duration, retries, and any metered external charge.
- Approval events, denials, cancellations, and the timestamp at which each threshold was crossed.
Estimate charges using the provider’s current rate card outside the agent prompt. Set at least two thresholds: a warning that asks for approval to continue and a hard stop that prevents new model or tool calls. Decide whether already-running calls are allowed to finish or are actively cancelled. Test that decision, because a budget number is not useful if queued children continue after the parent has stopped.
Choose the budget scope deliberately
Ask the provider or inspect current documentation whether a limit is per request, run, session, credential, project, or account. A session cap may not protect a workflow that starts multiple sessions; an account quota may stop unrelated production work. Store the scope beside the configured value and expose it in operational dashboards.
Plan capacity as well as dollars
Concurrency changes latency and pressure on downstream systems even when model pricing is unchanged. Define a target completion time, then reserve capacity for retries, human approvals, and cleanup. A six-worker ceiling may still overload a rate-limited API if every worker makes several calls at once. Conversely, a low ceiling can make a deadline impossible. Measure queue wait, active time, tool latency, retry count, and completion rate separately so you can tell whether the bottleneck is the model, a connector, or your semaphore.
Use a dry-run mode for new workflows. It can execute planning and read-only calls while replacing writes with recorded intents. This exposes unexpected fan-out, oversized prompts, and hidden credential dependencies before the workflow can alter an external system. Keep dry-run traces comparable to production traces so the same dashboards and budget calculations apply.
Common failure modes to eliminate
- Permission by prompt: telling an agent “do not publish” is weaker than omitting the publishing tool and write credential.
- One shared superuser: a child that needs repository reads should not inherit a deployment token merely because the coordinator has one.
- Configured count mistaken for activity: 1,000 managed definitions do not prove that 1,000 executions can run simultaneously.
- Unbounded recursive delegation: every child needs a depth and child-count check, not only the root.
- Arrival-order aggregation: nondeterministic branch completion can change outputs unless results carry IDs and are reconciled deterministically.
- Budget checked only at the end: by completion time, queued calls and retries may already have consumed the allowance.
- Approval without exact arguments: approving “update the account” is unsafe when the account, fields, and values are not fixed.
- Assuming action policy filters data: restricting an operation does not automatically redact sensitive fields returned by that operation.
Design an end-to-end guarded run
- Classify the request. Mark data sensitivity, external side effects, expected branches, and maximum acceptable cost.
- Select a least-privilege coordinator. Give it planning and read capabilities, not every worker’s write credential.
- Create a bounded plan. Set child-count, depth, retry, timeout, and active-task limits before starting work.
- Assign isolated roles. Each child receives only its required tools, server connections, data sources, and short-lived identity.
- Start independent branches. Use a semaphore and stable task IDs; write intermediate state to an explicitly designed store.
- Aggregate deterministically. Validate every child result, reconcile conflicts, and reject missing or out-of-order results.
- Gate external writes. Present exact action arguments for approval or a narrowly scoped policy decision.
- Enforce the budget. Update the ledger after every model and tool event; warn, pause, or stop at thresholds.
- Close the run. Cancel orphaned children, revoke temporary credentials, persist an audit record, and report actual usage.
Test the controls before production
- Permission test: a researcher attempts a write and receives a denial before the external system is contacted.
- Credential test: a child attempts to use an inherited secret outside its role; the identity provider rejects it.
- Approval test: a send or deployment pauses, displays exact parameters, and resumes only after approval.
- Concurrency test: launch more branches than the ceiling and verify that excess work queues or fails predictably.
- Nested fan-out test: have children delegate and confirm that global limits still hold.
- State test: run parallel branches that finish in different orders and verify deterministic aggregation.
- Budget test: cross warning and hard-stop thresholds; confirm whether active calls finish, queued work is cancelled, and resume requires an explicit decision.
- Failure test: kill a worker, expire its token, deny a tool call, and interrupt the coordinator; check cleanup and audit logs.
Run these tests with the same identities, token lifetimes, connector settings, and provider versions used in production. Limits and product behavior can change, so record the documentation date and re-run the suite after platform upgrades.
Operational checklist
- Every agent has a documented role and an allow-list of tools and MCP servers.
- Credentials are least-privilege, short-lived where possible, rotated, and checked for inherited scope.
- Read and write identities are separated.
- Consequential writes require an approval or a narrowly scoped policy gate.
- Action constraints are not treated as output-data filtering.
- Direct children, delegation depth, retries, timeouts, and total active tasks are bounded.
- Configured agent count is never substituted for concurrent execution count.
- Run-level token, tool, approval, and interruption events are logged.
- Warning and hard-stop thresholds have tested behavior.
- State sharing and result ordering are explicit for parallel branches.
- Provider-specific limits are labeled with their unit and scope, and current documentation is rechecked before relying on them.
Frequently asked implementation questions
How do I limit what an AI agent can access? Create an allow-list per role, remove unused tools and MCP servers, and bind each tool to a least-privilege identity. Then test a forbidden call in both the coordinator and child contexts.
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Should every child agent receive the coordinator’s credentials? No. Give each child its own identity or a narrowly scoped delegated token. Inspect inherited environment variables, session tokens, mounted files, and worker roles.
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How many agents should run in parallel? Start below the provider maximum and tune against downstream rate limits, review capacity, latency, and measured usage. A documented maximum is a ceiling, not a workload recommendation.
Is Google’s 1,000-agent limit a concurrency limit? No. It refers to managed agent definitions. It should not be presented as permission to run 1,000 tasks simultaneously.
How can I cap the cost of one run? Maintain an application-side ledger, calculate estimated charges from the current rate card, warn at an approval threshold, and hard-stop new calls at a second threshold. Verify whether active calls and queued children are cancelled or allowed to finish.
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What if a vendor advertises a budget feature? Confirm whether it applies per call, run, session, credential, project, or account; document the pause and resume semantics; and test the threshold with production-like credentials. A vendor cap and your own run ledger protect different failure modes.
Frequently Asked Questions
How do I limit what an AI agent can access?
Create an allow-list per role, remove unused tools and MCP servers, and bind each tool to a least-privilege identity. Test forbidden calls in both coordinator and child contexts.
How many agents should run in parallel?
Start below the provider maximum and tune against downstream rate limits, review capacity, latency, and measured usage. A documented maximum is a ceiling, not a recommendation.
How can I cap the cost of one run?
Track tokens and tool calls in an application-side ledger, set warning and hard-stop thresholds, and test what happens to active and queued work when the limit is reached.
Do parallel branches share context automatically?
Not in Google ADK’s documented ParallelAgent behavior. Use an explicit shared store, locking, version checks, or parent aggregation.
What actions should require human approval?
Require approval for sends, edits, publishing, purchases, deletions, permission changes, and deployments, with exact targets and arguments bound to the approval.
The Bottom Line
Use least-privilege tools and identities for every agent, approval gates for consequential writes, and explicit limits on children, depth, retries, and active work. Track usage across the entire run instead of assuming a provider’s agent count or concurrency setting controls spend. Label every documented limit by its unit and scope, then test permissions, fan-out, budget stops, state handling, and cleanup with production-like credentials before relying on the workflow.
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