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Use a model to classify a request or recommend a next step; keep authorization and execution in trusted application code. A classification can inform a policy decision, but it cannot grant permission to call a tool, access data, spend money, or send a message. System One’s integration guide puts it plainly: “A model result is not authorization.” System One’s official integration guide describes the model’s choice as a proposal for application code to evaluate, not an action to execute.
Where classification fits in an agent loop
An agent loop is an iterative control flow: the model receives context, may request a tool, the runtime validates and executes that request, and the result returns to the model for another turn. The loop can end when the model gives a final response or another stop condition applies. Strands Agents documents this pattern, including examples of stop conditions such as cancellation, turn or token limits, content filtering, and guardrail intervention; other frameworks may behave differently. Strands Agents’ agent-loop documentation
For a classifier-first design, put a trusted host-side policy check between the model’s proposal and any consequential tool execution:
Request → model classification → host authentication and policy checks → allowed tool execution → tool result → next model turn
The model can choose among bounded outcomes, route a request, score it against a rubric, or estimate whether a condition holds. System One advises using another reasoning step or a person when the task needs open-ended planning. The host remains responsible for deciding whether a proposed action is permitted.
Define outcomes that do not carry authority
Give the classifier a small, explicit set of possible results. For example, it might return answer, think, or review. These labels describe a proposed next step; none should directly invoke a tool or bypass the application’s authorization checks.
- answer: Continue toward a response that does not require a protected action.
- think: Send the request to a separate reasoning step, if your application supports one.
- review: Pause for a configured human or policy review before any action requiring it.
Use an explicit schema and validate the returned value. Treat an unknown label, malformed response, or missing required information as an unresolved decision—not as permission to choose the most convenient action.
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Before a tool call can cause a side effect, the application should authenticate the actor, resolve the relevant tenant and resource, check authorization, and map the model’s proposal to an allowlisted action. A model response can help select a route, but only the application’s policy should determine whether the route is allowed.
- Authenticate the actor. Establish the identity associated with the request using the application’s trusted authentication mechanism.
- Load the relevant policy facts. Resolve tenant, resource, permissions, and other facts needed to evaluate the requested action. Do not assume the model’s account of those facts is authoritative.
- Map the proposal to an allowlist. Convert an accepted model outcome into a known application action. Reject outcomes or tool names outside the allowlist.
- Apply approval requirements. If policy calls for escalation, pause execution and send the action through the configured approval path. Do not execute while approval is pending or if it fails.
- Bind approval to the exact action. Preserve the reviewed tool, arguments, actor, tenant, policy version, and relevant facts. If the arguments or target change, evaluate and approve the changed action rather than reusing stale approval.
- Execute with scoped credentials. Use least-privilege tool credentials and retain independent authorization checks in backend services. A runtime policy layer does not replace the backend’s own access controls.
- Record the decision trail. Keep an auditable record of the proposal, policy result, approval state, and executed action, while avoiding exposure of secrets in logs.
Enforce policy at the tool boundary
The model and tool outputs should be treated as untrusted inputs. Microsoft’s Agent Governance Toolkit describes pre_tool_call as the point where a model-influenced proposed invocation meets real tool authority. At that boundary, the host must follow the policy verdict: block, transform, escalate, or proceed as appropriate. Microsoft Agent Governance Toolkit security model
Any execution route that bypasses the host’s mediation falls outside the protection described by that model. Ensure that every path to a tool—including retries, background jobs, alternate runtimes, and direct backend calls—enforces the applicable authorization rules. If policy transforms a target or argument, execute only the transformed action that was checked.
Handle failures without turning them into permission
Decide how the application behaves when a classifier, policy service, or approval path cannot provide a valid result. For consequential actions, fail closed: stop the action until the required checks succeed. A fallback that automatically executes a proposed tool call would turn an availability problem into an authorization bypass.
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- Unknown or malformed outcome: Reject it and request a valid result or route to a safe review path.
- Missing facts: Gather the facts from trusted application sources or ask for clarification before policy evaluation.
- Classifier or policy service unavailable: Do not execute consequential actions without the required decision. Define a safe alternative, such as returning an error or queueing review.
- Approval is stale or arguments changed: Re-evaluate the current action and obtain approval for the exact version to be executed.
- Unmediated tool route discovered: Close or protect that route so it cannot bypass host policy and backend authorization.
Use System One’s integration details with version awareness
System One documents a typed decision request that returns a proposed choice for application code to evaluate. The guide’s example labels that choice a proposed step, not an action to execute. Its reviewed text-only hosted-client example lists @system-one-ai/core, @system-one-ai/adapter-system-one, and @system-one-ai/transport-fetch at version 0.6.0, with Node.js 22.18 or later. These are the versions and runtime requirement stated in the guide checked on October 7, 2026; verify the documentation before adopting them because SDK requirements can change. System One integration guide
The guide also says to keep a hosted API key in a server environment variable or trusted private credential setting. Keep it out of prompts, tool descriptions, browser bundles, URLs, and logs, and revoke keys when they are no longer needed. Separate agents sharing an account do not thereby receive isolated balance, rate-limit, or idempotency namespaces: the guide says account keys share those resources.
Evaluate the classifier and the complete control path
A fast response or a model’s name does not establish that it is suitable for your task. System One recommends evaluating task quality, latency, price, and usage limits on representative cases. Also test the host’s authorization and recovery behavior, since a good classification cannot compensate for a tool route that ignores policy.
Quick Recap
- Test ambiguous wording, missing information, and consequential mistakes—not only clear, routine requests.
- Check whether the classifier can express the small outcome set your application needs, and how malformed or unknown results are handled.
- Measure latency, price, and applicable usage limits for your workload rather than assuming they from the model label.
- Verify that the host checks the actor, tenant, resource, action, and arguments before execution.
- Confirm that escalated actions wait for approval and that approval remains tied to the exact action eventually run.
- Exercise service outages, cancellations, retries, and alternate execution paths to ensure they cannot bypass policy.
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