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A preflight harness is the runtime control layer that checks a coding agent’s proposed tool call before anything runs. The model can select a tool and draft its arguments, but your application or harness must decide whether that call is well-formed, authorized for this job, safe in the current sandbox, approved when necessary, executed, and reported back without leaking secrets. Treating a prompt instruction such as “be careful” as the control layer leaves the execution boundary in the wrong place.
What a preflight harness does
A tool-using coding agent normally produces a request such as “run this shell command,” “write these bytes to a file,” or “call this MCP tool.” The language model is responsible for choosing among the tools you expose and supplying arguments. It does not automatically own the machine, container, credentials, network, or policy decision behind that request. Those remain application responsibilities.
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The harness sits between the model’s request and the executor. It receives the tool name and arguments, validates their shape, applies policy, checks the execution context, asks for approval when required, invokes the operation in the permitted environment, and returns a bounded result. This is why OpenAI’s tool guidance keeps shell, apply-patch, and computer-use harnesses in the runtime even when the SDK models the tool decision: modeling a decision is not the same as safely carrying it out. See OpenAI’s “Using tools” guide.
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- Inventory: Is this tool enabled for this run, and what systems or data can it reach?
- Schema: Do the arguments conform to the declared input type, with no unexpected fields?
- Policy: Is this operation allowed for this user, repository, workspace, and run mode?
- Context: Are the required paths, binaries, dependencies, credentials, and network routes available in the intended execution origin?
- Boundary and approval: Does the action remain inside the sandbox, or does it require explicit review?
- Execution and result handling: Can the operation run, and can its output be returned without exposing secrets or unnecessary environment details?
Keeping these gates separate makes failures explainable. A malformed JSON object is a schema failure; a valid request to delete a protected directory is a policy failure; a permitted command with a missing executable is an environment failure. Combining all three into one “tool call failed” response makes recovery and auditing much harder.
Why a valid tool call may still be unauthorized
Tool schemas describe what an input looks like. They do not establish whether the caller should be allowed to perform the requested action. Anthropic’s documentation similarly separates tool definitions from permission policies; its overview is at Tool use with Claude, and policy concepts are described at Permission policies.
| Check | Question it answers | Typical outcome |
|---|---|---|
| Tool inventory | Is this capability exposed in the current run? | Allow or deny before parsing execution details |
| Input schema | Are required fields, types, ranges, and enum values correct? | Reject malformed or unexpected arguments |
| Authorization policy | May this identity perform this operation on this target? | Allow, deny, or send for review |
| Resource scope | Does the path, repository, service, or endpoint fall inside the job’s scope? | Allow only an explicitly bounded target |
| Side-effect classification | Is the action read-only, state-changing, destructive, or externally consequential? | Apply the matching approval rule |
| Execution context | Can the configured sandbox or remote service safely and successfully run it? | Run, or fail closed with a bounded error |
This separation is an implementation pattern synthesized from documented tool definitions, runtime handlers, and permission controls; the documentation does not prescribe one universal preflight schema. Your policy result should be explicit—allow, deny, or review—rather than inferred from a successful schema parse.
Build the preflight sequence
1. Declare a versioned tool surface
Maintain an inventory for every tool containing its stable name, description, input schema, side-effect label, reachable systems and data, execution origin, and policy version. Mark whether it can read files, write files, launch processes, alter infrastructure, send data externally, or spend money. Version this inventory alongside the agent configuration so an audit can identify which capabilities existed when a run occurred.
Expose only the tools required for the task. For MCP integrations, discovery should not silently become unrestricted access. The Agents API MCP documentation supports an explicit allowed_tools list and an option to make initialization required; configure both deliberately in OpenAI’s MCP connections guide.
2. Validate arguments, then evaluate policy
Parse the model’s arguments with a strict schema. Reject missing required fields, wrong primitive types, unknown properties, path strings that fail normalization, and values outside declared bounds. Do not “repair” ambiguous input by guessing; return a short validation error that tells the agent what field must change.
After parsing, evaluate authorization independently. Check the current run mode, user or service identity, repository and workspace, target resource, operation class, and any time or rate limits. A request to read src/ may be allowed while the same tool’s request to read /etc is denied. A patch inside the workspace may be allowed automatically, while changing a deployment manifest may require review.
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if decision.status == "deny" or decision.status == "review_pending":
return bounded_error(decision.reason)
result = execute(decision.normalized_arguments, decision.execution_context)
return sanitize_result(result)Keep policy explanations useful but non-sensitive. “Path is outside the writable workspace” is actionable; dumping environment variables, access tokens, or a complete filesystem listing is not.
3. Check the execution context
Determine where the call will execute: inside the application process, in an isolated agent sandbox, or through a remote service. Confirm that the origin matches the trust boundary you intended. A local MCP connection and a connection from OpenAI’s service do not have the same network path or credential implications; the MCP connection documentation covers HTTP and stdio setup and distinguishes these origins.
Before execution, verify the required working directory, binaries, language runtimes, package dependencies, mounted paths, environment variables, and approved network endpoints. Resolve paths to canonical form before comparing them with allowlists, and account for symlinks and platform-specific path syntax. If a dependency or executable is absent, fail with a bounded “unavailable in this environment” result instead of automatically broadening access.
4. Enforce sandbox boundaries and approvals
Sandboxing and approvals solve different problems. A sandbox constrains what the process can reach—files, protected paths, network routes, and sometimes system calls. An approval policy decides when a human or supervising service must review an otherwise possible action, such as crossing a configured boundary. OpenAI describes these complementary controls in Sandbox security and Running Codex safely at OpenAI.
- Use isolated compute when jobs must not share files, processes, caches, or credentials.
- Give each job the smallest readable and writable filesystem scope that meets its purpose.
- Allow outbound traffic only to named endpoints, ports, and protocols needed by the task.
- Require approval for destructive changes, credential use, external publication, financial actions, and any operation that leaves the sandbox boundary.
- Record the rule that triggered review and the identity and time of the approval.
An approval dialog is not a substitute for isolation. A reviewer can approve a command without realizing that the process can also read unrelated secrets if the sandbox is broad. Conversely, a tightly isolated sandbox still needs approvals for actions whose consequences inside that sandbox are significant.
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5. Treat MCP servers and credentials as trust decisions
MCP adds a server, transport, tool definitions, and data-sharing path to the execution chain. Verify the server identity and endpoint against the provider’s documentation. Prefer a provider-hosted official server over an unverified proxy when practical, and review changes to its tools and behavior over time.
OpenAI’s Agents SDK documentation states: “Connect only to servers you trust, use least-privilege credentials, keep access tokens in authorization fields or headers rather than URLs, and require approval for sensitive operations.” Apply that guidance by issuing narrowly scoped credentials, never embedding tokens in URLs or reusable agent definitions, and reviewing which prompts, repository files, and tool outputs are sent to each server. The MCP security guidance also uses the heading “Always require approval for sensitive actions”; it appears in OpenAI’s MCP servers documentation.
When a tool genuinely needs a secret, consider a trusted proxy or server that injects a scoped credential outside agent-generated code. Keep high-value application credentials unavailable to arbitrary generated processes wherever possible. Agent-generated code can access whatever resources the execution environment exposes, so credential placement is a boundary decision, not merely a prompt-writing decision.
6. Define failure behavior before launch
Specify what happens for each expected failure class:
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| Failure | Safe response | Agent-facing message |
|---|---|---|
| Malformed arguments | Do not execute; retain no partial side effect | Identify the invalid field and required format |
| Tool disabled or unauthorized | Deny without probing the target | State that the capability or resource is not available |
| Sandbox or approval boundary | Pause for approval or deny according to policy | Explain the boundary and the required next step |
| MCP initialization, credential, or network failure | Fail closed; do not retry with broader credentials | Report that the server could not be initialized or reached |
| Missing executable or dependency | Do not install or fetch unapproved software automatically | Name the unavailable dependency without exposing secrets |
| Timeout or tool error | Terminate according to the tool’s cleanup contract | Return a bounded timeout or execution error |
The official MCP connections guide lists initialization, network, credential, executable, dependency, and working-directory conditions among the issues operators must troubleshoot. A retry policy should be operation-specific: repeating an idempotent read may be safe, while repeating a state-changing request could duplicate its effect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to record in the audit trail
Store enough structured information to reconstruct what the agent requested, what policy decided, and what actually happened. OpenAI describes OpenTelemetry export for prompts, approvals, execution results, MCP server usage, and network proxy decisions in its safety guidance.
- Run, user, repository, and policy-version identifiers.
- Tool name and normalized, relevant arguments, with secrets and sensitive payloads redacted.
- Schema result, authorization result, approval event, and the rule or reviewer responsible.
- Execution origin, sandbox profile, target server identity, and network allowlist decision.
- Start and end times, exit status, bounded output, timeout or failure reason, and cleanup status.
Apply your organization’s retention and privacy rules. Do not log raw access tokens, complete secret-bearing environment variables, or unfiltered command output merely because it is convenient for debugging. A redacted event that preserves the decision path is more useful than an unrestricted transcript that creates a second security problem.
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Compare implementations against the same workload and threat model; there is no published benchmark in the available documentation that ranks providers or proves a particular architecture reduces risk by a measured percentage.
| Axis | Questions to ask |
|---|---|
| Execution isolation | Are jobs separated by VM, container, or session? Can one job read another’s files, processes, caches, or artifacts? |
| Filesystem scope | Which paths are readable or writable? Do protected paths remain inaccessible after symlink and path normalization? |
| Network policy | Which endpoints are allowlisted? Where do local and remote MCP connections originate? |
| Credential exposure | Can generated code read application, cloud, repository, or third-party secrets? |
| Tool control | Are schemas strict? Are tools allowlisted? Are read-only and state-changing operations classified separately? |
| Review policy | Which operations require human approval? Are approval decisions recorded and attributable? |
| Failure behavior | What happens when a server, dependency, executable, credential, or route is unavailable? |
| Observability | Can an operator reconstruct the request, policy result, execution, MCP usage, and network decision? |
Document the assumptions behind every comparison: operating system, sandbox profile, network topology, tool versions, credential scopes, and whether the workload is read-only or state-changing. Otherwise two “sandboxed” designs may be enforcing very different boundaries.
A deployment checklist
- Inventory and version every exposed tool, schema, side effect, reachable resource, and execution origin.
- Use strict parsing and reject unknown or ambiguous arguments.
- Run authorization after schema validation, not instead of it.
- Normalize and scope paths, repositories, service identifiers, and network destinations.
- Use isolated compute and least-privilege mounts for untrusted or generated code.
- Keep outbound network access allowlisted and distinguish local from remote MCP connections.
- Keep high-value credentials outside generated code; use scoped proxy credentials when needed.
- Require approval for sensitive actions and sandbox-boundary crossings.
- Fail closed when a decision cannot be checked, and return bounded errors that support recovery.
- Record policy, approval, execution, MCP, and network events without logging secrets.
- Review MCP server identity, tools, permissions, and data flows whenever the server or its configuration changes.
What the available guidance does—and does not—prove
The cited OpenAI and Anthropic documentation provides implementation and safety guidance for tool definitions, MCP connections, sandboxing, approvals, and permission policies. It does not provide a comparative effectiveness study of preflight harnesses, a prevalence statistic, or a measured percentage of incidents prevented. Treat the sequence and checklist here as an engineering design framework to adapt to your threat model, not as a benchmark result.
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