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A preflight harness is the runtime control layer that evaluates a coding agent’s proposed tool call before anything runs. It validates the arguments, checks authorization and environment constraints, applies sandbox and approval rules, executes the operation in the correct context, and returns a bounded result. The model can choose which tool to request, but your application still owns the tools, execution environment, policy boundary, and result handling.
This separation is the practical way to let an agent work with shells, files, APIs, or MCP servers without turning a prompt instruction such as “be careful” into your security model.
What a preflight harness actually controls
Tool use has two distinct decisions. First, the model proposes a call: a tool name plus arguments. Second, a runtime decides whether and how that call executes. OpenAI’s tool guidance keeps shell, file-editing, and computer-use harnesses in the runtime even when the SDK represents the model’s tool decision. See OpenAI’s tool-use guide.
The harness therefore sits between an agent and every side effect. It can reject an unknown tool, detect malformed arguments, determine whether the current run may touch a path or service, request human approval, execute inside an isolated environment, and redact sensitive details from the result. A valid schema is necessary, but it does not prove that the requested operation is authorized.
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Keep the control layers separate
| Layer | Question it answers | Typical control |
|---|---|---|
| Tool inventory | What capabilities exist in this run? | Versioned names, descriptions, side-effect labels, reachable systems, and an allowlist |
| Schema validation | Is the request well formed? | Strict argument parsing, required fields, types, ranges, and rejection of unexpected fields |
| Authorization policy | Is this caller allowed to perform this operation here? | Run identity, workspace rules, service scope, operation class, and data sensitivity |
| Execution context | Where will it run and what can it reach? | Application process, isolated sandbox, container, VM, or remote service |
| Approval policy | Must a person review it first? | Review for sensitive actions or attempts to cross a configured boundary |
| Result handling | What does the agent receive afterward? | Bounded output, secret redaction, error classification, and retry guidance |
This division reflects the way documented function tools, permission controls, runtime handlers, and agent environments work; no single schema or prompt supplies all of these controls. Anthropic’s overviews of tool use and permission policies make the same practical distinction between describing a tool and governing its use.
A six-stage preflight sequence
1. Declare a minimal, versioned tool surface
Maintain an inventory for each agent run. For every tool, record its name, purpose, input schema, whether it is read-only or state-changing, the systems and data it can reach, and the version of its implementation. Expose only the capabilities required for the task. A coding agent that needs to inspect a repository does not automatically need deployment, billing, production database, or unrestricted network tools.
For MCP integrations, discovery should not equal access. The Agents API MCP documentation describes an allowed_tools option and a way to require initialization, so you can constrain which server tools are available and ensure setup has completed before calls proceed: MCP connections documentation.
2. Validate syntax, then evaluate policy
Parse the model’s arguments against the declared schema and reject malformed or unexpected fields. Do not silently coerce ambiguous values such as a path that is missing, a command represented as both a string and an array, or an omitted service identifier.
After parsing, run an independent policy decision. Check whether the tool is enabled for this run, whether the target lies within the permitted workspace or service account, and whether the operation is read-only, state-changing, or externally consequential. Return an explicit allow, deny, or review outcome. Keeping these outcomes separate makes audit records understandable and prevents a syntactically perfect request from bypassing authorization.
3. Verify the execution context
Determine where the operation will execute: in the host application, in an agent sandbox, through a remote service, or through an MCP connection. Confirm that required paths, binaries, dependencies, environment variables, and network endpoints exist in that context. A path visible to the application may not exist in an isolated worker, and a command available on a developer laptop may be absent from a production image.
Connection origin matters for MCP. The documented API distinguishes connections made from OpenAI’s service from connections originating in the execution environment and covers HTTP and stdio setup. Record the intended origin, then verify the endpoint, working directory, executable, dependency set, and credential injection method before allowing a call.
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Use isolated compute when jobs should not share files, processes, credentials, or network state. Match filesystem permissions to the task: provide the repository paths that must be read or changed, keep protected paths inaccessible, and make temporary areas explicit. Restrict outbound traffic to approved endpoints rather than granting general internet access.
Sandboxing and approvals solve different problems. A sandbox constrains what the process can access; an approval policy determines when a person must authorize an operation, including a request to leave the configured sandbox boundary. OpenAI’s safety guidance describes these controls as complementary in Sandbox security and Running Codex safely at OpenAI.
Require review for actions such as modifying protected files, changing infrastructure, sending external messages, handling sensitive data, or accessing a service outside the run’s normal scope. Never treat a natural-language caution as a substitute for an enforced deny or approval gate.
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5. Treat MCP servers and credentials as trust decisions
An MCP server can define its own tools and can change its behavior over time. Verify the server identity and endpoint against the provider’s documentation. Prefer an official, provider-hosted server over an unverified proxy when practical, and periodically review its tools, permissions, transport, and data flows.
OpenAI’s Agents SDK guidance 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.” Follow that rule operationally:
- Use credentials scoped to the smallest repository, project, tenant, or API operation.
- Keep tokens out of URLs, prompts, reusable agent definitions, command arguments, and logs.
- Review which prompt, repository, or generated files are shared with each server.
- Expose only the server tools needed for the current job.
- For credentials that tools require, consider a trusted proxy or server that supplies scoped credentials outside agent-generated code.
OpenAI’s MCP security documentation also uses the heading “Always require approval for sensitive actions.” Treat that as an operational rule, not as evidence that every MCP call is safe by default: MCP servers guidance.
6. Define failure behavior before production
Preflight should have a known response for every expected failure class:
- Invalid arguments: reject the call and identify the field-level correction without exposing unrelated environment details.
- Unavailable tool: return a bounded error that tells the agent the capability is disabled, rather than allowing it to guess another privileged route.
- Initialization failure: stop MCP calls until initialization succeeds; do not proceed with a partially configured server.
- Timeout: terminate or cancel according to the runtime’s guarantees and report that the outcome may be unknown if cancellation is not confirmed.
- Permission denial: state which policy class blocked the request and whether a review path exists.
- Tool error: return the tool’s safe diagnostic and a recovery hint, excluding secrets, full environment dumps, and unrelated file contents.
- Unavailable dependency, executable, working directory, or network route: fail closed for consequential actions and identify the missing prerequisite.
The MCP connection documentation lists initialization, network, credential, executable, dependency, and working-directory conditions as troubleshooting concerns: MCP connections.
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Sandbox design: what to constrain
Agent-generated code can access resources available to the process that runs it. Isolation is therefore a workload property, not merely a model setting. Separate jobs that should not share data, constrain writable and readable paths, and keep high-value application credentials outside the agent environment whenever possible.
A useful sandbox review asks:
- Which directories are readable, writable, or completely hidden?
- Can the process reach host sockets, metadata services, sibling jobs, or protected system paths?
- Which outbound domains, ports, and protocols are allowed?
- Are package installation and arbitrary downloads disabled or mediated?
- Can generated code read environment variables containing application or third-party secrets?
- What happens to files and credentials when the job ends?
Apply the narrowest permissions that still let the task complete. If a build needs network access to a package mirror, allow that mirror rather than the whole internet. If a test needs a service, provide a disposable endpoint or scoped token rather than a production credential.
Approvals should be predictable, not improvised
Define approval classes in policy and show the operator enough context to make a decision: tool name, normalized arguments, target path or service, requested side effect, identity of the MCP server if applicable, and the reason the rule triggered. An approval prompt that hides the destination or collapses a complex command into “run tool” is not meaningful review.
Use automatic approval only for low-risk operations whose scope is already constrained, such as reading files inside the assigned workspace. Require review for destructive changes, external side effects, secret access, privilege changes, production systems, and boundary crossings. Record approvals and denials so an operator can distinguish an agent failure from an intentional policy decision.
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Logs should reconstruct what the agent requested, what the harness decided, and what actually happened. Record:
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- run and agent identifiers, timestamp, and tool version;
- tool name and relevant normalized arguments, with secrets removed or hashed;
- schema result and policy outcome: allow, deny, or review;
- approval identity, decision, and displayed context;
- execution context, sandbox or worker identifier, and MCP server identity;
- network allow or deny decisions;
- execution result, exit status, timeout, or classified failure;
- redaction and retention actions applied to the record.
Do not log raw access tokens, private keys, or unrestricted command output merely because it is convenient. Apply your organization’s retention and privacy rules. OpenAI describes OpenTelemetry export for prompts, approvals, execution results, MCP server usage, and network proxy decisions in its safety discussion: Running Codex safely at OpenAI.
Comparing two harness implementations
Do not rank harnesses by a single feature such as container support. Compare the same workload using the same questions:
| Axis | Questions to answer |
|---|---|
| Execution isolation | Are jobs separated by VM, container, or session, and can they share data or processes? |
| Filesystem scope | Which paths are readable or writable, and do protected paths remain inaccessible? |
| Network policy | Which endpoints are allowlisted, and does a local or remote MCP connection originate inside the intended boundary? |
| Credential exposure | Can generated code read application or third-party secrets, or are credentials supplied through a scoped proxy? |
| Tool control | Are schemas strict, tools allowlisted, and read-only operations distinguished from state-changing ones? |
| Review policy | Which actions require a person, and are decisions recorded? |
| Failure behavior | What happens when a server, dependency, executable, or network route is unavailable? |
| Observability | Can an operator reconstruct the request, policy decision, execution, and result? |
These are implementation comparison axes, not a published benchmark or an empirical ranking of providers. A harness is stronger when it makes each answer explicit and enforceable.
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A practical decision flow
receive model tool call
-> identify tool from versioned inventory
-> parse strict schema
invalid: return bounded validation error
-> evaluate run policy and target scope
denied: record denial and return policy error
-> determine execution context and prerequisites
unavailable: fail closed and report missing prerequisite
-> classify side effect
sensitive or boundary crossing: request approval
rejected: record rejection and stop
-> execute in constrained sandbox or approved remote context
-> redact and bound the result
-> record request, decision, approval, execution, and outcome
-> return result or classified error to the agentThe flow deliberately puts policy and context checks after parsing but before execution. It also makes “unknown” a safe state: if the harness cannot verify a destination, credential scope, or boundary, it does not guess.
Preflight checklist for a production rollout
- Version the tool inventory and expose only task-required tools.
- Mark every tool’s side effects and reachable systems.
- Enforce strict schemas and reject unexpected fields.
- Implement separate allow, deny, and review policy outcomes.
- Document the execution origin for local and remote MCP connections.
- Provision isolated compute, narrow filesystem permissions, and an outbound network allowlist.
- Keep high-value credentials outside generated code and use least-privilege tokens.
- Verify MCP server identity, transport, initialization, and shared-data scope.
- Define bounded errors for malformed input, unavailable capabilities, timeouts, denials, and tool failures.
- Require approval for sensitive actions and boundary crossings.
- Log policy, approval, execution, MCP, and network decisions without raw secrets.
- Review tools, permissions, endpoints, and data flows whenever a server or agent definition changes.
The result is not a model that is trusted to behave perfectly. It is a runtime that remains in control when the model is mistaken, the request is malformed, a dependency disappears, or an otherwise valid operation exceeds its authority.
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