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An AI coding agent follows a repository’s rules only as far as the rules are enforced. Repository instruction files give the agent useful context, but they do not stop an action on their own. A workable setup combines three things: repository-specific context, runtime limits on what the agent can touch, and logs that record each prompt, approval decision, tool result and network decision so a reviewer can reconstruct what happened and why.
The guidance below draws on OpenAI’s published material about Codex and its API tooling, including its May 8, 2026 article on running Codex safely internally. Other coding agents may expose different controls, so treat the feature names here as Codex and OpenAI examples rather than a standard that every product shares.
Start with repository context, and treat it as guidance
OpenAI’s description of the Codex agent loop explains that repository instructions can be drawn from files such as AGENTS.md, including files found along the project path. This is the practical way to tell an agent how a codebase is built, which test commands matter, which directories are sensitive, and which conventions to follow. The benefit is relevance: the agent starts from the repository’s own assumptions instead of generic habits.
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#1 Best Overall
Build a threat model specific to the codebase
Codex Security is an example of repository-aware analysis. According to OpenAI’s Codex Security help article, it builds a threat model tailored to the codebase. That model identifies attacker entry points, trust boundaries, sensitive data and important code paths. Teams can inspect and edit it so it reflects their deployment assumptions, such as which services are internet-facing or which data stores are regulated.
Feature access, preview status and commercial terms for Codex Security change over time. Check the help article for current availability before planning a rollout around it.
Rank #2
Separate the sandbox from the approval policy
The sandbox and the approval policy are often confused, but OpenAI describes them as complementary controls, as set out in its article on running Codex safely. The sandbox defines the technical execution boundary, such as which files the agent can write and whether it can use the network. The approval policy decides when the agent must stop and ask before acting beyond that boundary.
| Control | What it decides | Question it answers | What it does not decide |
|---|---|---|---|
| Sandbox | Technical limits on file writes and network access | Could the agent write outside the checkout or reach an unapproved host at all? | Whether a permitted action is wise in context |
| Approval policy | When the agent must ask before crossing its allowed boundary | Does this step need a person to say yes? | How far the process can reach if an approval is granted |
| Managed configuration | Consistent requirements applied across an organization | Do all developers and projects get the same baseline? | Repository-specific intent, which needs local instructions |
| Network policy | Which destinations are allowed, blocked or escalated | Is this outbound destination expected for this project? | Which credentials the agent holds |
Managed configuration and network policy are described in the same OpenAI article. Network policy can allow expected destinations while blocking or escalating unfamiliar ones, which turns network access from an all-or-nothing choice into a reviewable decision.
Put checks at consequential tool boundaries
Not every tool call needs a gate. Checks belong where an action can change files outside the working tree, open a network path, use credentials or touch production systems. OpenAI’s guardrails guidance for agents, which discusses authorized cybersecurity workflows specifically, describes a pattern you can adapt to coding agents:
- Check the proposed target and action against the defined scope of the task.
- Pause ambiguous or high-risk actions for explicit approval rather than letting them proceed.
- Record the decision and the execution outcome.
- Fail closed: if review times out or becomes unavailable, the action does not go ahead.
The fail-closed step is the one teams most often skip. A reviewer who is offline should block the action, not default it to allowed. Source: OpenAI’s guardrails and human review guide.
Rank #4
Make decisions reconstructable
Ordinary system logs show that a process started and exited. For a coding agent, a reviewer also needs to know why the agent ran a command, who approved it and what came back. OpenAI’s article on running Codex safely says Codex can export OpenTelemetry events of an agent-aware kind, and that these can be centralized in SIEM and compliance logging systems.
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- User prompts that started or steered the session.
- Tool approval decisions, including what was approved and what was denied.
- Tool execution results.
- MCP server usage.
- Network proxy allow and deny events.
Retention periods, access controls on the log store and alerting rules are your decisions. The OpenAI material establishes that these events can be exported and centralized; it does not set how long to keep them.
Best Value
Use the events in a review
When a change is questioned, a reviewer should be able to answer four questions from the logs alone: what the agent was asked to do, which actions it proposed, which of those were approved or denied and by what route, and what the environment actually did. If any of those answers depends on memory or a chat transcript, the audit trail is incomplete.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Limit what the agent can reach, especially credentials
OpenAI’s sandbox security documentation emphasizes that generated code can access the files, credentials and network made available to its environment. The practical consequence is that the agent’s exposure is the sum of what you mount into it.
- Isolate the compute the agent runs on from production infrastructure.
- Mount only the directories the task needs, not the whole home directory or a shared volume.
- Keep long-lived or broadly scoped application credentials outside the execution environment where possible, and give the agent narrowly scoped credentials when it must authenticate.
- Restrict outbound network paths to the destinations the project expects, and review anything that falls outside that list.
Keep humans in the fix loop
In the documented Codex Security workflow, validated vulnerabilities are checked in an isolated environment and proposed fixes are presented for human review rather than applied automatically, according to the Codex Security help article. Routing those fixes through your ordinary pull request and code review process keeps the same people accountable for changes whether a human or an agent wrote them.
Evaluate a setup against six questions
Use these axes to compare any coding-agent configuration, including one built on a different product.
| Axis | Question to ask |
|---|---|
| Repository context | Can maintainers supply local instructions and adjust assumptions for this codebase? |
| Enforcement boundary | Are filesystem, network, identity and project permissions constrained independently? |
| Approval behavior | Which actions are permitted automatically, blocked, or held for review, and what happens if review is unavailable? |
| Decision evidence | Are tool activity, approvals, outcomes and policy decisions retained in a form reviewers can query? |
| Human review | Do validated findings and patches go through the team’s normal review path? |
| Credential and network exposure | What can the agent reach, and are credentials scoped and kept outside its execution context? |
A configuration that scores well on repository context but cannot show who approved a network call has not met the goal of making its decisions visible.
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