Safe AI-agent deployment requires three separate controls: permissions decide which tools and actions exist, sandboxing limits what agent-generated code can technically reach, and approval policy determines whether a tool call runs immediately, is denied, or pauses for a person. They reinforce one another, but none replaces the others—and asking a model to “be careful” is not an enforcement boundary.
This guide explains how the layers work, how documented OpenAI and Anthropic systems implement them, and how to design a setup that limits blast radius while preserving useful automation.
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What each safety layer controls
Start by separating the decisions that are often bundled under “agent safety.” A tool can be unavailable, available but technically constrained, or available and waiting for approval. Those are different states.
| Layer | Question it answers | Typical controls |
|---|---|---|
| Permissions | What may the agent call? | Enable or disable tools, select toolsets, assign per-tool policies, limit file operations or commands. |
| Sandbox | What can the executing code reach? | Readable and writable paths, isolated compute, network egress, protected directories, available environment variables and credentials. |
| Approval policy | When must a call stop for a decision? | Always allow, always ask, automatic evaluation, denial, or escalation to a human. |
| Audit and monitoring | Can you reconstruct what happened? | Prompts, decisions, tool results, MCP activity, and network allow/deny events. |
OpenAI summarizes the relationship as “Approvals and sandboxing work together.” Sandboxing establishes a technical boundary; approval policy controls when an action must stop for review. See OpenAI’s Codex deployment guidance.
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Why a sandbox is an execution boundary
Agent-written code runs with the access exposed by its environment. If the environment can read a directory, reach a network destination, or see an environment variable, generated code may be able to use it. A prompt instruction cannot reliably remove that capability.
Files and data
Define the smallest set of paths needed for the task. Separate repositories, customer tenants, and production data when they should not share a process. Use isolated virtual machines or equivalent compute for workloads that require stronger separation. OpenAI’s sandbox security guidance recommends isolated compute for workloads that should not share data.
Network egress
Choose an explicit outbound policy: no network, unrestricted network, or an allowlist of approved destinations. An allowlist reduces the chance that generated code uploads data or retrieves untrusted instructions. Treat DNS, package registries, webhooks, and proxy endpoints as part of the egress design, not as afterthoughts.
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Keep high-value application keys outside the agent’s execution environment. OpenAI’s example supplies an executor environment key that connects environments while keeping the application API key outside. For third-party services, broker real credentials through a proxy that permits only approved hosts and operations. A secret injected directly into the environment is still readable by agent-generated code; moving it from a file into an environment variable does not make it inaccessible.
Permissions: remove capabilities before evaluating calls
Permission configuration is the first reduction in attack surface. Disable tools the workflow does not need instead of enabling everything and relying on later review.
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Tool availability versus run policy
Anthropic’s Managed Agents documentation makes the distinction explicit: disabling a tool removes it from the agent, while a permission policy governs when an enabled tool runs. A file-writing tool that is disabled cannot be approved later; it is absent. An enabled tool with an always_ask policy exists but pauses before execution.
Design a least-privilege inventory
- List every tool, MCP server, command runner, file operation, and network integration.
- For each, record the data it can read, the systems it can change, and whether the action is reversible.
- Disable unused tools and split broad tools into narrower operations where the platform allows it.
- Give read-only access for discovery and analysis tasks; add write access only for a defined step.
- Use separate agent identities or environments for development, staging, and production.
Approval policy: allow, ask, deny, or evaluate
Approval is a decision point, not a substitute for technical isolation. A human gate is appropriate when an operation has material consequences, while automatic evaluation can reduce interruptions for lower-risk work.
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Always allow
The call executes without confirmation. This is suitable only for narrowly scoped, low-impact operations inside a constrained sandbox. Anthropic documents always_allow as one of its Managed Agents policies; its agent-tool permissions default to this policy, while MCP toolsets default to always_ask on that documented beta API.
Always ask
The system pauses before every call covered by the policy. Use this when a person must inspect the proposed action before execution. Anthropic’s documentation states that if a person must review a call first, configure always_ask.
Automatic evaluation
An evaluator classifies each call as safe to run, risky and denied, or indeterminate and requiring user input. This reduces friction but is probabilistic. Anthropic’s warning is concise: “auto is not a human checkpoint.” An automatically approved call may execute before a person sees it.
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Denial and overridability
Document whether a denial is final or whether a user, administrator, or retry can override it. A policy that merely asks the model to reconsider is weaker than a platform-enforced denial. Repeated blocked attempts can themselves indicate prompt injection or a misunderstood workflow and should be logged.
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Anthropic Claude Code Auto mode
Anthropic’s Claude Code Auto mode announcement says the default permissions ask before every file write and bash command. Auto mode checks each tool call with a classifier: calls judged safe proceed, risky calls are blocked, and repeated blocked attempts can prompt the user. Anthropic describes the mode as reducing risk compared with skipping all permission checks, not eliminating risk. It may miss a risk when intent or context is ambiguous, may block benign actions, and remains recommended for isolated environments. The announcement was published March 24, 2026 and updated July 10, 2026 to report general availability; behavior can change, so verify the current documentation before deployment.
Anthropic Managed Agents
The Managed Agents permission-policy documentation defines always_allow, always_ask, and auto. Enabled tools can receive per-tool overrides. These settings apply to that documented beta API, not automatically to every Anthropic product or workspace.
OpenAI sandboxing and Codex approvals
OpenAI’s sandbox documentation says agent-generated code can access the files, credentials, and network available to its environment. It recommends isolated compute, outbound allowlists, and separate credential handling. OpenAI’s Codex deployment article describes sandboxing as the technical boundary and approvals as the mechanism that asks before an action crosses that boundary. It also describes exporting OpenTelemetry events for prompts, approvals, tool results, MCP server usage, and network-proxy allow/deny decisions. Those are documented deployment controls, not universal defaults for every Codex setup.
OpenAI workspace policy hierarchy
The Agent Security help article describes a global policy baseline plus local and Codex Cloud environment controls. A local or Codex Cloud environment without an override inherits the applicable global policy. Codex Cloud settings do not automatically apply to Work Cloud, and local MDM and legacy managed-device requirements take priority over Agent Security. Keep this hierarchy scoped to that OpenAI workspace policy system.
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How automatic review performs—and where it fails
OpenAI’s Auto-review report gives a vendor-reported snapshot from 2026. In the cited deployment, 720 out-of-sandbox actions would have interrupted a user under manual approval. Auto-review examined them: seven were rejected, four continued through a safer path, and three stopped for user input. On synthetic evaluations, OpenAI reports 90.3% overeagerness recall, 99.3% prompt-injection recall, and 96.1% MonitoringBench [Hard] recall.
These figures are not independent benchmarks or security guarantees. OpenAI says ratios depend on the use case, environment, and sandbox configuration, and explicitly cautions that Auto-review is not a guarantee. Its red-teamers found cases in which the reviewer could be misled into approving commands. Treat automatic review as a risk-reduction layer with residual uncertainty, never as proof that a dangerous call cannot run.
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- Map the workflow. Write down the agent’s required inputs, tools, files, destinations, and side effects. Mark actions that change production data, spend money, publish content, or expose personal information.
- Disable unnecessary capabilities. Remove unused tools and MCP servers. Start with read-only access and add one write capability at a time.
- Build the sandbox. Run the agent in isolated compute when workloads should not share data. Mount only required directories, protect host and production paths, and select no network or an explicit egress allowlist.
- Separate secrets. Keep application keys outside the sandbox. Use a credential-brokering proxy for approved third-party hosts, with narrowly scoped tokens and revocation. Do not assume an injected environment variable is hidden from generated code.
- Assign approval policies by consequence. Use always-allow only for low-impact operations. Require always-ask for irreversible or externally visible actions. Use automatic evaluation only where the organization accepts the possibility of false approvals and false blocks.
- Define denial behavior. Decide which denials are final, which can be retried, and who can override them. Make the decision enforceable in the platform rather than in prompt text.
- Log the decision path. Capture the prompt or request, selected tool, arguments, approval result, evaluator result, tool output, identity, environment, and network allow/deny event. Protect logs because they may contain sensitive data.
- Test adversarially. Attempt prompt-injection strings, path traversal, secret discovery, unauthorized destinations, repeated retries, and ambiguous commands. Verify that the sandbox blocks the action even when the model requests it.
- Review after changes. Recheck policies when tools, model versions, network routes, credentials, or workspace settings change. Vendor defaults and availability are platform-specific and can change.
Comparison framework for security reviews
When comparing two agent deployments, record concrete answers rather than labels such as “safe mode.” The following matrix keeps the review focused on enforceable behavior.
| Review axis | Questions to answer |
|---|---|
| Files | Which directories can be read or written? Are host, sibling-tenant, and production paths blocked? |
| Network | Is outbound traffic disabled, unrestricted, or allowlisted? Are DNS and proxy routes covered? |
| Credentials | Where are secrets stored? Can generated code read them? Is access brokered and scoped to approved hosts? |
| Tools | Which tools are enabled? Are MCP servers and command runners separately controlled? |
| Call handling | Does each call always run, always ask, or receive automatic evaluation? |
| Denials | Are blocked calls technically enforced, retryable, or overridable by a user or administrator? |
| Audit | Are prompts, approvals, tool results, MCP use, and network decisions retained and searchable? |
This is a deployment-review framework, not a neutral certification or comprehensive product benchmark. A setting name or default in one product should not be transferred to another.
Common failure modes and fixes
“The model was told not to access secrets.”
Problem: The secret is still mounted, injected, or reachable through a broad filesystem or network route. Fix: Remove the secret from the environment, use a brokered proxy, and restrict paths and egress.
“Auto approval means a person checked it.”
Problem: Automatic evaluators can approve before a human sees the call. Fix: Put consequential operations behind always-ask and reserve automatic evaluation for accepted low-risk residual exposure.
“A sandbox makes approval unnecessary.”
Problem: A sandbox may still contain sensitive project data or permit damaging actions within its boundary. Fix: Combine isolation with least-privilege tools and human gates for irreversible operations.
“A blocked call proves the policy works.”
Problem: The user may be able to override it, or a different route may bypass the policy. Fix: Test alternate tools, retries, proxies, and identities; record whether enforcement is final.
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Problem: Without event history, investigators cannot tell whether a model, user, tool, or network policy made a decision. Fix: Export and protect prompts, approval decisions, tool results, MCP usage, and network decisions.
Quick Recap
Operating checklist
- Every enabled tool has an owner, purpose, data scope, and approval policy.
- Production and unrelated-tenant data are isolated from exploratory workloads.
- Outbound destinations are disabled or explicitly allowlisted.
- Application keys remain outside agent-written code; third-party credentials are brokered.
- Irreversible, high-impact, or externally visible actions require a human gate.
- Automatic review is documented as fallible, with false-approval and false-block response plans.
- Denials are technically enforced and their override path is known.
- Logs cover requests, decisions, results, identities, environments, and network events.
- Policies are retested after tool, model, credential, network, or workspace changes.
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