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Read-only limits what an agent can change. Approval required pauses a specified action until a person or policy authorizes it. Full access is not a universal standard: in some products it means broad workspace and tool rights, while in others it removes both sandbox and approval barriers. These controls can be combined, so an agent may read data freely, edit only a working folder, and still need approval to send, delete, publish, or access a network.

Choose permissions by examining the actual boundary—identity, tools, files, cloud resources, network, credentials, approvals, logging, and recovery—not by trusting a mode name alone.

What the three labels actually control

Read-only is a capability constraint

A read-only agent can inspect the data exposed to it but cannot write through that constrained interface. It may search an approved knowledge base, inspect source files, analyze a report, or explain a configuration without changing the underlying resource. Read-only does not mean harmless: sensitive records can still be copied into an answer, log, prompt, or external service. Microsoft recommends limiting reads to authorized users and tenants and treating sensitive-data access as a separate policy decision. See Microsoft Learn’s access-pattern guidance.

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Approval required is a decision gate

Approval-required does not describe what the agent is technically capable of doing. It describes when execution must pause. A policy can require approval before sending an email, changing a customer record, calling a non-approved endpoint, deleting a file, or provisioning infrastructure. The agent may have write capability, but the action is not released until the designated user or reviewer approves it. Approval can be limited to one action, a particular resource, or one session.

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Full access is product-specific

“Full access” has no cross-vendor definition. It can mean broad permissions inside a workspace, or—more dangerously—running without a sandbox and without approvals. OpenAI’s Codex documentation uses the latter meaning for its dangerous full-access option, while its Auto-review documentation warns that no environment rules then block malicious code execution, data exfiltration, or credential probing. Read the product’s stated boundaries before treating the label as meaningful: Codex approval and security documentation and OpenAI’s Auto-review article.

Permission layers you must evaluate separately

An agent’s effective authority is the intersection of several controls. A restrictive setting in one layer does not compensate for an open setting in another.

Layer Question to answer Typical control
Application identity Which user, service account, tenant, or role does the agent act as? Scoped role, delegated identity, tenant boundary
Tools Which functions can it call? Allow-list tools; remove send, delete, publish, or provisioning functions when unnecessary
Data and resources Which files, records, cloud resources, and fields are visible? Resource, customer, folder, and field-level scopes
Filesystem sandbox Which paths are readable or writable? Read-only, workspace-write, or isolated temporary directories
Network Which outbound destinations can receive data or commands? Approved endpoints, blocked internet, monitored egress
Credentials Can generated code read tokens, keys, cookies, or environment secrets? Brokered, short-lived credentials kept outside the execution environment
Approval policy Which operations stop for review, who reviews them, and for how long is approval valid? Per-action or per-session approval with an explicit target and scope
Audit and recovery Can you reconstruct and undo an action? Immutable logs, change history, backups, rollback, and incident procedures

OpenAI’s security guidance states the practical risk plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” Apply that warning to every runtime, not only coding agents: OpenAI Sandbox security.

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How read, draft, edit, and execute permissions differ

Separating operations by consequence produces safer workflows than granting one broad role.

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  • Read: allow searches and analysis only over approved data. Sensitive reads should be authorized for the relevant user or tenant.
  • Draft: permit an email, ticket, code patch, or infrastructure plan to be prepared without sending, merging, submitting, or applying it.
  • Edit: constrain changes to a working area, allowed fields, or a non-production resource. Keep a diff or version history.
  • External side effect: sending, submitting, deleting, publishing, changing access, charging money, or updating production should invoke policy checks and commonly explicit approval.

Microsoft’s guidance summarizes the enforcement principle: “The important part is that authorization is enforced outside the model.” The policy service should evaluate the user, tenant, agent, tool, target resource, requested scope, approval requirement, and logging or escalation before the tool runs: Microsoft Learn.

Concrete examples of sensible boundaries

Task Safer baseline Escalation point
Knowledge-base search Read-only access to approved indexed content for the current tenant Request approval or deny when a source is outside the tenant or contains restricted data
Email assistance Read permitted messages and create a draft Require approval immediately before sending, adding recipients, or attaching sensitive files
CRM maintenance Update only allowed fields for an explicitly scoped customer set Review bulk changes, ownership changes, deletion, or records outside the customer scope
Infrastructure operations Inspect plans and logs; use a narrow non-production role Require a separate role and human review before production changes, access-policy edits, or resource deletion
Code agent Read repository files and write to an isolated workspace Review network access, package installation, credential use, and changes destined for a shared or production branch

Approval workflows: human-in-the-loop versus agent-only

Human-in-the-loop

In a human-in-the-loop design, the agent proposes an action and a person approves each gated operation. The reviewer should see the exact tool, arguments, target, data scope, expected effect, and any generated content—not merely an “Allow” button. Google Cloud notes that a user can still approve a malicious or destructive suggestion without sufficient verification, so approval is a checkpoint, not a security guarantee: Google Cloud AI security and safety guidance.

Agent-only execution

Agent-only mode proceeds without waiting for a person. Security then depends more heavily on independently enforced permissions, safe tool design, isolation, validation, and error handling. Prompt injection, insecure tool chaining, and faulty assumptions can turn a legitimate instruction into an unintended side effect. Autonomous operation can be appropriate for low-impact, reversible tasks, but it needs tighter technical boundaries than a simple role assignment.

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Designing a useful approval prompt

  1. Show the initiating request and the agent’s proposed action.
  2. Name the tool, identity, target resource, fields or paths, and data leaving the environment.
  3. State whether approval covers one call, a bounded batch, or the rest of a session.
  4. Run the policy check again immediately before execution; do not rely on a decision made when the task started.
  5. Record the reviewer, timestamp, policy result, tool arguments, outcome, and any error.

What full access means in a Codex-style implementation

One documented implementation illustrates why labels must be read literally. A read-only sandbox can be paired with on-request approval for actions outside the sandbox. A writable workspace can still require approval for network access or operations outside that workspace. A dangerous full-access option removes sandboxing and approvals. The exact flags and behavior are product-specific and may change, so verify the current documentation before using them: OpenAI Agent approvals and security.

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--sandbox read-only --ask-for-approval on-request

The command above is an example of a documented configuration, not a universal syntax for other agents. Treat a writable workspace as a narrower capability than unrestricted execution, and treat unrestricted execution as a high-risk exception requiring an isolated environment, test data, and a recovery plan.

Sandbox, network, and credential safeguards

Constrain the runtime

OpenAI recommends isolating workloads, limiting writable paths, and restricting outbound traffic to approved endpoints. A sandbox reduces the blast radius of a faulty command, but it does not make unsafe instructions safe if the sandbox contains sensitive files or a route to a powerful service. Keep application and third-party credentials outside the agent environment where possible: OpenAI Sandbox security.

Use least privilege for identities

Grant only the roles and resources needed for the task. Google Cloud recommends an agent identity with necessary roles rather than a broad administrative identity. Microsoft specifically cautions against granting an Azure Owner role without a defined administrative scenario and strong governance. A narrow role also limits damage when a reviewer approves the wrong action or a tool is manipulated.

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Make actions reversible

Prefer drafts, previews, transactions, staged deployments, soft deletes, and versioned changes. Require a backup or rollback path before allowing an irreversible operation. Recovery is part of permission design: an action that can be undone safely has a different risk profile from one that permanently destroys data or changes access for an entire tenant.

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Log the complete chain

Record the original request, agent decision, policy check, approval, tool execution, resource touched, and outcome. OpenAI describes managed configuration, constrained execution, network policies, and agent-native logs as complementary controls; logging alone does not stop an action, but it enables detection, investigation, and recovery: Running Codex safely at OpenAI.

A least-privilege setup process

  1. Define the job. Write down the intended outcome, data sources, tools, side effects, and whether the task is production-impacting.
  2. Start with no write access. Test the agent with read-only data and a blocked network. Confirm that it can answer the task without hidden dependencies.
  3. Add the smallest writable area. Provide a temporary or workspace directory, limited fields, or a staging project rather than an entire repository, database, or cloud account.
  4. Separate drafting from execution. Expose functions that prepare a message, patch, plan, or change request before functions that send, merge, apply, or publish it.
  5. Set explicit approval rules. List the exact tools, resources, thresholds, and users that require approval. Do not use a blanket approval for an entire session when a single action is sufficient.
  6. Broker secrets. Use short-lived, purpose-limited credentials and prevent generated code from reading unrelated environment variables, token files, or mounted home directories.
  7. Restrict egress. Allow only documented endpoints and inspect data sent to them. Block arbitrary downloads and package registries unless they are required and controlled.
  8. Test abuse and failure. Try prompt injection, malformed tool arguments, timeouts, partial updates, and a reviewer approving a harmful suggestion. Verify that policy enforcement still blocks the operation.
  9. Instrument and rehearse recovery. Confirm that logs connect request to outcome and that operators can revoke credentials, disable tools, restore data, and identify affected resources.
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Choosing a permission mode

Use case Recommended starting point Why
Summarizing approved documents Read-only, scoped to the relevant tenant or folder No write side effect is needed, while data exposure remains bounded
Preparing emails, tickets, or code patches Read plus draft/write in an isolated area; approval before send or merge The useful work happens before the irreversible action
Routine reversible maintenance Narrow write role with policy checks and bounded approvals Automation is possible without granting unrelated authority
Production infrastructure changes Plan-only by default; separate role and explicit review for apply Blast radius and recovery requirements are high
Experiments in disposable environments Broader access only inside an isolated, credential-free test environment Containment reduces consequences, but does not eliminate code or data risks

Common mistakes and how to correct them

“Read-only means no risk”

Correction: scope sensitive reads, redact outputs, and prevent the agent from forwarding retrieved content to unapproved tools or endpoints.

“A human approved it, so it is safe”

Correction: show meaningful context, enforce identity and resource limits outside the model, and make approval narrow and auditable. A person can approve a destructive suggestion.

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“The sandbox protects credentials”

Correction: do not mount credentials the agent does not need. Code running inside the sandbox can read any credentials, files, and network destinations available to that environment.

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“Full access is just read-only plus write”

Correction: check whether the mode also removes network restrictions, sandbox boundaries, approval gates, or identity limits. Those are independent controls.

“A broad administrator role is simpler”

Correction: create a task-specific role and resource scope. Broad roles make mistakes, prompt injection, and compromised tools harder to contain.

How to interpret vendor statistics

OpenAI’s 2026 Auto-review article reports that Codex sessions stopped for human approval roughly 200 times less often in Auto-review mode than in manual approval mode, and that Auto-review approved around 99% of the small fraction of actions routed for review. In an illustrative internal snapshot, 720 out-of-sandbox actions would have interrupted a user under manual approval; Auto-review rejected 7, continued 4 through a safer path, and stopped 3 for user input. These are vendor-reported internal results, and OpenAI says the ratios depend on the use case, environment, and sandbox configuration. They are not universal safety or accuracy benchmarks and should not substitute for a permission review: OpenAI Auto-review.

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Policy requirements versus defaults

Some managed products distinguish non-overridable policy requirements from defaults that establish starting values within those requirements. OpenAI’s Help Center describes that distinction for particular ChatGPT Work and Codex global, local, and cloud environment settings. Do not assume the same precedence model exists in another product; identify which administrator, workspace, environment, and local settings actually govern the agent you are configuring: OpenAI Help Center.

Bottom line

Use read-only when the agent only needs to inspect; add narrowly scoped writes for drafts or work-in-progress; require approval before consequential side effects; and reserve broad or unrestricted access for isolated, well-instrumented environments. The safe choice is not a label—it is the combination of least-privilege identity, constrained tools and resources, sandbox and network controls, protected credentials, independently enforced policy, meaningful approvals, complete logs, and a tested recovery path.

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