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Set up an AI agent for one narrowly defined, low-impact task first, and limit its permissions outside the prompt. Give it only the data, tools, credentials, and network destinations that task requires; require a person to review consequential actions; and test the complete workflow—including exceptions and hostile inputs—before expanding its scope.

1. Define one task and its boundaries

Choose a repetitive task with a clear completion condition and limited consequences. Before configuring an agent, write down what starts the workflow, what inputs it may use, what it should produce, which actions are allowed, and which conditions require escalation.

  • Trigger: What event starts a run?
  • Inputs: Which files, records, or messages may it read?
  • Output: What should a successful run produce, and where?
  • Allowed actions: What may the agent do without additional authority?
  • Prohibited actions and exceptions: What must it never do, and what should make it stop for review?

A narrow job is easier to test and to limit. Avoid bundling unrelated tasks into the first workflow; each added task can require new data access, tools, or permissions.

2. Restrict access with technical controls

Do not rely on instructions in a prompt as the main security boundary. An agent can act on resources exposed to its runtime, and text it encounters may attempt to redirect it. OpenAI describes the sandbox as defining where Codex can write, whether it can reach the network, and which paths remain protected in Running Codex safely at OpenAI. Google Cloud recommends a separate agent identity with only the roles and permissions required for the job in its AI security and safety documentation.

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Use a separate identity and minimum permissions

Create a dedicated identity or credential for the workflow rather than reusing a personal or broadly privileged account. Grant only the permissions needed for the defined task. Keep unrelated sensitive files and secrets out of the agent’s runtime, and do not expose credentials merely because a future task might need them.

Isolate execution and limit network access

If the task runs code or uses a workspace, use an isolated execution environment where available. Keep trusted application services separate from the execution environment where practical, and restrict outbound connections to destinations the task actually needs. Anthropic warns that untrusted input can exploit network access available to an agent; its guidance on mitigating jailbreaks and prompt injections also recommends sandboxing tools and narrowly scoped permissions.

Keep the controls in the environment itself: a prompt that says “do not access other files” cannot substitute for making those files inaccessible.

3. Decide which actions need human approval

Separate routine, low-impact actions from actions that could have significant or irreversible consequences. Depending on the task, it may be reasonable to automate bounded, reversible work while pausing before consequential actions. Configure approval behavior for each tool, and check what its defaults actually are: policy labels and behavior vary by platform, and an automatic mode is not a human checkpoint.

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A useful approval request should show the proposed action and enough context for a reviewer to assess it. Human review only helps when the reviewer checks what the agent is about to do; Google Cloud cautions that reviewers can still approve malicious actions without adequate scrutiny.

4. Treat documents and tool output as untrusted

Files, web pages, messages, and tool results can contain instructions intended to change the agent’s behavior. Do not let arbitrary external text directly determine a tool call. Instead, extract the expected structured fields, validate them against the task’s rules, and reject or escalate unexpected values.

For example, if an agent processes a request, define which fields it may use and which actions each valid value permits. A request’s free-form text should not be able to grant new permissions or alter the workflow. Technical limits—restricted tools, isolated execution, and narrow network access—reduce the possible damage if an injection still succeeds.

5. Test the entire workflow before relying on it

Test not just whether the model produces a plausible answer, but whether the agent and its tools behave correctly together. Include ordinary runs, exceptions, malformed inputs, and attempts to redirect the agent through external content.

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  • Check that successful cases produce the expected output in the right place.
  • Confirm that missing, conflicting, or malformed data triggers the intended stop or escalation.
  • Verify that an approval is requested before each action designated for review.
  • Try adversarial content and confirm it cannot expand access or bypass the workflow’s limits.
  • Inspect tool calls and outcomes, not only the final response shown to a user.

OpenAI’s guidance on agent evaluations describes evaluating decisions and tool calls, including with trace graders. Its Agent Builder safety documentation discusses agent-aware telemetry for understanding what happened. OpenAI says Agent Builder is scheduled to shut down on November 30, 2026, so verify its current transition status rather than treating it as a durable setup choice. For workflows that need files, commands, artifacts, or resumable state, the Agents SDK sandbox documentation describes a distinct sandbox path.

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6. Roll out gradually and keep reviewing

Start with a small deployment and review failures, tool activity, approval decisions, and outcomes. Expand the task or its permissions only when evidence from the workflow supports doing so. Recheck access rights and allowed network destinations periodically, especially when the task, its inputs, or its connected tools change.

Keep logs of prompts, tool calls, approval decisions, and results where appropriate, while setting privacy and retention rules suited to the data involved. The right level of logging and retention depends on the workflow and platform; do not collect or retain sensitive material without a reason.

7. Compare platforms on the controls that matter

There is no universal platform choice established by the official guidance. Before choosing a product or architecture, compare these practical controls and verify current documentation, since defaults and feature availability can change.

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What to compare Why it matters
Tool and identity permissions Can access be limited to the specific actions and resources this task needs?
Execution isolation and network policy Can the workflow isolate file or command execution and restrict outbound connections?
Approval behavior Can approval be configured for each tool, and are the defaults clear?
Evaluation and traces Can you inspect decisions, tool calls, and outcomes, and test the workflow systematically?
Credential, data, and retention handling Can you keep secrets out of the runtime unless needed and manage data retention appropriately?

Anthropic’s managed-agent permission documentation labels that feature beta; check the current status and behavior before depending on it: Claude Code IAM documentation.

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