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An AI agent is a system in which a model directs its own multi-step process: it plans, selects tools, acts, observes results, adjusts its approach, and repeats until it completes the task or needs a person. That is different from a conventional workflow whose code follows a fixed sequence, even though products often use the word agent for both.
Agents become useful when they can do more than generate text, but that capability also creates risk. A tool call can read data or trigger an operation; permissions decide which calls are allowed; sandboxes limit the damage if something goes wrong; and human review provides judgment for consequential decisions. No one layer is sufficient by itself.
What makes a system an AI agent?
“Agent” has no single industry-wide definition. A practical distinction is whether the model dynamically directs the work and tool use, rather than merely filling in steps chosen in advance by application code.
Workflow orchestration
In a workflow, developers define the path. Code might send a document to a classifier, pass the result to a formatter, and save the output. The model may perform one or more steps, but the surrounding program determines what happens next. This approach is usually easier to test because the possible paths are known.
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Model-directed agency
In an agent, the model decides what to do next within the capabilities and limits supplied by the application. Anthropic describes this as a self-directed loop: the model plans, takes an action, observes the result, revises its plan, and continues until it finishes or requests human input. The loop can branch when a search returns unexpected information, a command fails, or a task requires a different tool.
Dynamic control does not mean unlimited independence. An agent can only affect resources exposed through its tools and permissions. One product may let an agent inspect files but not change them; another may allow changes after approval; a third may run a routine operation automatically. Always ask what a particular product is permitted to do rather than assuming that every “agent” has the same autonomy.
How agents use tools
A tool is an interface that lets the model obtain information or request an operation from software or a service. Examples include a file reader, database query, calculator, browser, ticketing API, code executor, or deployment command. The model chooses a tool and supplies arguments in the format the application defines.
The tool-call cycle
- Interpret the goal. The model turns the user’s request and available context into a working objective.
- Plan a next step. It selects a tool whose stated purpose appears relevant and constructs parameters such as a file path, query, record ID, or command.
- Request execution. The host application validates the request, applies permission rules, and either runs the tool or asks for approval.
- Observe the result. The tool returns data, an error, or a status message to the model.
- Update the plan. The model interprets that response and decides whether to continue, use another tool, retry with corrected arguments, or stop.
The model’s choice is not the same as the operation’s implementation. The tool code determines what a call actually does. An agent can hallucinate a tool that does not exist, misunderstand a tool’s scope, select the wrong one, provide unsafe or malformed parameters, or misread the response. A correctly implemented tool therefore improves the boundary around an action, but it does not make the agent’s decision reliable by itself.
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Designing safer, clearer tools
Tool descriptions should state a precise purpose, required inputs, limits, side effects, and expected output. “Manage files” is ambiguous; a description that distinguishes listing, reading, creating, overwriting, and deleting gives the model and the host application clearer boundaries. Return structured errors that identify whether a request was denied, invalid, or temporarily unavailable.
Builders should evaluate how agents actually call tools, not only whether each tool works in isolation. Tests should include wrong-tool selection, missing arguments, surprising results, permission denials, repeated retries, and untrusted text that attempts to redirect the model. Logging the requested tool, parameters, approval decision, result, and follow-up action makes those evaluations possible.
What permissions control
Permissions define which data an agent can read and which operations it can request or trigger. They can be attached to a user, workspace, service account, tool, file system, network destination, or individual action. A permission decision is separate from the model’s confidence: an agent may be certain it should perform an action and still be denied.
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- Data access: Which files, messages, databases, secrets, or customer records can the agent read?
- Write and execution rights: Can it create, edit, delete, send, purchase, deploy, or run code?
- Time span: Is access granted for one call, one session, or persistently for future tasks?
- Approval: Which actions require a person to approve them first?
- Scope: Is access limited to a project or directory, or does it extend across an account or organization?
- Network reach: Which hosts and services can the agent contact?
- Evidence: What does a reviewer see before approving, and what is recorded afterward?
A vendor-specific example
Anthropic’s August 4, 2025 safety framework describes Claude Code as read-only by default and asking for approval before actions that modify code or systems. It also describes persistent permissions for trusted routine tasks. These are Claude Code behaviors, not universal defaults for AI agents. A different product can choose different defaults, so its permission documentation and administrative settings must be checked directly.
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Human approval: useful gate, imperfect safeguard
Approval prompts keep a person in the decision loop for actions with meaningful consequences. A reviewer can reject an unexpected file change, an external message, a financial operation, or a deployment. The quality of that control depends on what the reviewer can see: the proposed action, affected resources, parameters, reason, and likely side effects should be presented in understandable terms.
Prompts can also become routine. Anthropic reported in its May 25, 2026 account of Claude containment: “Our telemetry showed users approved roughly 93% of permission prompts.” That figure describes Anthropic’s own Claude Code telemetry, not a universal approval rate. It illustrates approval fatigue: when prompts appear constantly and usually look harmless, people may approve without careful inspection.
Reducing approval fatigue
- Ask only for actions that cross a meaningful risk boundary, rather than every harmless read.
- Group a clearly defined set of routine operations, while keeping destructive or external actions separate.
- Show exact targets and parameters instead of a vague “allow agent to continue” message.
- Make denials informative so the agent can choose a safe alternative instead of repeatedly asking.
- Require renewed approval when the target, scope, or side effect changes materially.
- Review approval logs for patterns of automatic acceptance and adjust the policy.
Approval is a judgment aid, not proof that the action is safe. A person can misunderstand a request, approve a malicious instruction embedded in a document, or overlook a dangerous parameter.
Containment: limiting what a mistake can affect
Containment places an enforcement boundary around the agent. Sandboxes, virtual machines, separate accounts, file-system restrictions, and network egress controls can limit the consequences of an incorrect decision or compromised tool.
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Anthropic’s May 25, 2026 engineering account gives a Claude Code configuration in which the agent could read and write within a workspace while network access was denied by default. That is an example of one vendor’s engineering setup, not a rule that every agent should use the same boundaries. In another environment, an agent might need access to a specific package registry or internal API; those destinations should be allowlisted deliberately rather than opened broadly.
Containment checks
- Run the agent with the smallest practical operating-system and service-account privileges.
- Mount only the directories and data sources required for the task.
- Separate credentials for development, testing, and production; do not expose long-lived secrets when short-lived tokens will work.
- Restrict outbound network destinations and inspect uploads, downloads, and callbacks.
- Set CPU, memory, storage, execution-time, and retry limits to prevent runaway loops.
- Preserve an audit trail outside the agent’s writeable area so it cannot erase its own history.
Containment reduces potential impact; it does not make an agent’s reasoning correct. A sandbox can still permit harmful actions inside its boundary, and an exposed internal service can be valuable even when the public internet is blocked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Additional security risks
Anthropic’s 2026 submission responding to a NIST request for information identifies several agent-security concerns. It is a vendor submission, not a NIST finding, and it does not establish how frequently these attacks succeed.
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Untrusted content can contain instructions aimed at the model rather than the user’s task. A web page, issue ticket, email, or document might tell the agent to reveal secrets, ignore its original objective, or call a different tool. Treat retrieved text as data, not as an authority, and keep high-impact tools behind independent policy checks.
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Persistent memory poisoning
If an agent stores notes, preferences, summaries, or task history and later trusts them, an attacker may try to plant misleading instructions that persist across sessions. Memory should have ownership, provenance, retention, and review rules. Do not give every piece of remembered text the same authority as a current user instruction or administrator policy.
Tool supply-chain risk
A tool, plugin, connector, package, or service can introduce vulnerabilities or behave differently after an update. Inventory tools, pin and review versions where practical, restrict their permissions, and monitor changes. A model’s description of a tool is not an independent security audit of the tool implementation.
How to keep an agent from taking unintended actions
A layered design is more dependable than relying on either the model or a single control. Use this sequence when deploying an agent that can affect real systems.
- Define the task boundary. Write down the allowed goal, data sources, side effects, and explicit exclusions. Separate “draft a response” from “send the response.”
- Expose narrow tools. Prefer separate read, create, update, and delete operations with strict schemas over one unrestricted command tool.
- Start read-only. Let the agent inspect information and produce a proposed plan before granting write or execution rights.
- Apply least privilege. Use a dedicated identity and restrict files, records, APIs, and network destinations to what the task needs.
- Choose approval points. Gate irreversible, external, financial, privacy-sensitive, or production actions. Keep low-risk, repetitive steps automatic only when their scope is stable.
- Contain execution. Use a sandbox or isolated machine, resource limits, and controlled network egress. Verify that the boundary is enforced by the host, not merely described in a prompt.
- Validate before committing. Check paths, recipients, quantities, diffs, query scope, and policy conditions independently of the model.
- Log and observe. Record prompts, tool calls, parameters, approvals, denials, outputs, retries, and final effects. Alert on unusual destinations, volume, or repeated failures.
- Provide a stop path. Make it possible to revoke credentials, terminate execution, roll back changes, and hand the task to a person.
How to compare an agent configuration
There is no neutral market-wide ranking implied by the examples above. Compare the controls that matter to your task:
| Comparison area | Questions to ask | Why it matters |
|---|---|---|
| Readable data | Which directories, records, messages, and secrets are visible? | Read access can expose confidential information even when writes are blocked. |
| Actions | Can the agent edit, delete, execute, send, purchase, or deploy? | Different side effects require different approval and rollback plans. |
| Permission duration | Are grants per call, per session, or persistent? | Persistent access can make routine work easier but broadens the window for misuse. |
| Isolation | Does execution use a sandbox or virtual machine, and what can escape it? | Isolation limits the blast radius of mistakes and compromised tools. |
| Network access | Is outbound traffic denied, unrestricted, or allowlisted? | Network reach affects data exfiltration and which external systems can be changed. |
| Human review | Which actions prompt, what context is shown, and can denials be escalated? | A prompt is useful only when a reviewer can make an informed decision. |
| Auditability | Are calls, results, approvals, and changes recorded outside the agent? | Logs support incident response, debugging, and policy improvement. |
Vendor safeguards still need independent checks
Anthropic describes an “auto mode” for Claude Code that adds a review layer intended to judge tool actions against user intent and stop or escalate after repeated denials. This is a vendor-specific safeguard. A classifier or approval mechanism can misjudge context, so it should complement—not replace—least privilege, containment, logging, and human escalation.
The same principle applies to any product claim about autonomy or safety. Ask what the host enforces, what the model merely recommends, how failures are handled, and whether you can inspect and revoke access. Defaults can change between editions and releases; verify the current behavior in the product’s documentation and your own environment.
Bottom line
AI agents work by connecting model-directed planning to tools in a repeated plan–act–observe loop. Tools provide capabilities, permissions define the allowed scope, containment limits the consequences, and people review decisions that require judgment. Because agents can choose the wrong tool, misread data, or follow hostile instructions, dependable deployments combine all of these layers rather than treating approval, a sandbox, or the model’s own judgment as a complete safety solution.
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