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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAn AI agent is a system in which a model decides what to do next, selects and calls tools, examines the results, and continues until it completes a task or requires a person. The important engineering questions are not just how capable the model is, but what the tools can change, which permissions are granted, where execution occurs, and when a human must approve or stop an action.
What is an AI agent?
“AI agent” is used inconsistently. A useful definition is a model-directed system that operates in a self-directed loop: it plans, acts, observes the result, adjusts its plan, and repeats until the task is complete or a human decision is needed. Anthropic describes this distinction in Building effective agents (December 19, 2024) and Trustworthy agents in practice (2026).
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That is different from a conventional workflow. In a workflow, application code decides the sequence in advance: call service A, transform its response, then call service B. A model may fill in a step, but the route is predetermined. In an agent, the model can decide which available operation to use, whether another operation is needed, and how to react when the result is unexpected. Some products use “agent” for systems that are partly scripted, so ask what decisions the model actually controls rather than relying on the label.
A concrete example
Suppose an agent is asked to investigate a failed software deployment. It might read the deployment record, inspect recent logs, compare the last successful version, propose a likely cause, and ask whether to roll back. If the user authorizes a rollback, the agent can call the deployment service, check the result, and report whether the site recovered. A fixed workflow could perform the same sequence, but it would not normally choose a different diagnostic path when the first log query is inconclusive.
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How the agent loop works
An agentic system is best understood as several controlled stages rather than as a mysterious autonomous personality.
1. Interpret the goal and constraints
The model turns the request, system instructions, and available context into an intended outcome. Good systems make constraints explicit: which files or records are in scope, what counts as success, what must never be changed, and when to ask the user.
2. Plan a next action
The model selects a next step based on the current state. It may decide to search, read a document, run a calculation, or request approval. Planning can be brief and incremental; an agent does not need to generate a complete plan before every tool call.
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3. Select and parameterize a tool
The model chooses from the tools exposed to it and supplies arguments such as a search term, file path, record identifier, or destination. The application, not the model, should validate those arguments and enforce authorization.
4. Execute and observe
The tool runs and returns a result, error, or denial. The model then interprets that output. A successful call does not prove that the underlying operation was correct; it only means the software accepted and executed the request.
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5. Adjust, stop, or escalate
The model may use another tool, revise its hypothesis, present a draft, or stop because the task is complete. It should escalate when the requested action is outside its permissions, the evidence is contradictory, the consequence is material, or a required approval has not been given.
Loop limits matter. Set maximum steps, time, spending, or tool calls so a confused agent cannot continue indefinitely. Treat a loop limit as a safety boundary, not as proof that the final answer is reliable.
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A tool is an interface through which an agent obtains information or requests an operation from software or a service. Examples include a document reader, database query, calculator, browser, ticketing API, code executor, or deployment control. The model chooses and describes the call, but the tool implementation determines what the call actually does.
Why tool calls can fail
Anthropic’s Writing effective tools for agents — with agents (September 11, 2025) identifies several failure modes:
- The model may hallucinate a tool that does not exist or misunderstand what a real tool does.
- It may select the wrong tool when several have overlapping purposes.
- It may provide an invalid, incomplete, or dangerously broad parameter.
- It may misread a valid response, especially when the response is ambiguous or contains untrusted text.
- The implementation may perform more than the description suggests, or fail to enforce its stated boundaries.
Tool descriptions should state the purpose, required inputs, side effects, limits, and typical errors in plain language. Prefer narrow operations such as read_invoice(id) and draft_refund(id, amount) over a single unrestricted administrative endpoint. Return structured results with explicit status fields, affected resources, and validation errors. Builders should evaluate real traces of tool use, not only whether the model can produce a plausible call in a demonstration.
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Read tools versus action tools
Read-only tools usually reveal information without changing an external system, although the information itself may be sensitive. Action tools create or modify records, send messages, purchase items, deploy code, delete data, or change access. The distinction should appear in both the tool definition and the permission policy. A “preview” or “draft” operation is safer than combining inspection and execution in one call.
What permissions determine
Permissions define which resources and operations an agent can access. They are separate from the model’s apparent intelligence: a highly capable model cannot safely perform an operation that the enforcement layer denies, while a poorly bounded tool can make a mistaken model decision consequential.
Permission dimensions to specify
| Question | Examples | Why it matters |
|---|---|---|
| What data can be read? | One folder, a project database, or an entire account | Limits exposure if the prompt or tool output is malicious |
| What can be changed? | Draft only, edit existing records, delete, deploy, or send | Defines the maximum direct impact of an error |
| How long does access last? | One call, one session, or a persistent grant | Controls what a later request can reuse |
| Is approval required? | Every action, selected high-risk actions, or none | Places a human decision at the point of consequence |
| Where does it run? | Host machine, container, virtual machine, or sandbox | Restricts files, processes, credentials, and network effects |
| What is recorded? | Tool arguments, outputs, denials, approvals, and identity | Enables investigation, auditing, and recovery |
A vendor-specific example
Anthropic’s Our framework for developing safe and trustworthy agents (August 4, 2025) describes Claude Code as read-only by default and says it asks for approval before actions that modify code or systems. It also describes persistent permissions for trusted routine tasks. Those are Claude Code behaviors, not defaults for AI agents generally. Other products may begin with broader access, require approval for different operations, or provide no approval mechanism at all.
When evaluating a product, verify the actual default policy in the edition and deployment you use. Ask whether a permission covers a single call or an entire session, whether a user can revoke it immediately, and whether administrators can impose stricter limits than an individual user.
Why human oversight still matters
Human review is useful when an action has legal, financial, safety, privacy, or reputational consequences; when the request is ambiguous; or when the agent is operating outside well-tested conditions. The person should see enough context to make an informed decision: the proposed action, target, key evidence, expected side effects, and any uncertainty.
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Approval prompts are not a complete safeguard
Too many prompts encourage people to approve mechanically. In its May 25, 2026 account How we contain Claude across products, Anthropic wrote: “Our telemetry showed users approved roughly 93% of permission prompts.” This is Anthropic’s reported Claude Code telemetry, not a universal approval rate or proof that the approved actions were correct.
Design approvals around risk rather than frequency. Grouping harmless read operations can reduce noise, while a payment, deletion, access change, external message, or production deployment should show a precise confirmation. If a user denies an action, the agent should explain the consequence, offer a safer alternative, and avoid repeatedly asking for the same permission.
Review should be meaningful
- Show the exact resource and scope, not merely “allow tool.”
- Describe what will change and whether the change can be reversed.
- Identify data that will leave the environment or be sent to a third party.
- Provide a preview or diff for edits, messages, and code changes.
- Keep an audit record of the request, decision, tool call, and result.
Anthropic’s description of Claude Code auto mode presents an additional review layer intended to judge tool actions against user intent and stop or escalate after repeated denials. It is a vendor-specific safeguard, and no classifier or approval mechanism should be treated as infallible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Containment: limiting what a mistake can affect
Containment puts an enforcement boundary around the agent. Sandboxes, containers, virtual machines, separate credentials, filesystem restrictions, and network-egress controls can reduce the blast radius of a bad instruction, compromised tool, or mistaken call.
Anthropic’s May 25, 2026 containment article gives a Claude Code example in which reads and workspace writes were allowed while network access was denied by default. That configuration is an engineering example, not a universal prescription. A coding agent may need access to a package registry; a document assistant may need no network access at all. Permit only the destinations and capabilities the task requires.
Containment does not replace authorization
A sandbox can prevent an agent from reaching the host system while still allowing it to corrupt every file inside the sandbox. A read-only token can protect production while exposing confidential data. Human approval can stop a visible action while an earlier read has already disclosed sensitive information. Use containment, permissions, and review together, with independent enforcement where possible.
Security risks specific to agentic systems
In its 2026 submission to a NIST request for information, Anthropic names prompt injection, persistent memory poisoning, and tool supply-chain risk as agent-security concerns. The submission is a vendor response, not a NIST finding, and it does not establish how often these attacks succeed.
Prompt injection
Untrusted text in a webpage, document, email, or issue can contain instructions aimed at the agent rather than information relevant to the task. Separate data from instructions, identify the trust level of retrieved content, and require confirmation before a recommendation derived from untrusted text triggers an external action.
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Persistent memory poisoning
If an agent stores notes, preferences, or retrieved facts for later sessions, an attacker may try to insert a false instruction that persists. Keep provenance with stored items, limit who can write memory, expire sensitive entries, and provide a review and deletion mechanism.
Tool supply-chain risk
A connector, plugin, dependency, or service can be compromised or can expose more authority than its description implies. Inventory tools, pin and update dependencies deliberately, isolate credentials, inspect returned data, and remove unused integrations.
How to keep an AI agent from taking unintended actions
Use the following design sequence for an internal assistant or a production service.
- Define the task boundary. Write the allowed resources, prohibited actions, success condition, escalation conditions, and maximum runtime or number of steps.
- Separate read, draft, and execute operations. Let the agent gather evidence and prepare a change before granting an execution path.
- Apply least privilege. Give each tool only the records, filesystem paths, credentials, and network destinations required for the task.
- Validate every call outside the model. Enforce schemas, ranges, ownership checks, rate limits, and destination allowlists in application code.
- Insert risk-based approvals. Require a person for irreversible, externally visible, expensive, sensitive, or production actions. Show a preview and the evidence behind it.
- Run in a contained environment. Use a sandbox or virtual machine where practical, deny unnecessary network egress, and prevent access to host credentials.
- Log and monitor. Record identity, prompt context where permitted, tool arguments, outputs, approvals, denials, and final effects. Alert on unusual volume or repeated failures.
- Test adversarially. Feed the system malicious documents, ambiguous requests, stale data, tool errors, and denied permissions. Check that it stops or escalates rather than improvising authority.
- Provide recovery. Use backups, reversible changes, transaction limits, credential revocation, and a clear kill switch. Practice restoring from an erroneous action.
Comparing agent configurations
There is no single autonomy setting that is safest for every task. Compare configurations using the same questions about data, actions, duration, isolation, and review.
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| Configuration | Typical behavior | Best fit | Main trade-off |
|---|---|---|---|
| Scripted workflow with model steps | Application code fixes the sequence; the model fills bounded tasks | Repeatable processes with known paths | Less adaptable when conditions change |
| Read-only agent | Can search and analyze permitted data but cannot modify systems | Research, triage, diagnostics, and drafting | Someone or another system must perform changes |
| Approval-gated agent | Chooses actions dynamically and pauses before selected operations | Mixed-risk work with a responsible operator | Prompt fatigue and poor decisions if context is unclear |
| Contained execution agent | Acts inside a sandbox with restricted files, credentials, and network | Code execution, transformations, and experiments | Isolation can break legitimate integrations and is not a substitute for data controls |
| Persistent-permission agent | Retains authorization for trusted routine operations | High-volume, low-risk internal tasks | A later mistake can reuse authority without a new prompt |
Questions to ask before deployment
- Can the agent read confidential information that is unrelated to the task?
- Which actions are irreversible or externally visible?
- Are permissions temporary, persistent, or approval-gated?
- Can the tool implementation enforce the limits independently of the model?
- What network destinations, subprocesses, and credentials are available?
- What exactly does a reviewer see before approving?
- Are denials, overrides, and tool outputs logged for later investigation?
- What happens when the model is uncertain, the tool fails, or retrieved content contains instructions?
- How can an administrator revoke access and restore state after an error?
These questions reveal the practical level of autonomy more reliably than a product’s marketing label. An agent is only as safe as the combination of its model behavior, tool implementations, authorization policy, containment boundary, monitoring, and human operating procedure.
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