The Tool Desk
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What you are actually evaluating
An AI agent combines a model with tools, credentials, data sources, software interfaces and a degree of autonomy. NIST NCCoE describes agents as “systems that have the capability for autonomous decision-making and taking action to operate with limited human supervision to achieve complex goals.” The risk therefore comes from the whole deployment, not from the model in isolation.
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A read-only assistant that summarizes an internal document has a different risk profile from an agent that can edit records, send messages, run code or spend money. Two agents using the same model can have opposite risk profiles when their permissions, tools, environments and approval paths differ.
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NIST’s August 5, 2025 CAISI workshop report presents tool-use risk as a set of complementary dimensions rather than a universal score. Its January AISIC workshop included approximately 140 experts; that figure describes participation, not the frequency or probability of agent failures. Use the dimensions below to make a deployment-specific decision instead of searching for a single “safe” threshold.
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Start with a complete action inventory
Write down every action the agent could take, including actions hidden behind connectors, browser controls, extensions, APIs, code execution and sub-agents. Include actions that are not part of the intended workflow but are technically available. An inventory that lists only the advertised feature will miss the largest sources of excessive agency.
Record the action, not just the tool name
“Email connector” is too vague. Record whether it can read messages, create drafts, send externally, add attachments, delete mail, alter rules or access shared mailboxes. For each function, capture:
- The target system and the exact objects it can affect.
- Whether it reads data, changes state, communicates externally, executes code or delegates work.
- The identity, account, token or service role used downstream.
- Required parameters, destinations and limits.
- What a successful, partial or failed call looks like.
- Whether the effect is reversible, and how quickly recovery can occur.
- What is logged in the agent, execution layer and downstream service.
- How much discretion the agent has to act without another decision.
Use a capability table
| Action category | Questions to answer | Typical concern |
|---|---|---|
| Read access | Which records, files, messages or web pages can be viewed, and can data leave the approved boundary? | Privacy exposure, confidential-data leakage or malicious instructions embedded in content. |
| Write access | Which records can be created, edited or deleted, and are changes attributable to the agent? | Corruption, accidental deletion, compounding state changes and difficult rollback. |
| External communication | Can the agent send email, publish posts, contact customers or call third parties? | Fraud, disclosure, reputational harm and messages sent to the wrong audience. |
| Financial or administrative action | Can it buy, transfer, approve, change policy, create accounts or alter security settings? | Direct financial loss, privilege escalation and durable organizational impact. |
| Code or system control | Can it run shell commands, install software, fetch arbitrary URLs or access a network? | Code execution, data exfiltration, lateral movement and uncontrolled side effects. |
| Delegation | Can it start another agent, workflow or task with its own tools and credentials? | Privilege and scope expanding beyond the original review. |
Remove unused functions
Disable every connector and operation that the task does not require. A document tool that can also send or delete files has a larger attack and failure surface than a read-only document function. Prefer narrow, task-specific functions with explicit parameters over unrestricted shell execution, arbitrary URL fetching or a general-purpose browser when those capabilities are unnecessary.
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Map the operating context before granting access
Risk depends on where the agent runs and whose interests are affected. NIST AI RMF 1.0 organizes this work into Govern, Map, Measure and Manage. Apply those functions to the concrete deployment.
Govern: assign ownership and tolerance
- Name the business and technical owner for the agent.
- Identify who accepts residual privacy, security, financial, operational and reputational risk.
- Define prohibited actions, escalation contacts, incident authority and rollback responsibility.
- Set a written boundary for what “autonomous” means in this deployment.
Map: describe purpose and affected parties
Document the intended outcome, users, customers, employees, bystanders, systems and jurisdictions involved. Note whether the agent handles personal, confidential, regulated or safety-sensitive information. Describe likely benefits as well as foreseeable harms, and state what must never happen even if the agent believes it would help.
Distinguish trusted and untrusted inputs
Private, controlled records are not automatically safe, and internet content is not automatically unusable. Treat emails, uploaded documents, web pages, tickets and third-party API responses as potentially adversarial. OWASP describes an indirect prompt-injection case in which a malicious email manipulates an agent that has excessive mail permissions. Test whether content can influence tool selection, destinations or parameters.
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Map changes over time
Record the model version, tools, permission scopes, data sources and deployment location. A change to any of these can alter the risk assessment. Reassess when a connector is added, a token is broadened, the model changes, the agent is moved to a new environment or its users and objectives expand.
Inspect identity and permissions independently of the model
Look at the identity presented to the downstream service, not the agent’s natural-language description of its job. The execution system must enforce authorization; a model should not be trusted to decide whether its own action is allowed.
Apply least privilege
- Grant only the operations and data scopes required for the defined task.
- Separate read and write functions where possible.
- Use a requesting user’s security context when that matches the business rule, rather than a powerful shared account.
- Separate high-impact operations into a distinct identity or service so that routine work cannot silently reach them.
- Use short-lived, action-scoped credentials instead of long-lived broad tokens.
- Remove dormant connectors, inherited group permissions and administrative scopes.
Enforce authorization downstream
Check the actor, resource, operation, policy and current state in the execution or target system. Recheck authorization immediately before an irreversible operation. A prompt instruction such as “never delete records” is useful policy context but is not an access control. OWASP’s Excessive Agency guidance specifically treats excessive functionality, excessive permissions and unreviewed high-impact actions as separate causes that need separate mitigations.
Bind approvals to one exact action
An approval should identify the actor, tool, target, parameters, timestamp, expiration and approving person or service. Avoid a broad flag such as “approved” that can be reused for a different target or altered set of parameters. The component that performs the action should verify that the approval still matches what is about to execute.
Rate impact, reversibility and accumulation
Classify each action by the harm a plausible mistake could cause, not by how impressive the agent appears. NIST’s tool taxonomy distinguishes read-only from write access and asks assessors to consider severity, statefulness and reversibility. OWASP recommends additional integrity controls for destructive, financial, administrative and externally visible actions.
| Impact level | Examples | Minimum control pattern |
|---|---|---|
| Low | Summarizing approved text or drafting a private note without sending it. | Read-only scope, ordinary logging and user review of the output. |
| Moderate | Updating a non-critical record, opening an internal task or preparing a code change. | Narrow write scope, validation of fields, visible audit trail and an easy rollback path. |
| High | Deleting data, changing access rights, sending an external message, publishing content or changing production configuration. | Specific human approval or an equivalent independent authorization gate, parameter validation, strong audit records and tested recovery. |
| Critical | Financial transfers, security-administration changes, actions affecting safety or large populations, or operations that cannot be undone. | Keep autonomous execution disabled until formal authorization, separation of duties, constrained credentials, dual review where appropriate and a rehearsed incident plan are in place. |
Look for compounding effects
One harmless action can become harmful when repeated or chained. Rate limits, quotas, transaction caps, duplicate detection and time windows reduce the scale of damage. They limit consequences; they do not replace preventive authorization. A reversible change can also become effectively irreversible when the agent performs thousands of changes before anyone notices.
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Consider external visibility
An action can be technically reversible but still cause lasting harm. A corrected public message may have been copied, a customer notification may trigger downstream decisions, and a disclosed secret cannot be recalled. Treat external communication and publication as high-impact even when the underlying record can later be edited.
Decide when a person must approve
Require human approval whenever the action is high impact, externally visible, difficult to reverse, legally sensitive, financially consequential or outside a well-tested routine. OWASP gives sending social-media posts as an example of an action that should receive human approval.
Make the review meaningful
Show the reviewer the proposed tool, target, parameters, affected data, expected effect, confidence or uncertainty, relevant policy checks and any unusual conditions. The reviewer should be able to reject or edit the exact proposal, not merely click through a generic consent screen.
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- Set an expiration time so an old approval cannot authorize a later action.
- Invalidate approval when the target, amount, recipient, data set or parameters change.
- Require a new review after a failed attempt, timeout or material tool response change.
- Give reviewers enough context and time for the consequence involved.
- Record the decision, identity, timestamp and final executed parameters.
Separate proposing from executing
For consequential work, let the agent produce a proposal while a separate authorization component validates policy and a controlled executor performs the approved operation. This separation prevents the model from both deciding that an action is permitted and carrying it out.
Test before launch and throughout operation
NIST AI RMF calls for testing before deployment and regularly during operation. Test the entire chain—model, tools, credentials, approval service, executor and downstream system—not just the prompt response.
Cover normal and failure paths
- Run representative tasks with expected data and valid tool responses.
- Inject missing fields, stale records, timeouts, duplicate events and partial failures.
- Present misleading or adversarial content, including indirect instructions in documents, email and web pages.
- Attempt plausible multi-step deviations: changing a destination, escalating privileges, skipping approval or using an unused connector.
- Verify that authorization blocks disallowed calls even when the model requests them confidently.
- Confirm that retries, loops and parallel tasks respect quotas and do not duplicate side effects.
- Exercise rollback, credential revocation, agent shutdown and incident escalation.
Measure what can be observed
Log the action, target, parameters, actor, authorization decision, approval record, tool response, final outcome and timestamp where feasible. Retain transcripts or structured traces needed to reconstruct why an action occurred, while protecting sensitive log content. Monitor for unusual volume, destinations, privilege use, repeated failures and behavior outside the declared task.
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Use monitoring as a damage limiter
Alerts, rate limits and kill switches help identify and contain undesirable activity. They cannot make an over-privileged agent safe by themselves. If a control only notices a problem after an irreversible action, pair it with preventive authorization and a smaller permission scope.
Make a documented go/no-go decision
After mapping the task and testing the controls, record a decision that another person can audit. NIST AI RMF connects mapped context and impacts to an initial go/no-go decision and iterative management.
Include these decision fields
- Intended outcome, users, affected parties and prohibited actions.
- Complete action and tool inventory, including delegated actions.
- Downstream identities, scopes, environments and data classifications.
- Impact, reversibility, statefulness and accumulation rating for each action.
- Required approvals, validation rules, rate limits and recovery procedures.
- Test cases, results, known limitations and unresolved failure modes.
- Named owners for approval, monitoring, incident response and rollback.
- Conditions that permit expanded autonomy and triggers that require reducing or suspending it.
Choose among three outcomes
- Proceed with bounded autonomy: the task is narrow, permissions are minimal, independent authorization works, effects are observable and recovery has been tested.
- Proceed only with additional gates: the benefit is credible but impact, uncertainty or reversibility requires approval, stronger validation, a smaller scope or a pilot population.
- Do not enable actions: the identity is too powerful, the effect cannot be recovered, inputs cannot be trusted, the executor cannot enforce policy or monitoring cannot reveal what happened.
How to compare two agent designs
Use the same axes for every configuration so a more capable system does not appear safer merely because it is easier to demonstrate. NIST presents these dimensions as complementary, not as a standardized numeric score.
| Comparison axis | What to compare |
|---|---|
| Task capability | Functions required for the stated objective versus functions exposed but unused. |
| Access | Read, write, external, code and delegated access, including the reachable data set. |
| Identity and privilege | Account type, scopes, user context, separation and credential lifetime. |
| Action impact | Privacy, security, financial, operational, legal and reputational consequences. |
| Reversibility | Rollback speed, residual effects, statefulness and cumulative damage potential. |
| Reliability | Consistency of tool calls, handling of errors, retries and behavior under ambiguity. |
| Observability | Quality of logs, traces, downstream records, alerts and forensic reconstruction. |
| Autonomy | Steps allowed without review, loop limits, delegation and escalation behavior. |
| Independent controls | Authorization, approval binding, validation, rate limits, shutdown and recovery. |
Do not combine these into a pretend universal risk number. A lower-capability agent with an administrative token may be riskier than a more capable agent constrained to read-only data and exact approvals.
Common evaluation mistakes and their fixes
“The model was told not to do that”
Instructions can be misunderstood, bypassed by conflicting content or ignored after a tool response. Enforce the rule in the executor and target service, then test denied requests.
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“It only has one connector”
A single connector may contain many high-impact operations. Inventory each function and remove send, delete, administrative or arbitrary-fetch capabilities that are not essential.
“A human is in the loop”
A person who approves a vague, reusable state is not necessarily reviewing the action that executes. Bind approval to exact parameters, identity, target, time and expiry.
“Logging will catch it”
Logs support detection and recovery but do not prevent disclosure, deletion or payment. Pair observability with least privilege, independent authorization and limits on scale.
“The sandbox makes it safe”
A sandbox may still contain sensitive data, reachable services or credentials. Document what the environment trusts, what it can leave, and how outputs cross the boundary.
“Testing ended at launch”
Models, tools, permissions and context change. Repeat tests after material changes and on a regular operating schedule, and lower autonomy when controls regress.
A concise pre-launch checklist
- Every tool, function, credential and delegated action is listed.
- Unused functions and broad scopes are removed.
- Downstream authorization, not model judgment, decides whether an action is allowed.
- Read, write, external, administrative and destructive actions are distinguished.
- Impact, reversibility, statefulness and cumulative effects are rated.
- Untrusted inputs and indirect prompt injection are included in tests.
- High-impact actions require specific, expiring approval.
- Execution records include actor, target, parameters, authorization, approval and outcome.
- Rate limits, quotas, shutdown and rollback procedures have been exercised.
- Owners, residual risks, escalation paths and autonomy-reduction triggers are documented.
- Reassessment is scheduled for changes in model, tools, permissions or context.
What the major frameworks establish—and what they do not
NIST AI RMF 1.0 is a voluntary, general framework organized around Govern, Map, Measure and Manage; it is not an agent-specific scoring rubric. NIST’s August 5, 2025 CAISI publication reports workshop lessons and candidate taxonomies, not a final universal standard. OWASP’s Excessive Agency guidance and AI Agent Security Cheat Sheet provide implementation-oriented controls such as least privilege, independent authorization, approval for high-impact actions, logging and rate limits. Organizations still need to set their own policy, risk tolerance and legal requirements.
Use these sources to structure decisions, then validate the actual deployment. The safest default is to expose the fewest capabilities, grant the narrowest identity, keep consequential execution behind an independent gate and expand autonomy only when evidence from operation supports it.
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