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Before adopting agentic AI, decide what outcome it must deliver and exactly which actions it may take without approval. Then examine its access, governance, safety evidence and ongoing accountability. An agent that can act through tools or connected accounts creates both new opportunities and risks; treat its identity and permissions as a core control, not an implementation detail.
1. What outcome justifies this agent—and how much autonomy does it need?
Name the business or public-service outcome before choosing a system. Set a baseline and a measurable success threshold so a pilot can show whether the agent improves on the current process. Possible uses include information retrieval, workflow automation, software development and cybersecurity operations, but the label “agentic” alone does not establish value or suitability. NIST’s AI Risk Management Framework provides a voluntary structure for addressing trustworthy AI across design, development, use and evaluation.
Define the action boundary in advance. Separate actions into three groups: those the agent may take automatically, those that require human approval, and those that are prohibited. Keep autonomy narrow, particularly where sensitive data or critical systems are involved. CISA and partner agencies recommend avoiding broad or unrestricted access for agents. CISA’s AI guidance is a useful reference for that boundary.
- Automatic: low-impact, reversible steps within a defined workflow.
- Approval required: consequential actions, such as changing records or sending communications, when a person should review the proposed action first.
- Prohibited: actions outside the agent’s purpose or beyond the organization’s acceptable risk.
These categories are a starting point, not a universal list: what counts as consequential depends on the process, affected people and possible harm. A clear success threshold and action boundary make it possible to judge whether greater autonomy is warranted at all.
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2. What data, tools, identities and permissions will it use?
Map every connection the agent can use, including data stores, APIs, plugins, credentials, memory and downstream systems. Consider not just what information it can read, but what it can change, disclose, transmit or trigger through those connections.
Give the agent a distinct identity rather than relying on a shared employee account. Apply least privilege: grant only the access needed for the defined task, use scoped tokens, and require explicit authorization for each action. Review whether permissions are temporary or persistent and how they can be revoked. NIST’s agent security work identifies identity, authorization and governance as central concerns as agents take actions at greater scale. NIST NCCoE’s Software and AI Agent Identity and Authorization project describes this challenge.
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Weak identity and authorization controls can contribute to data leaks, compliance failures, prompt injection and unpredictable autonomous behavior. NIST’s AI and cybersecurity materials discuss these risks. For a pilot or vendor shortlist, compare options on:
- Identity isolation and the scope of permissions.
- Data handling and retention, including how connected systems and memory are controlled.
- Tool and API restrictions, plus the ability to require approval for sensitive actions.
- Auditability: whether reviewers can see what the agent did and the actions it attempted.
3. What governance and risk process will control adoption?
Assign an accountable owner before launch. Document the risk assessment, affected-party analysis, privacy and security reviews, escalation rules and incident process. The owner should be able to explain the agent’s purpose, permitted actions and route for reporting a problem—not just its technical configuration.
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The NIST AI Risk Management Framework is voluntary and supports incorporating trustworthiness into AI design, development, use and evaluation. NIST released it on January 26, 2023. Its Generative AI Profile, NIST-AI-600-1, released July 26, 2024, adds guidance for generative AI risks. These frameworks can help organize governance, but adopting a framework does not by itself establish that a particular agent is safe or compliant.
For a two-option evaluation, compare the governance evidence each supplier or internal team can provide: risk ownership, impact assessment, privacy and security review, escalation paths, incident response and controls over vendor changes. Interoperability and total operating cost also matter, but neither substitutes for accountable risk management.
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4. How will safety, security and reliability be tested before launch?
Do not rely on benchmark accuracy alone. Test realistic tasks and failure modes in conditions that resemble actual use. NIST’s ARIA program evaluates AI through model testing, red-teaming and field testing, and considers technical and contextual robustness beyond simple accuracy. NIST ARIA describes those evaluation levels.
Build test scenarios around the agent’s actual tools and permissions, including:
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- Prompt injection or other instructions that try to redirect the agent.
- Attempts to use a tool or access data without authorization.
- Data leakage through outputs, memory or connected services.
- Privilege escalation or actions beyond the defined boundary.
- Unsafe plans, incorrect actions and failure to request human approval.
- Degraded or unavailable dependencies, and how the agent behaves when a tool fails.
Use threat modeling and red-teaming to probe how the system can be misused, then scenario-based evaluation to see whether it behaves acceptably in context. Record what was tested, what failed, how failures were addressed and which limitations remain. The evidence should cover the specific configuration being deployed, including its connected tools and permission scope.
5. Who is accountable after deployment, and how will controls adapt?
Assign a human owner with authority to pause the agent or revoke its access. Establish what events trigger escalation and who decides whether service can resume. Accountability should remain clear even when the agent’s actions involve multiple connected systems or vendors.
Monitor actions, permissions, outputs, incidents, drift and vendor changes. Review logs and reassess risk on a schedule, as well as after significant changes to tools, models, data access or operating conditions. CISA calls for continuous monitoring and regular security assessments in its AI guidance; NIST’s agent work highlights identity, authorization, interoperability and security evaluation. NIST NCCoE’s project addresses those agent-specific issues.
Compare shortlisted options on monitoring, incident response, audit logs and vendor change controls as well as their initial security-testing evidence. A system’s risk profile can shift when permissions, connected services or threats change, so launch approval should not be treated as permanent approval.
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Proceed only when the intended outcome is measurable, autonomy is bounded, access is explicitly authorized, governance has an owner, testing has produced relevant evidence, and someone can intervene after launch. If any of those conditions is missing, narrow the pilot, reduce permissions or defer deployment until the gap is addressed.
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