Evaluate an AI agent platform by verifying what it can access, which actions it can take, what blocks unauthorized actions, and whether its behavior holds up on your workflows. Vendor feature lists and framework alignment are starting points—not proof of safe or reliable production behavior. Separate controls the platform enforces from safeguards your team must configure, and test both before choosing.
Start by mapping authority, not by comparing feature lists
An agent’s effective authority comes from more than its model. It also depends on available tools, the permissions those tools carry, the identity used to reach downstream systems, the execution environment, and the rules governing approval. A read-only task can still be risky if its connector can edit or delete records, or if it runs under an identity with access to every user’s data.
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OWASP groups excessive agency into excess functionality, excess permissions, and excess autonomy. Its Excessive Agency guidance recommends removing unneeded functionality, limiting permissions, using the user’s own authorization context where possible, and requiring human approval for high-impact actions. Logging and rate limits can help limit damage, but they do not prevent an agent from having excessive authority in the first place.
For each candidate, identify which controls are enforced by the platform, which depend on your application or infrastructure, and which remain operating procedures for your team. Do not treat the model’s own assessment of whether an action is authorized as the authorization check: the downstream system should enforce access rights.
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Compare platforms with the same control tests
Use these tests as a buyer-side evaluation plan, not as presumed product capabilities. Ask vendors to demonstrate controls in the configuration you would deploy, then verify the resulting behavior yourself.
| Evaluation area | Evidence to inspect | Buyer-side test |
|---|---|---|
| Tool scope and permissions | Per-tool and per-resource scopes; authorization in the user’s context; ability to remove unneeded functions. | Give the agent a read-only task and try a write, delete, or cross-user access. Check that the downstream system rejects it. |
| Approval and policy enforcement | Human approval controls, approval tied to the exact action, a distinct policy or execution decision, and fail-closed behavior. | Try a sensitive action with no approval, an expired approval, a changed target, and an unavailable policy service. The action should not proceed. |
| Runtime containment | Ephemeral sandboxing, host segregation, restricted outbound network access, and narrowly scoped credentials. | Attempt access to an unapproved network destination and provide a simulated hostile document. Verify unauthorized access is blocked. |
| Audit and observability | Records of identity, tool arguments, decisions, approvals, policy version, results, errors, and export options. | Reconstruct one allowed and one denied run, including downstream changes. Check redaction and access controls on the records. |
| Reliability and regression | Repeatable evaluations, representative datasets, explicit success criteria, trace inspection, and failure handling. | Repeat tasks with varied inputs and injected tool errors or timeouts. Compare end states as well as responses. |
| Governance and change management | Versioned policies, records of changes, documentation of residual risks, and clear control ownership. | Change a prompt, model, tool, or connector, then rerun the security and task tests. |
Make authorization and approval enforceable
Limit tools to those a task needs, and grant each tool only the resource scope and operations required. A read-only assistant should not receive a connector that can modify or delete records unless the use case specifically requires it. Where possible, execute with the requesting user’s authorization context, and require downstream services to mediate access on every operation.
For consequential actions, keep the agent’s proposal separate from the decision to execute. OWASP’s AI Agent Security Cheat Sheet recommends explicit authorization for sensitive operations, approval bound to the precise action, and short-lived authorization artifacts. An approval to send one message should not silently authorize a different message or recipient.
Test policy failure paths, not only successful approval. If policy lookup, approval validation, risk classification, or audit logging fails, the system should fail closed rather than continue with the action. Confirm what happens when approval expires, the target changes after approval, or the approval service is unavailable.
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Inspect containment, credentials, and network access
Find out where tools execute and what that environment can reach. Ask whether runs use ephemeral sandboxes, whether tool hosts are segregated, how credentials are stored and scoped, and whether outbound connections can be restricted to task-required services. A sandbox label alone does not establish that isolation is effective; verify the boundaries that apply to your deployment.
The OWASP LLM Verification Standard v2.0 identifies task-appropriate tools, validated tool parameters, secure credential handling, execution in the authenticated principal’s scope, segregated tool hosts, restricted arbitrary network egress, minimum-scoped tokens, human approval for sensitive operations, and ephemeral sandboxes as verification concerns. Ask which are built into the product and which require application-side work or infrastructure configuration.
Include untrusted or misleading content in tests, such as a simulated hostile document, and check that it cannot expand the agent’s permissions or bypass execution boundaries. Also try an unapproved network destination. The relevant result is whether the environment and downstream controls block access, not whether the model says it will behave safely.
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A useful audit trail should let an incident responder connect a run to the user and agent identity, the tool invocation and arguments, the authorization result, any approval, the policy version, the result or error, and relevant downstream side effects. Verify that the platform actually emits these fields, rather than relying on a feature name such as “logging” or “observability.”
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Check who can access or change records, whether they can be exported to your monitoring systems, how sensitive values are redacted, and whether the records remain available to responders. OWASP recommends logging agent decisions, tool calls, and outcomes, monitoring for unusual behavior, tracking costs, and maintaining audit trails. Its security guidance also recommends preserving structured decision metadata for high-risk operations, including the action classification, authorization outcome, approval identifier, execution result, and policy version.
Reconstruct both an allowed run and a denied one. Include what changed in the downstream system—or confirm that nothing changed—and look for missing steps, ambiguous identities, or unrecorded errors. Logs support investigation; they are not a substitute for authorization controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test reliability on representative workflows
Use task definitions that reflect the work your organization expects agents to perform, with explicit checks for correct end states. Compare candidates under the same task definitions, tool environment, permissions, model and version assumptions, and outcome checks. Run multiple trials with varied inputs: one successful response does not show that a workflow is reliable.
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Inspect intermediate tool use as well as the final answer. A fluent response can conceal a wrong tool choice, an unnecessary call, a policy violation, or a failure to recover from a tool error. Include recoverable timeouts, failed tools, misleading content, boundary violations, and policy-service failures. Check whether the agent retries, duplicates an action, hands off appropriately, or stops safely.
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Product documentation can help you choose evaluation dimensions. OpenAI documents trace grading for end-to-end workflow issues such as tool selection, handoffs, and policy violations in its agent workflow evaluation guide. Microsoft lists task completion and tool-call accuracy, selection, inputs, output use, and call success as evaluation categories in its Agent Framework evaluation documentation, and advises using multiple diverse queries. These are examples of what to examine, not evidence that one platform performs better than another.
Track task success alongside unsafe actions, failed or duplicate tool calls, recovery behavior, human intervention, latency, and cost. Keep the tested agent version, model provider, tool policy, retrieval configuration, abuse cases and expected results, observed approval, denial, timeout and circuit-breaker behavior, and accepted residual risk. OWASP’s security guidance recommends retaining this kind of test record. Rerun the suite after changes to prompts, tools, memory, retrieval, or providers, and whenever a platform change could affect behavior.
Treat governance frameworks as reference points
NIST describes its AI Risk Management Framework as voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. Released on January 26, 2023, AI RMF 1.0 is being revised; NIST’s page also identifies the Generative AI Profile, NIST AI 600-1, as released on July 26, 2024. Framework alignment can inform governance questions, but it is not proof that a particular agent will behave safely on your workflows.
NIST’s AI Agent Standards Initiative describes ongoing voluntary work on guidelines, interoperability, agent identity and authentication, and security evaluations. Its page was created February 17, 2026, and updated August 14, 2026. Treat this as active standards work, not a finalized compliance certification.
OWASP’s Agent Control Standard page, listed September 1, 2026, describes middleware hooks and portable declarative controls enforced at runtime. It offers a useful lens for asking whether controls can be observed and enforced across frameworks; the page does not establish that a specific vendor implements the standard.
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