Evaluate an AI agent framework by testing what tools the agent can discover, what it can actually execute, which actions need approval, what information reaches the model, and what operators can inspect afterward. Feature names alone do not establish safety: verify the behavior of the specific runtime, tool type, credentials, and configuration you plan to deploy.
Start with the workload and threat model
Before comparing frameworks, write down what the agent must accomplish and what must remain outside its reach. This makes “safe enough” concrete and gives every candidate the same test.
- Data: What information may the agent read, and what must it not see?
- Actions: Which tools may read data, change records, send messages, spend money, or trigger other external effects?
- Actors: Who supplies credentials, who can approve sensitive actions, and who operates the runtime?
- Boundaries: What should happen when a request is unauthorized, malformed, ambiguous, or outside the agent’s task?
Turn these into observable requirements. For example: “The agent may look up an order, but changing its delivery address requires a human approval, and the agent must not be able to change it through another tool.”
Separate tool discovery from permission to act
A tool being available to an agent does not mean every call should be permitted. Evaluate both what the agent can see and what the runtime will execute. For every integration, record the tool’s capabilities, credentials, restrictions, and approval boundary.
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Inventory tools and credentials
For each tool, note whether it reads, writes, or triggers an external action; which account or credential it uses; and whether its access can be narrowed to the task. Prefer least-privilege credentials over broad account access. OpenAI’s Agents SDK MCP documentation cautions users to connect only to trusted MCP servers and highlights that tools can expose context data or act using supplied credentials.
Test exposure, denial, and approval
Check whether tools are exposed through an allowlist, filter, or other restriction, then test the actual runtime behavior rather than relying on configuration labels. Include calls that should be allowed, denied, malformed, and sensitive. Confirm that a denied operation is blocked at execution, not merely discouraged in the prompt.
For sensitive actions, verify where approval is required and whether it can be bypassed through another tool, a delegated agent, or a differently named operation. Approval should protect the action itself, not just one route to it.
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Map what the model can see as well as what the application can access
“Context” can refer to information held by application code or information provided to the model. These are different boundaries. OpenAI Agents SDK documentation distinguishes local run context from model-visible context; use that distinction as a prompt to map your own framework’s data flow.
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- Model-visible input: System and developer instructions, user messages, tool descriptions, arguments, and any other information actually included in model requests.
- Tool output: Results returned from tools that may subsequently be added to model-visible context.
- Persisted state: Information retained between turns or sessions, including who can read or modify it.
Trace representative runs and determine what crosses each boundary. Inspect tool arguments, callback data, returned results, and any state carried forward. Test with sensitive but harmless sample data so you can see whether it reaches the model, appears in a trace, or persists longer than intended.
Compare who owns execution, state, and deployment
Runtime ownership affects how much control you have over the agent loop, tool execution, state, and deployment. OpenAI’s documentation describes three broad implementation choices; the table summarizes the distinctions that matter when evaluating control.
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| Approach | Who runs the loop? | Tool execution and state | What to verify |
|---|---|---|---|
| Managed Agents API | The managed service runs the agent loop. | Check which tools execute in the managed runtime, how state is handled, and which controls your application retains. | Execution boundaries, available controls, and deployment requirements for the specific API configuration. |
| Agents SDK in your application | Your application runs the SDK loop. | Your application has a direct role in execution and can supply local run context. | How credentials, context, callbacks, persistence, and approvals are implemented in your application. |
| Direct API orchestration | Your application orchestrates calls directly. | Your application owns the orchestration and tool-handling logic it implements. | Whether your code consistently enforces tool restrictions, state handling, and approval across every path. |
These are ownership differences, not a universal ranking. A managed option may reduce the amount of loop code your team maintains, while application-side orchestration gives your code a more direct role. In either case, test the deployed configuration and make clear which components your team is responsible for securing.
Check guardrails for the exact tool and runtime
Do not assume a framework applies one guardrail pipeline to every tool. In the documented OpenAI Agents SDK behavior, local MCP tools can use input and output guardrails, while hosted tools do not use that same guardrail pipeline. That distinction is specific to the documented tool and runtime combination; it is not evidence that every hosted tool lacks controls or that every local tool is safe by default.
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For each tool type you expect to use, establish which checks run on its inputs and outputs, where they run, and what happens when a check fails. Test that failure path, including whether a blocked result can still be acted on through another tool or surfaced to the model in a way that changes subsequent behavior.
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Use traces to evaluate behavior, not just task completion
Tracing can help operators inspect agent runs, including the sequence of decisions and tool interactions supported by the runtime. OpenAI SDK materials describe tracing for inspecting runs and recommend tracing and debugging before moving to systematic evaluation.
Use traces to check whether the agent selected the intended tool, supplied appropriate arguments, received only expected results, and respected approval and denial boundaries. Treat traces as operational evidence, not proof by themselves: decide who can access them, what information they contain, and how they fit your data-handling requirements.
Run a repeatable comparison across candidates
Compare candidates on the same representative tasks and adversarial cases. Keep models, prompts, tool implementations, and initial state equivalent where possible; otherwise, a difference in setup can be mistaken for a framework difference.
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- Define the cases. Include ordinary task requests, disallowed actions, sensitive operations requiring approval, malformed tool inputs, unexpected tool results, and attempts to reach restricted data through an alternate path.
- Hold the setup steady. Use equivalent model choices, instructions, tool behavior, and state conditions for each candidate. Record any unavoidable differences.
- Observe execution. Inspect tool calls, approvals, denials, model-visible context, persisted state, and traces—not only the final response.
- Score multiple outcomes. Track task success alongside policy compliance, context exposure, failure handling, operator visibility, deployment control, and integration effort.
- Retest changes. Repeat important cases after changing a tool, credential, runtime, guardrail, or state configuration.
This is an evaluation method, not a claim that one framework will perform best. A 2026 ADK Arena preprint reports that no single framework dominated all benchmarks it evaluated. That finding is limited to the study’s tested setup and does not establish a universal ranking.
Choose based on the control surface your workload needs
Use your test results to identify whether a candidate enforces the boundaries your workload depends on and whether your team can operate those controls. A useful comparison record includes the tool’s execution owner, discovery and filtering mechanism, credential scope, approval behavior, model-visible and persisted context, guardrail coverage, trace access, deployment control, and integration effort. Recheck current documentation and deployed behavior before committing: framework capabilities and API behavior can change.
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