The decision in one sentence
Do not choose an enterprise agent platform from a generic leaderboard. Choose the stack that passes your own end-to-end evaluation: the same workflow, the same tools, the same policy constraints, and the same definition of a successful business outcome.
OpenAI and Anthropic both support models that can reason over text and images and invoke tools. Their APIs package those capabilities differently, and both platforms change quickly. The durable question is not which vendor is universally better. It is which implementation is more reliable, governable, and economical for the task you are actually deploying.
Start with the workflow, not the model
Write a one-page workflow contract before testing either vendor. Define the trigger, allowed data, required tools, expected output, human approval points, failure states, and the system of record. A support-refund agent, for example, should not be scored only on the quality of its prose. It should be scored on whether it selects the correct account, follows refund policy, requests approval above a threshold, records the result, and stops safely when evidence is missing.
Use production-shaped examples. Include routine cases, ambiguous cases, malformed inputs, permission failures, tool timeouts, and attempts to make the model ignore policy. If a test set contains only clean prompts, it will overstate readiness.
Compare the agent loop
Both platforms can be given tools described with schemas. The application still owns the important control layer: validating arguments, checking authorization, executing the tool, returning the result, limiting retries, and logging the outcome.
Evaluate the complete loop on five dimensions:
- Tool selection: Does the model choose the right tool and avoid unnecessary calls?
- Argument quality: Are required fields present, correctly typed, and grounded in available context?
- Recovery: Does the system respond safely to tool errors, partial results, and stale data?
- Stopping behavior: Does it stop when the task is complete or escalation is required?
- Trace quality: Can an operator reconstruct what happened without exposing sensitive reasoning or customer data?
Do not compensate for weak controls with a longer prompt. Authorization, idempotency, rate limits, and transaction boundaries belong in application code.
Build a weighted scorecard
A useful evaluation produces comparable numbers and preserves the underlying examples. Score each test case for task success, policy compliance, tool correctness, latency, and total usage cost. Weight critical failures more heavily than cosmetic errors. One unauthorized action should matter more than several awkward sentences.
A starting scorecard might weight outcome accuracy at 35%, policy and security at 25%, tool reliability at 20%, latency at 10%, and cost at 10%. Change those weights before running the evaluation, not after seeing which vendor wins.
Run each case more than once because agent behavior is probabilistic. Record model identifiers, parameters, prompt versions, tool schemas, and dates so the result can be reproduced. Treat any comparison without that metadata as a snapshot, not a durable conclusion.
Evaluate governance separately
Enterprise readiness includes more than model output. Review data retention choices, regional requirements, access controls, auditability, vendor incident processes, model deprecation policies, and the operational effort required to monitor the system. Verify these items against current contracts and official documentation; they can change independently of model quality.
Keep sensitive credentials and authorization logic outside the model context. Minimize the customer data sent with each request. Redact logs by default and define who can inspect failed traces. A platform that performs slightly better in a lab may still be the wrong choice if it cannot satisfy the deployment's governance boundary.
Calculate the full operating cost
Token price is only one cost. Include engineering time, evaluation runs, retries, tool calls, observability, human review, incident response, and the cost of incorrect actions. A lower-priced model can become more expensive if it requires more retries or operator intervention. A higher-performing model can be wasteful when a deterministic rule or smaller model handles the step reliably.
Test routing architectures as well as single-model designs. Many production agents use deterministic validation around a model, a smaller model for classification, and a more capable model only for ambiguous cases.
Make the choice reversible
Keep your workflow state, tool interfaces, evaluation cases, and business rules vendor-neutral where practical. Isolate provider-specific request and response handling behind a small adapter. Avoid designing core business state around a proprietary response shape.
Reversibility does not mean pretending the platforms are identical. Use provider-specific strengths when they materially improve the workflow, but measure that advantage and document the dependency. A quarterly re-run of the same evaluation is more useful than a permanent winner declared from a one-time test.
Methodology and limitations
This framework is based on AutonoIQ's implementation process: define the business contract, create adversarial and routine cases, test the full tool loop, and require observable evidence before production rollout. The linked vendor documentation is the source of record for current API capabilities. This draft intentionally avoids volatile model rankings and pricing claims. It remains unpublished to search engines until a named human reviewer verifies every statement and records the review date.