Haiku 5.5 puts smaller AI models back on the workflow shortlist. What should teams test?

Anthropic introduced Claude Haiku 5.5 on October 7, describing it as a small model intended for high-volume, cost-sensitive work such as classification and summarization. The announcement also introduces adjustable effort for the Haiku class and says the model is available through its platform and cloud providers.

Those are vendor descriptions of the product's intended fit. They do not establish how well it will handle a particular company's documents, terminology or exceptions. A release announcement can justify a comparison; it cannot supply the result of a test that has not been run.

Consider a hypothetical operations team that wants incoming requests sorted into scheduling, billing or manual review. This is an illustrative workflow, not an OrangeHat client result or a claim about any model's measured accuracy.

Before choosing a model, the team could define the task more precisely: return one allowed category, preserve the request identifier and route uncertain cases for review. It could explicitly prohibit sending replies, changing appointments or editing financial records. Classification and acting on the classification are separate responsibilities.

That scope makes an error easier to inspect. A request put into the wrong queue is a different failure from a malformed output or a system that takes an unauthorized action. A single overall score can conceal those differences.

Anthropic's engineering guidance on agent evaluations distinguishes test tasks, individual trials, grading logic and the final outcome. It notes that outputs vary between runs and that an agent's claim of completion is different from the actual state left behind. Those distinctions are useful even when the task is simpler than a full agent.

For the hypothetical intake test, the following is an editorial evaluation framework, not a vendor requirement:

Use the same authorized test inputs and acceptance criteria for each candidate. Record the prompt, model identifier, settings and test date, and document any deliberate differences. If one candidate receives extra context or a different task definition, describe that change instead of attributing the entire result to model size.

Keep ordinary cases and troublesome cases visible separately. For example, a request mentioning both an appointment and an unpaid invoice may need a written precedence rule or a manual-review route. Calling it wrong without deciding the expected behavior first creates an ambiguous test.

Anthropic's evaluation guidance also recommends reviewing trial records to understand failures and warns that grading can reject a valid result. A useful investigation therefore checks both sides: what the system produced and whether the reference decision or scoring rule was appropriate.

A limited test can support another limited decision: whether to continue evaluation, revise the task or trial a bounded use with appropriate oversight. It does not establish suitability for every document, audience or business action.

For the illustrative routing workflow, the owner should be able to explain what happens to uncertain outputs, who examines mistakes and how the previous process can resume. Those responsibilities matter regardless of which model is selected.

The article reports a current release and proposes a way to assess it; it does not report a benchmark, guarantee savings or recommend uploading private records to a new provider. Use approved data and existing organizational permissions when designing a real evaluation.