Integrating language models into business processes: interfaces, data and review
The short answer
Treat the language model as a processing step with a defined input and a verifiable output. Your application decides on data access, validation and the next action. Start with a bounded task whose errors can be detected and whose result can be corrected.
Bound the task
A useful first candidate could be a draft summary of a service request. The model should condense the text and identify open questions. It should neither give binding advice to the customer nor close the case independently. Establish who uses the draft and what happens when information is missing.
Anthropic distinguishes predefined workflows from agents that determine their own steps. For a fixed sequence, I recommend starting with a workflow that can be followed and inspected. More autonomy makes sense when the actual process requires it.
Define the interface before the prompt
A possible output contract for the example would be:
This illustrates a format; it is not an executable API call. The application should define allowed categories, field lengths and handling of empty values. A missing value must not silently turn into approval.
Schema-conforming model output helps with processing. It does not prove that the summary is factually correct. OpenAI documentation explicitly notes remaining mistakes and possible refusals. Interrupted responses also need a defined failure path.
{"summary": "…", "open_questions": ["…"], "suggested_category": "…"}Separate reading and writing deliberately
For an initial prototype, I would have the application supply only the fields needed for the summary. Incoming text is data to be processed; instructions within it should not grant new permissions.
- Input: select necessary fields and leave out unnecessary personal information.
- Processing: produce a draft; handle technical failures and empty responses.
- Validation: check the format, allowed values and relevant claims against the original text.
- Output: store the draft separately; require a separate permission check and, where appropriate, approval for the intended write operation.
Retries are part of the workflow
Imagine that a target system stores a draft but its acknowledgement is lost. A blind retry could create the same draft twice. A stable operation identifier and a check for completed processing help keep this scenario manageable.
For troubleshooting, record status, configuration and duration. Full confidential inputs do not automatically belong in the logs. Also set a time and cost budget per operation and define a clear failure state.
Evaluate success on real tasks
Before rollout, assemble a small set of permitted test cases: short and long requests, ambiguity, missing data and deliberate instructions in the input. Add an expected assessment for each case.
| Criterion | Question to check |
|---|---|
| Factual accuracy | Which statements are supported by the input? |
| Usability | How much does the responsible person need to correct? |
| Operations | What happens on timeout, refusal and repeated delivery? |
| Effort | How does the work saved compare with review effort, runtime and cost? |
Limits and scope
These workflows are an architectural proposal, not evidence of a tested client integration. The JSON is illustrative only.
Permissions, appropriate data handling and business approvals depend on the use case. A good format, fine-tuning or an additional knowledge source does not guarantee a correct answer.