We design model-assisted steps and agents that interpret information and use permitted tools. Scope, evaluation and human decisions stay visible.
A clear objective, permitted information and tools, and a stopping condition.
Expected outputs, difficult cases and explicit escalation criteria.
Validated output, operating limits and human checkpoints before consequential action.
The system retrieves only the information permitted for the case.
The model proposes a response and identifies missing information.
A person reviews and decides whether to send it. Uncertain cases return to manual handling.
An agent should not receive broad permissions or make irreversible decisions without controls. When a rule or integration can do the job, it is usually the better mechanism. Model capability does not repair poor source data or unclear accountability.
We define the task, allowed tools, data access, confidence thresholds, approval points, and failure scenarios. Evaluation happens on agreed examples before scope expands. Useful inputs include real-world variants, expected outcomes, access policies, and a named decision owner.
No. Classification, extraction or drafting can be one model-assisted step inside a conventional workflow. An agent becomes useful when the next action depends on newly discovered context.
Compare rules, integrations, model-assisted features and agents through variability, risk and evaluation.
Read the related guideShare the situation today and the change you need. The technical specification can come later.
