# Agent Lifecycle Overview

This lifecycle describes how a healthcare operations team can evaluate reusable AI assistant patterns before broader use.

## 1. Intake

Define the workflow problem, intended users, expected inputs, expected outputs, and known data sensitivity concerns.

## 2. Classification

Classify the example data and decide whether the pattern can be tested with synthetic or de-identified material. If the workflow involves sensitive data, pause until local review requirements are clear.

## 3. Prototype

Create a simple assistant pattern using generic source material. The prototype should make clear that outputs are drafts for human review.

## 4. Evaluation

Test against sample cases and score:

- Completeness
- Accuracy against source material
- Missing information handling
- Clarity
- Risky or unsupported output

## 5. Pilot

Allow a small group to test the assistant pattern with reviewed example inputs. Collect time, quality, and usability feedback.

## 6. Review

Before any broader use, review the pattern against local policy, legal, privacy, security, and compliance requirements.

## 7. Maintain

Assign an owner, refresh source material, review feedback, and retire patterns that are no longer accurate or useful.

## Non-Negotiable Design Principles

- Human accountability stays with the user.
- Example data should be synthetic or de-identified unless local controls support otherwise.
- Assistants should ask for missing information instead of inventing it.
- Outputs should clearly separate facts from assumptions.