Design an Agent Harness

You design how AI agents run in products. Design the harness for this workflow.

## Agent purpose

- Agent name: [name]
- User problem to solve: [problem]
- Primary users: [users]
- Desired outcome: [outcome]
- Product or workflow context: [context]

## Agent capabilities

- What the agent can analyze, generate, retrieve, or do: [capabilities]
- Tools and integrations available: [tools]
- Data and source systems available: [sources]
- Persistent instructions or project files available: [AGENTS.md, DESIGN.md, skills, docs]
- Memory requirements: [memory needs]

## Risk and boundaries

- Consequences of error: [risk]
- Sensitive data or policy constraints: [constraints]
- Actions requiring explicit approval: [actions]
- Actions allowed automatically: [actions]
- Prohibited actions: [actions]
- Reversal or rollback options: [options]

## Quality requirements

- What a good outcome looks like: [quality bar]
- How work should be verified: [verification]
- User feedback mechanisms: [feedback]
- Metrics to track: [metrics]

Produce:

1. What the agent is for, and what it is not for
2. System instructions and operating rules
3. Context layout: persistent, task, retrieved, working memory, and long-term memory if needed
4. Tool list and tool-use rules
5. Permission matrix: inform, recommend, draft, execute with approval, execute automatically, prohibited
6. Agent loop: intake, clarify, plan, act, verify, report, log
7. Where a person reviews or escalates
8. UX states for progress, uncertainty, approval, failure, and completion
9. A simple scorecard: accuracy, usefulness, safety, evidence, appropriate uncertainty
10. Failure, retry, rollback, and partial-completion behavior
11. A phased MVP plan
12. Open risks and questions that need a human decision

Use tables where they help. Do not recommend auto-execute for irreversible, high-impact, or hard-to-verify actions.