A Verifiable AI Agent Workflow: 7 Checks Before You Add Autonomy

Most agent projects fail before the model does.

The failure usually starts with an unclear outcome, an overpowered tool, or a demo that has no repeatable pass/fail test. The agent looks impressive once, then becomes impossible to trust in production.

Here is the checklist I now use before adding more autonomy.

1. Define one verifiable outcome

Avoid goals such as "help the user with support."

Use an outcome a human can inspect:

Given a support ticket and the account history, produce a draft reply with cited evidence and route it to a human reviewer.

A good outcome has a clear input, output, owner, and acceptance test.

2. Separate three kinds of work

Every step belongs in one of these buckets:

1. Deterministic work: API calls, schema validation, database reads, file transforms.
2. Model judgment: classification, summarization, ranking, drafting.
3. Human approval: sending messages, spending money, changing permissions, or deleting data.

This separation makes failures easier to locate. It also prevents a model from being asked to do work that ordinary code can do more reliably.

3. Give every tool a typed contract

An agent tool should look like an API, not a vague capability.

{
  "name": "get_order",
  "input": {
    "order_id": "string"
  },
  "output": {
    "status": "enum",
    "items": "array",
    "refund_eligible": "boolean"
  },
  "errors": [
    "ORDER_NOT_FOUND",
    "UPSTREAM_TIMEOUT"
  ]
}

Define timeouts, retry rules, idempotency, and the exact errors the model may receive. Do not return an unbounded blob and hope the model interprets it correctly.

4. Make approval gates explicit

A human approval gate is not a weakness. It is a product feature when the consequence is expensive or irreversible.

Useful approval gates include:

• sending an external email
• issuing a refund
• publishing content
• changing account access
• running a destructive command

The agent can prepare the action and explain its evidence. The human approves the final side effect.

5. Design failure cases before the happy path

Write at least five realistic failures before implementation:

• required data is missing
• two sources contradict each other
• the tool times out
• the model returns invalid structured output
• the requested action exceeds the user's permission

For every failure, define whether the system retries, asks a question, escalates, or stops.

6. Build pass/fail evaluations

"Looks good" is not an evaluation.

Create a small suite with cases such as:

• known-good input produces the expected action
• missing evidence prevents the action
• an injected instruction inside retrieved content is ignored
• a repeated request does not create a duplicate side effect
• a timeout produces a recoverable state

Start with 10 carefully chosen cases. Run them after every prompt, tool, or model change.

7. Ship the smallest observable workflow

The first milestone should fit into one day:

• one input type
• one model decision
• one read-only tool
• one structured output
• one approval screen
• one event log

Do not begin with a swarm of agents. Add another agent only when it removes a measured bottleneck.

A reusable planning prompt

Turn the AI agent idea below into a verifiable MVP.

Return:
1. one measurable user outcome
2. inputs and outputs
3. deterministic steps, model-judgment steps, and human approval gates
4. a typed contract for every tool
5. five realistic failure cases
6. pass/fail evaluation cases
7. the smallest milestone testable in one day

Do not add autonomy unless it is required for the outcome.

Agent idea:
[Describe the user, task, data, tools, and constraints]

The goal is not maximum autonomy. The goal is a workflow that can earn trust one observable step at a time.

I keep the copyable prompt and related workflow examples in this AI agent workflow guide.