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Simon Place
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Module 10 of 10, lesson 1 of 5

Use cases and final project

Spot real opportunities, avoid needless agents and ship a measured agent.

Lists of use cases by industry run long. Most cases share the same traits, which are more useful to remember than the list itself.

Examples by industry

  • Customer service: understand the request, open a ticket, offer a refund, escalate complex cases.
  • IT: monitor, diagnose, propose a fix.
  • HR: answer common questions, process leave requests.
  • Sales: qualify prospects, prepare meetings.
  • Supply chain: assess suppliers, flag risks.

Published gains often come from commercial sources. Before quoting a number, trace it to its source.

A good candidate for an agent

  • The input is unstructured and the path varies.
  • Mistakes can be detected and repaired.
  • The task takes long enough to justify the cost.
  • A human can take over.

Four options, not one

  • Human: judgment, ethics, accountability.
  • Rules: known, stable logic with no room for error. Payments, format validation.
  • Machine learning: patterns in past data, such as fraud.
  • Generative AI and agents: unstructured data, reasoning, language.

Systems that work often combine all four. The useful question is: how much unpredictability can this task tolerate?

Final project

Bring the running project together: a refund agent you could show in an interview.

  • Three tools, one of them irreversible and under human approval
  • A bounded loop and a log of every step
  • Two knowledge sources with routing
  • A cap in the code that resists injection
  • 30 evaluation cases, with pass rate, cost per task and causes of failure
  • A README that says what doesn’t work yet

A demo shows it worked once. An evaluation shows how often.

Worked example: The real cost of a task

The cost per run is misleading. What counts is the cost per task actually finished, including the time of the people who take over the failures.

function costPerSuccess({ runCost, successRate, humanCost }) {
  const failureRate = 1 - successRate;
  const total = runCost + failureRate * humanCost;
  return Math.round((total / successRate) * 100) / 100;
}

const agent = { runCost: 0.4, successRate: 0.8, humanCost: 6 };

costPerSuccess(agent);

2

An agent that costs 0.40 per run really costs 2 per finished task. Compare that with what the task costs today before you decide.

Try it in the editor

function costPerTask(t) {  // TODO  return 0;}function decide(t) {  // TODO  return { automate: false, costPerTask: 0, monthlySaving: 0, paybackMonths: null };}
Give the agent a skillShould this be automated?
Open in the editor

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