When you don’t define success criteria for your agent project, there’s no way for it to succeed.

Teams launch the agent, see some activity, and then lose track of whether it’s actually helping the business. A working agent isn’t one that simply runs.

It’s one that creates measurable, repeatable outcomes that align with a goal you set.

This lesson is about building the discipline to measure what matters: not vanity metrics, but indicators that prove your agent is doing real work. Success begins with a clear definition of value.

The answer should connect directly to a business result.

For example:

• Resolve 50 percent of support requests without human involvement.
• Increase average basket size by 20 percent.
• Collect and summarize customer feedback each week.
• Reduce the average response time by half.

Each of these goals is simple, measurable, and aligned with a real operational need. Once you’ve set a goal, you can establish metrics that confirm whether you are meeting it.

The most reliable metrics for AI agents fall into five categories:

1. Usage — how many sessions or interactions occur within a given period.
2. Resolution rate — how often the agent completes a task successfully.
3. Escalation rate — how often conversations are handed to humans.
4. Business impact — metrics tied to outcomes like revenue, conversion rate, or satisfaction scores.
5. System health — performance data like latency, cost, and error rate.

Tracking all five gives a balanced view of both customer experience and technical reliability. When these metrics move in the right direction, you know the agent is doing its job. When they don’t, you have the information needed to make improvements.

At Terminal Roast, the team agrees to share responsibility for monitoring success.

Taryn, the owner, focuses on qualitative outcomes — customer sentiment and overall satisfaction.

Gideon, the tech lead, watches the analytics dashboard for usage, completion rate, and errors.

Adrian, the barista, reviews the weekly summaries generated by the agent to see whether the feedback is actionable.

Together, they meet once a week to look at the numbers and discuss what needs adjustment. If the agent begins handing too many conversations to humans, they check whether the prompts or instructions need fine-tuning. If usage drops, they verify that the widget is visible and functioning on the website.

This shared accountability keeps the project active. The team treats the agent as a living system that improves over time, rather than a one-time build. Success criteria also determine how you iterate. If you track only surface-level data, you’ll miss where the real issues live.

For example, a high conversation count might look good, but if completion rates are low, the agent is failing silently. A well-defined metric framework prevents that. It tells you when to retrain, when to refine workflows, and when to adjust the experience for users.

Here’s a good structure for post-launch monitoring:

1. Define 2–3 primary metrics that align with your original goal.
2. Set baselines using your current process before the agent launches.
3. Establish thresholds for when to intervene — like an escalation rate above 20 percent, or response time exceeding a set limit.
4. Review weekly at first, then monthly once the system stabilizes.

Include both quantitative and qualitative data. Numbers show outcomes, and human feedback shows quality.

Terminal Roast’s team ends up with a repeatable process.

They collect feedback, make small updates, and track the results. Each improvement is guided by evidence, not guesswork.

This rhythm (measure, adjust, and repeat) turns their agent from a pilot into an operational tool. It’s the same rhythm used by every team that succeeds with AI at scale. Defining success does not just measure results. It ensures that progress never stops.

Action: Write down two success metrics for your agent: one tied to user experience and one tied to business impact.

Decide who on your team will monitor each, and how often they’ll review them after launch.

And that’s it! If there’s one thing you take away from this course, it’s that good planning before you start building will take you very far. Happy bot building!

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