Measure Agent Performance: KPIs, SLAs, and Costs

Understand what AI agents and workflows actually do (in plain terms)
See common starter use cases that make business sense
Learn how the handoff between AI and humans works
Get familiar with the data powering agents—and what’s safe
Know what to measure: accuracy, speed, cost-per-task, and more
Compare build vs. buy decisions for your team
Get a checklist to start measuring performance right away

What AI Agents and Workflows Are (Plain English Edition)

Agents = trained digital interns

Think of AI agents like reliable digital interns. They’re trained (by you or the platform) to handle specific digital tasks like organizing emails, updating records, or summarizing documents. Once they’re set up, they follow your instructions—no coffee breaks required.

Workflows = step-by-step systems

A workflow is simply a repeatable process. It might look like: “When a new email comes in, categorize it → draft a response → send for approval.” Each task follows a structure of input → action → output. Set once, repeat endlessly.

No code needed

Most AI agents today don’t require a developer. You can build and edit workflows using tools you already use—like your CRM or shared inbox—through simple dropdowns and toggles.

Learn how AI agents and workflows work →

Great Starter Use-Cases

Email and inbox triage: Automatically sort and flag high-priority emails.
Meeting or call summaries: Get readable summaries with key takeaways—no replay needed.
CRM updates and data entry: Keep customer info fresh and accurate without chasing team members.
Social or support message routing: Ensure the right person responds the first time.

Every one of these use-cases is designed to save time, reduce errors, and make life easier for your team today—not in the distant future.

The Handoff Pattern: Draft First, Then Human Oversight

How it works

AI drafts or preps a task — like writing a message or assigning a priority.
A human reviews — they can approve, edit, or escalate to someone else.

Why it matters

This method combines the speed of AI with the accuracy and judgment of your team. It builds trust and allows the automation to improve over time without taking full control right away.

Where the Data Comes From (and What’s Safe to Use)

Agents get data from your tools: email, CRM, calendar, forms—whatever you choose to connect.
You control access: agents use only the information you give them permission to see.
Most systems follow a secure login model (OAuth, API keys)—no snooping around your inbox.
Many platforms keep detailed logs so you can track exactly what agents have done.

See how we help with setup and coaching →

Failure Modes and Safe Fallbacks

Things that can go sideways

Unclear input: The AI drafts something off-topic or off-brand.
Incomplete data: If a connected tool isn’t syncing, things can get stuck.
Over-automation: When a task needs a human touch—but goes fully automated anyway.

Safety nets make a difference

Notify a human when something seems off
Save drafts instead of auto-sending
Escalate unusual cases to a shared inbox

Start small. Test pilot use cases at a limited scale so issues pop up early—before they’re widespread.

Key Metrics: What to Track (Without Overcomplicating It)

Accuracy: What percentage of AI results need fixing?
Cycle Time: How long does it take to complete a task, start to approval?
Resolution Rate: How many tasks get fully completed by the AI?
Cost per Task: Compare time and money saved vs. doing it the old way.
Agent Adoption: Are people actually using the AI workflows you built?
Human Overrides: How often are AI outputs being changed—and is that trending down over time?

Pick the metrics that matter for your business, then revisit them monthly. No fancy dashboards required—a simple sheet works fine.

Build vs. Buy: What Makes Sense for Small Teams

Build (custom setups)

Gives you flexibility—but takes more time and internal know-how. Great if you have strong technical folks and very unique needs.

Buy (prebuilt solutions)

Get launched faster and benefit from expert support. Many businesses prefer this to avoid reinventing the wheel.

Key questions to ask:

Do you want plug-and-play, or full customization?
Do you have someone on your team who understands AI tools and setup?
Are you optimizing for speed or control?

Explore AI setup options for your business →

Starter Checklist & Next Steps

Pick 1–2 pilot use cases — Good starters: inbox sorting, form replies, CRM updates
Define success — Example: “Faster replies with fewer handoffs”
Select 2–3 metrics — Accuracy, time saved, or task resolution rate
Create a tracking sheet — Don’t overcomplicate it
Set a monthly review — Look at the numbers and gather team feedback
Plan your next workflow — Build on what’s working and expand carefully

You don’t need dozens of flows to start seeing ROI. One solid system can save hours every week.

Conclusion

You don’t need complicated dashboards to measure success. If your business is running smoother, faster, or more affordably—that’s your scoreboard.

Start with something small. Track what matters. Refine as you go. With a little structure, AI can help your team do more without working more.

Need a partner to walk with you? Our team specializes in setting up smart, simple AI systems for real businesses.