An AI agent is a language model wrapped in a loop. It reads a goal, decides which tool to call, looks at the result and decides what to do next, until the task is done or it asks a person for help. That simple loop is why agents can handle work that used to need a human in the middle, and also why they need careful engineering.
Where agents work well today
- Triage and routing. Reading tickets, emails or alerts, gathering context from other systems and sending each one to the right place with a summary.
- Exception handling. Working through invoice mismatches, failed orders or data quality issues that follow known rules but need lookups across systems.
- Research and drafting. Collecting facts from internal sources and drafting a report, reply or recommendation for a person to approve.
- Operations runbooks. Running diagnostic steps during an incident and proposing a fix.
Where they still struggle
- Open-ended goals with no clear finish line.
- Tasks where one wrong action is expensive and cannot be undone.
- Workflows that depend on knowledge nobody has written down.
- Long chains of many steps, where small errors compound.
A safe path to your first agent
- Pick one narrow workflow with clear inputs and outputs, a measurable cost today and a person who owns it.
- Collect 50 to 200 past cases with known good outcomes. These become your evaluation set.
- Design the tools the agent may use. Give each a clear name, description and the least access it needs. The Model Context Protocol is a useful standard for this.
- Start in suggest mode. The agent proposes actions and a person approves them. Measure how often the person agrees.
- Add guardrails: spending and time limits, approval for high-risk actions, logging of every tool call and tests for prompt injection.
- Automate gradually. Let the agent act on its own only for the case types where it has proven reliable.
What to measure
| Measure | Why it matters |
|---|---|
| Task success rate on the evaluation set | Quality before and after every change |
| Human agreement rate in suggest mode | Readiness to automate |
| Cost and time per completed task | The business case |
| Escalation rate | How often people still need to step in |
| Policy violations caught by guardrails | Whether controls are working |
Agents reward discipline. Teams that treat them as production software, with scope, tests and monitoring, get dependable results. See how we approach this on our AI page.