AI agents — systems that can interpret a goal, plan steps, call tools and act on results — are moving quickly from research into business software. Used well, they can take on work that previously required a person to stitch together several systems.
Where agents work well today
The strongest early use cases share a few characteristics: the task is well defined, the tools involved are reliable, mistakes are recoverable and a person can review outcomes when needed. Examples include:
- Preparing first drafts of reports by gathering data from several internal systems
- Triaging and routing service requests with suggested responses
- Reconciling records across systems and flagging exceptions
- Assisting engineers with code changes, testing and documentation
Design principles for production agents
Narrow the scope. Agents perform best with a clear goal and a limited, well-described set of tools.
Keep people in the loop. Define which actions require approval, and make it easy for reviewers to see what the agent did and why.
Evaluate continuously. Build test scenarios that reflect real work, including edge cases, and run them on every change.
Log everything. Detailed traces of reasoning steps and tool calls are essential for debugging, audit and improvement.
Apply least privilege. Agents should only have the access they need, with safeguards against prompt injection and data leakage.
Getting started
Choose a process with clear inputs and outputs, measurable volume and a willing business owner. Start with an assistant that recommends actions, then increase autonomy as evaluation results and user trust grow.
Exploring AI agents? Talk to our AI engineering team.
