An AI agent is a loop rather than a single prompt. It plans a step, takes an action through a tool, observes the result, and decides what to do next. That loop is what lets an agent work through tasks that need more than one answer.
In everyday engineering the most useful agents are narrow. They triage logs, draft changelogs, or check configuration against a policy. Broad, open-ended agents are impressive in demos but harder to trust in production.
Tools define the boundary
An agent can only do what its tools allow. Read-only tools are low risk: searching documentation, querying metrics, reading files. Tools that change state, such as restarting a service or merging a branch, deserve an explicit approval step.
Give an agent read access freely; give it write access deliberately.
Keep the loop observable
Log every step the agent takes, including the tool calls and their results. When something goes wrong, a readable trace turns a mystery into a short debugging session.
- Start with read-only tools and expand gradually.
- Require approval for any action that changes state.
- Store traces alongside the outputs they produced.
Treated this way, agents become another reliable tool in the workflow rather than a black box that occasionally surprises the team.