Building AI agents that you can actually rely on
An AI agent that works in a demo is not the same as one your business can depend on. Reliability comes from design choices made long before the model is called.
Start with a narrow job
The most dependable agents do one well-defined task, such as triaging support requests or drafting a purchase order, instead of trying to do everything. A narrow scope makes behaviour easier to test, easier to measure, and easier to explain to the people who rely on it.
Give the agent tools, not freedom
Agents become useful when they can call real systems: search a knowledge base, read an order, create a ticket. Each tool should have a strict input format, clear permissions, and the least access needed. The model decides what to do; your code decides what is allowed.
Keep a human in the loop where it matters
Reads are low risk, writes are not. Let the agent act freely on low-risk steps and ask for approval before anything that moves money, changes records, or contacts a customer. Approvals can be relaxed over time as trust is earned.
Observe everything
Log every step: the input, the tools called, the outputs, and the final answer. When something goes wrong, you need to see exactly why. Good traces turn mysterious failures into fixable bugs, and they are the raw material for improving the agent over time.