Originally published on AI Tech Connect.
What you will build When an agent demo works in a notebook and then falls apart the moment real users touch it, the cause is almost never the language model. It is the tools. A tool with a vague description gets called at the wrong time. A tool that throws a raw stack trace leaves the agent guessing. A write tool with no idempotency key fires a payment twice. A search tool that returns ten thousand rows quietly evicts the rest of the conversation from the context window. None of these are model problems, and none of them are fixed by a bigger model. This guide is the design discipline that turns a flaky agent into a dependable one. It is written for engineering teams shipping agents to real users — whether that is a fintech in Bengaluru wiring an agent into a payments back end, or a…
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