A chatbot answers questions. An agent reads a ticket, checks inventory, drafts a refund, and files it — without a human in the loop for every step. That jump from 'generate text' to 'take actions with consequences' is where most of the real engineering work in applied AI now lives.
The three things that actually matter
- Tool design — the model is only as capable as the actions you expose to it, with clear inputs, outputs, and error states.
- Guardrails — what the agent is allowed to do autonomously versus what needs a human approval step, and how that's enforced in code, not in the prompt.
- Observability — every tool call, decision, and retry needs to be logged and replayable, because 'the agent did something wrong' is not a debuggable bug report on its own.
Most failed agent projects we've seen didn't fail because the model was too weak. They failed because nobody designed the permission boundaries or the fallback path for when the agent gets it wrong — and in production, it will get it wrong sometimes.
Start narrow, expand deliberately
The agent deployments that actually stick start with a single, well-bounded workflow — one queue, one decision, one action — running alongside a human for a few weeks before autonomy increases. That's slower to demo and much faster to trust, which is the thing that actually determines whether it survives contact with your real operations.
Deciding where an agent is worth the guardrail work — and where it isn't — is exactly what we scope in our AI Opportunity Assessment before any code gets written.