An AI agent in a product is a software component that uses a language model to decide on a sequence of actions and carry them out (call an API, fill a form, look up data) instead of just answering. The right question is not “do we need AI?” but “what repetitive work can the agent do for the user, reliably?”. Here are the use cases that deliver real value, the ones that are gimmicks, and the pitfalls to avoid.
Agent, chatbot, automation: the differences
The three are not equal. A chatbot answers a question. An automation runs a fixed, predictable set of steps. An agent chooses its own steps based on context, with a goal to reach. The more autonomy, the higher the potential value — but the higher the risk of error and the cost, too.
The use cases worth it
- Customer support: triage, qualify and answer common requests using your documentation, handing off to a human when needed.
- Document processing: extract data from invoices, contracts or emails and file it in the right place, no manual entry.
- Internal assistant: query your business data in plain language (“which customers haven’t paid this month?”) instead of writing a query.
- Onboarding and setup: guide the user and pre-fill their workspace from a few pieces of information.
The gimmicks to avoid
Adding a “magic” chatbot that doesn’t know your data, a “generate with AI” button that produces text nobody reads, or an autonomous agent let loose on an irreversible action (send money, delete data): those are demos, not products. A good use case is recognisable by one simple thing — the user saves measurable time and trusts the result.
An AI agent is only valuable if it saves measurable time on a task the user hates — the rest is just a demo.
The pitfalls: cost, reliability, UX
Three pitfalls come up every time. Cost: every model call is paid for, and an agent that “thinks” in a loop can multiply the bill tenfold — cap the steps and add caching. Reliability: a model makes mistakes, so validate its outputs, keep a human in the loop on sensitive actions, and log every decision. UX: show what the agent is doing, let the user correct it, and never hide an error behind a confident sentence.
How to start small
Start with a single, measurable use case on a reversible action. Give the agent a narrow scope and precise tools rather than total freedom. Measure the success rate and the cost per task before expanding. That is exactly the V1 approach: at Khufu we ship a first useful agent building block inside a production-ready product, delivered in 7 days at a fixed price (€15,000) — enough to validate real value before investing further.