When AI stops answering and starts acting.

A chatbot can suggest a plan. A system connected to tools can try to carry it out. That change sounds small until the output stops being a paragraph and becomes a changed file, a scheduled job or a decision inside another application. The product question shifts from whether an answer sounds convincing to whether the resulting action is appropriate.

In its December 2024 explanation of agent architectures, Anthropic distinguishes a workflow that follows predefined steps from an agent that chooses its own process and tools. It also describes the cost and latency trade-offs of adding autonomy. That is a useful architectural distinction, rather than a claim that every product using the word “agent” works the same way. Read Anthropic’s explanation

Imagine a fictional research assistant preparing a supplier comparison. A fixed workflow might collect three documents, extract prices and put them in a table. An agent might notice a missing specification and decide to search for another document. The second approach adds flexibility. It also introduces another decision that the person using it needs to understand. Neither arrangement should quietly turn a comparison into a purchase.

Our view: the strongest agent products will make their boundaries easy to see. A user should know what the system can read, what it can change and when approval is needed. A visible record of actions can matter more than a charming chat window. Give the system a narrow job, a clear finish line and a way to hand uncertainty back to a person. The impressive demo is the beginning of the conversation; predictable behaviour is what makes the product useful.