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A chatbot is a conversational interface. A workflow defines a sequence of operations. An AI agent uses a model to help choose actions toward a goal. An application may combine all three.
The useful question is where flexible decision-making is needed. If a process always validates a form, looks up an order and returns its status, ordinary application logic may be sufficient. If the next step depends on several uncertain observations, a bounded agent may be worth evaluating.
Map one real process
Write down each step, the data required and the action permitted. Mark which steps are predictable and which require interpretation. Identify who is responsible when the system cannot proceed.
For a fictional customer-support system:
- Read the enquiry and classify the request.
- Retrieve only records the signed-in user may access.
- Draft an answer using approved evidence.
- Ask for human approval before a refund or consequential message.
- Stop when the request exceeds the permitted scope.
Compare the full cost
An agentic design adds execution paths, repeated model calls and harder failure analysis. Evaluate completion rate, tool errors, escalation rate, latency and cost per successful task. An impressive demonstration is only the beginning of that comparison.
Discussion: Which part of your workflow truly needs an agent to choose the next action, and which part should stay deterministic?
Learning reference: Hugging Face introduction to agents.
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