An AI agent uses a model, tools, and defined rules to work toward a goal, check results, and hand off when needed. The model supplies reasoning and output; the surrounding workflow determines what information and actions are available.
Example: prepare a vendor onboarding review
Northstar Supplies submits an onboarding packet. Procurement asks: “Prepare an onboarding review from the intake packet, approved checklist, and current vendor record. Flag missing evidence and draft a document request for review. Do not approve the vendor or send a message.”
The agent consults only those three sources. It checks the packet against the checklist, compares relevant details with the current record, identifies missing information, and prepares a draft for procurement. The sample below uses fictional information and illustrates the expected kind of output; it is not a real vendor result.
Northstar Supplies — onboarding draft
- Finding: A current insurance certificate is missing from the intake packet.
- Draft request: “Please provide a current insurance certificate so procurement can continue its review.”
- Decision: Vendor approval has not been made; procurement must review the draft.
The agent’s useful action is preparing a grounded draft. If a source is unreadable or details conflict, the agent should say what it could not verify and route the case for review.
Review rule: Approval and sending remain with a person.
The agent completes the review when the evidence is clear. If information is missing, it checks further or hands the case to a person.
What makes this an agent?
- Job: Prepare a review.
- Context: Use the approved packet, checklist, and record.
- Tools: Determine what the system can read or change.
- Rules: Describe how to handle missing evidence and where to stop.
Together, these let the system choose a next step within a bounded task.
A model alone could summarize the submitted form. A fixed automation could check that required files are present. The example agent compares evidence across allowed sources and adapts its draft to what it finds. That flexibility needs clear permissions and a way to check the result.
When the pattern fits
Use an agent when:
- The request varies.
- The next step depends on what approved information shows.
- A person can check the output or handle exceptions.
A fixed workflow or direct model call may be easier to define for a stable sequence or single transformation.
Before giving an agent a consequential action, decide:
- Who owns unresolved cases.
- Which facts require verification.
- What the agent must never do.
For a comparison with chatbots and automation, read AI agent vs. chatbot vs. automation. For the system components, continue to AI agent architecture and the agent harness.