An agent should workinside the product.
A chatbot produces a response. A production agent can plan a job, use the software's real capabilities, inspect what happened, and keep going until the work reaches a useful end.
The chat may be the interface. It is not the agent.
- 01Understand the request
- 02Plan the work
- 03Use a capability
- 04Observe the result
- 05Continue or finish
The difference is the loop.
The model does not receive one large prompt and magically operate the software. An agent runtime gives it a cycle: understand the situation, decide what comes next, use a capability, read the result, and revise the plan.
The runtime might be the OpenAI Agents SDK or Anthropic Agent SDK. It supplies reasoning, tool selection, and the execution loop. The application still has to supply the capabilities, context, permissions, state, and evidence that make the loop dependable.
Its range comes from the product.
The agent becomes more useful as the product opens more of its capabilities to it. Each capability gives the agent a precise way to understand, inspect, or change something the software already knows how to handle.
It does not need every screen, schema, and instruction loaded into one enormous prompt. It discovers the capabilities relevant to the current goal and loads their context when needed. Underneath, those capabilities are controlled tools that carry the application's validation, permissions, business rules, and data model.
One ordinary request.
“Prepare this workspace for the new team. Keep our existing rules, connect the approved data, set access, and show me what still needs a decision.”
- 01
Read the current workspace, account rules, team, and connected data.
- 02
Discover the product capabilities relevant to this goal.
- 03
Plan the changes and identify anything that needs the user’s approval.
- 04
Use the product’s existing operations to configure data, rules, and access.
- 05
Verify the result and return what was completed and what still needs a decision.
One goal can cross several parts of the product. The agent chooses the capabilities and works toward the outcome instead of asking the user to find every screen and perform every step.
What production changes.
Context is selected.
The agent receives the state needed for this task, not everything the company knows and not whatever happens to fit in a prompt.
Control stays with the product.
Existing permissions, validation, confirmations, and business rules still apply when an action comes from the agent.
Behavior is measured.
Runs are traced and evaluated against repeatable scenarios so the team can see what works and improve what does not.
That is what I mean bya production agent.
Not a second chat window and not a model with an API key. A piece of product engineering that lets a user state the outcome and lets the software reason, act, observe, and work toward it.