The web was designed around a person looking at a page. A personal agent changes the caller: software may read the data, compare options and ask a person to approve the last step.
That does not make every website obsolete. It makes the hand-off between information and action a product design problem.
OpenAI's ChatGPT search provides answers with links to web sources. ChatGPT agent can navigate websites and do multi-step work, while asking permission before consequential actions. Those are different layers. One helps answer "what is true?" The other attempts "what should happen next?"
For developers, I think the second layer changes what a good integration looks like.
Start with a real task
Take a small retailer that wants an assistant to help a customer choose a desk chair. The customer says: "I need one under my budget, delivered by Friday, with a return policy I can live with."
A brittle integration hands the agent a catalogue page and hopes it can read the cards. A useful one makes the relevant facts explicit: exact variant, price, stock, delivery area and date, return terms, and when those facts were checked. The agent can then compare candidates and cite the records that support the choice.
The request is not "generate a chair recommendation." It is a workflow with inputs, live checks and a decision gate.
Make failure legible
Suppose stock changes between the shortlist and checkout. The integration should not silently substitute another chair. It should return an out-of-stock state, preserve the customer's constraints and ask for a new choice. The same applies when the delivery date moves, an API returns partial data, or the exact variant is missing.
Useful interfaces distinguish "not found" from "not checked" and "unavailable." They also expose a timestamp. An agent cannot compensate for stale data by writing a more confident sentence.
Separate reading from committing
Discovery can be broad. A purchase, booking or message is narrower. Give an agent read access to compare options without giving it an unlimited right to spend money or speak for a user. At the committing step, show the final item, amount, destination and cancellation terms to the person who owns the decision.
This separation also helps debugging. If the task fails, a trace should tell you whether the source data was wrong, the selection was wrong, or the final action failed. "The agent said it worked" is not an audit log.
Package repeat work
A prompt is a poor place to store a recurring workflow. Write down the task's inputs, allowed tools, check cadence, success condition, exceptions and human review points. Keep mutable state outside the prompt. Then a different model or interface can run the same job without reconstructing the rules from old chat messages.
I expect the prompt box to remain. It will be the place where a person changes a goal or asks for an explanation. But the value of an agent will come from whether it can complete the right task, check the result and stop at the right moment.
I am not claiming search traffic or websites have already vanished. Search remains useful for navigation and source checks. The builder question is simpler: if a customer's assistant arrives tomorrow, can it tell what your service offers, whether the offer is current, and what action requires the customer's approval?
Tej Pandya is the founder of GrowEasy.ai.
Further reading: OpenAI on ChatGPT search, OpenAI on ChatGPT agent, and the AI Agent Skills team's related essay.
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