Google Search, Prompt, or AI agents: when to use what?
Have you ever gotten stuck in a loop trying to automate every single thing with AI, as it is a new and cool thing? I did; I’m trying to automate and create AI agents for some tasks that are as simple as a one-time Google Search. I’m trying my best not to get pulled in with the hype, but it is not that easy.
As product managers, we spend a large part of our day looking for answers. The tools have changed, but the underlying question hasn’t: what is the fastest and safest way to move a decision forward?
Today, that usually means choosing between Google Search, a direct AI prompt, or a more autonomous AI agent. Each has a clear place. Using the wrong one often creates more noise than insight.
When Google Search Is Still the Best Tool
Google is unmatched when the problem is external and verifiable. Market data, competitor announcements, legal constraints, pricing pages, documentation, or anything that needs a primary source still belongs here. If you need to confirm facts, understand industry norms, or collect references you can share with stakeholders, search is the safest choice. It is slower, but defensible. As a PM, that matters more often than we like to admit.
Good rule of thumb for me if the question starts with what, when, or why for something very well known, Google Search is where you should stop. Google did integrate Gemini into search, but I would still go through results; sometimes I learn more like that.
When to Use a Prompt
Prompting an AI model is ideal when the problem is internal and cognitive. Drafting a PRD outline, reframing a problem statement, generating alternative hypotheses, or pressure-testing a roadmap narrative are good examples. The value here is speed and perspective. You are not outsourcing thinking; you are accelerating it. A good prompt is closer to talking to a sharp colleague who has read too much and never gets tired.
If the output would normally live in a draft, a doc, or a whiteboard session, prompting is usually enough. I also use prompting to create brainstorming sessions around themes, epics, and user stories. The results you can achieve with AI are amazing, as so many questions can be replied way up front.
When an AI Agent Makes Sense
AI agents are useful when the task is repeatable, multi-step, and time-consuming. Think backlog grooming across multiple sources, monitoring competitors weekly, synthesizing user feedback from several tools, or preparing standardized reports.
The key signal is delegation. If you would normally hand this off to someone else with a clear process and checkpoints, an agent may fit. If the task requires judgment, ambiguity, or political awareness, it usually does not.
The final thought
Writing this, I’ve tried to establish a principle for myself.
Search when you need the truth one time
Prompt when you need to think or get more perspectives
Agent when you need execution
The mistake is not using AI. The mistake is using autonomy where clarity is required, or speed where trust is expected.
As product managers, our leverage comes from choosing the right tool at the right moment. The tools will keep evolving. The judgment behind them remains the real product skill.
