By: Hemang Upadhyay, Strategic Product & AI Leader, RTM Insider
The easiest way to misunderstand an AI agent is to treat it as a better prompt. That framing survives the demo and falls apart in production.
A production agent is not a model following an instruction. It is a product. It has users, permissions, data dependencies, success metrics, failure modes, adoption patterns, escalation paths, and business consequences. Which means it needs product management at least as much as it needs model engineering. Prompt work can make an agent respond better. Product work decides whether the agent should exist, what it is allowed to do, how you will know it is working, and what happens the day it is wrong.
I Learned this from Commerce Platforms, not AI Demos
In complex B2B ecommerce, the hard part is rarely the button a customer clicks. It is everything behind it: catalog structure, product data, pricing logic, channel rules, order handoff, inventory, customer entitlement, support policy, and post-purchase expectations. Add an AI agent to that environment and the same complexity does not disappear. It becomes easier to trigger.
I have seen teams get excited because an assistant can answer a product question, summarize a policy, or draft a next action. Then the real questions begin. Which product attribute did it trust? Was the customer allowed to see that pricing? Did it understand the difference between a draft recommendation and a transaction? Did the answer create a commitment the business is now expected to honor? Those are not prompt questions. They are product questions.
Agents Create Product Risk, not just Technical Risk
Traditional software has visible edges. Users can see the workflow, inputs are structured, and the system generally behaves the same way twice. Agents interpret intent, pull context, choose tools, and produce outputs that vary with the situation. That flexibility is the point, and it is also the risk. An agent can look helpful while operating outside what the user expected. It can finish the task by the wrong route. It can optimize for completion while quietly creating downstream cleanup. None of those are simply model bugs. They are product decisions that nobody made.
Define the Job, Not the Capability
Most agent projects begin with a capability statement: the agent can summarize tickets, recommend products, update records. That is not a job. A product manager defines the job the agent is being hired to do. Who is the user? What decision are they trying to make? Which actions are too risky to automate? Where should the agent stop?
Take a returns agent in a commerce environment. As a capability, it processes a request. As a product, the questions multiply. Its users are service teams and, indirectly, customers. Its job is to resolve eligible cases without creating downstream disputes. Its authority stops at a threshold. It should not treat a damaged-in-transit claim, a warranty exception, and buyer-remorse return as the same thing. Its success is fewer escalations and stable exception rates, not raw throughput. Framed as a capability, you ship a feature. Framed as a product, you ship something you can actually operate.
Permissions are Part of the Experience
Permissions usually get filed under security plumbing. For agents, they are part of the product. If an agent can read, write, recommend, execute, or approve, the user needs to understand that boundary, and the system needs to enforce it. In practice, that might be an agent that drafts a refund and shows its reasoning but will not execute anything above a set threshold without a human pressing approve. That legibility earns adoption. People trust a tool more readily when they can see its limits.
Measure Outcomes, Not Activity
Agent metrics go wrong fast. A high completion rate is not value. A faster answer can still be the wrong answer. The questions worth tracking are about the workflow underneath: did the agent reduce rework, improve decision quality, cut avoidable escalations, help people finish the right work with less friction? That shift moves the conversation from what the agent can do to what actually got better.
Design Failure States First
Every serious product team designs error states. Agents need that discipline with more care than usual. What should the agent do when the request is ambiguous? When sources conflict? When confidence is low? When the action cannot be undone? A responsible agent does not perform certainty it does not have. It knows when to ask, when to stop, when to escalate, and when to hand control back.
Product managers hold the system together. Agentic AI lives between teams. Engineering builds the agent, security defines access, legal weighs risk, data teams govern sources, operations handles exceptions, the business owns the outcome. Without product leadership, each team optimizes its own corner while the agent experience fails as a whole.
Treating agents as products also means giving them a lifecycle. Real products are launched to a defined audience, watched against metrics, revised when the data shifts, and retired when they stop earning their place. An agent that quietly degrades because a data source changed or because a policy moved underneath it is not a one-time bug. It is a product to maintain, with an owner accountable long after launch day.
The first phase of agentic AI was about what is possible. The next is about what is worth shipping and running well, and the difference between the two is product discipline, not prompt engineering.