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User

Pattern

A reusable solution you can apply to your work.

Understand This First

  • Problem – the user is defined by the problem they experience.

Context

At the strategic level, the user is the person whose workflow, pain, or desire the product directly touches. While the Customer decides whether to buy, the user decides whether to use, and continued use is what sustains a product over time. Understanding the user is a prerequisite for designing features, writing User Stories, and building toward Product-Market Fit.

The user and the customer overlap completely in some products (a freelancer buying their own invoicing tool) and barely at all in others (a child using educational software purchased by a school district). Treating them as interchangeable leads to products that sell but collect dust, or products that users love but no one will fund.

Problem

Who will actually interact with this product, and what does their day look like? Teams that focus exclusively on the customer’s purchasing criteria often build products that look great in a demo but fail in daily use. Conversely, teams that obsess over user delight without understanding the customer may build something beloved by a handful of people and funded by no one.

Forces

  • User needs and customer needs diverge. The buyer cares about reports and compliance; the user cares about speed and simplicity.
  • Users resist change even when a new tool is objectively better, because switching costs are real.
  • Diverse user populations within a single customer mean different skill levels, workflows, and expectations.
  • Users adapt. They build workarounds and habits that make the current pain tolerable, masking the true depth of the Problem.

Solution

Build a concrete picture of the user. Not a demographic profile, a behavioral one. What does this person do on a Tuesday morning? What tools do they already have open? What task takes longer than it should? What makes them groan?

Observe users in their actual environment whenever possible. Interviews reveal what people say they do; observation reveals what they actually do. The gap between the two is where product insight lives.

Create user profiles that are specific enough to drive design decisions. “A junior developer at a 30-person startup who joined two months ago and is still learning the codebase” tells your team far more than “developers.” When directing an AI agent to generate UI or workflow code, include this kind of user context in the prompt. It changes the result meaningfully.

Tip

When writing prompts for an AI agent that will generate user-facing features, describe the user explicitly: their skill level, their environment, their goal, and their likely frustrations. An agent prompted with “the user is a non-technical marketing manager using this on a laptop between meetings” will produce different (and better-targeted) output than one prompted with “add a dashboard.”

How It Plays Out

A team building an internal deployment tool interviews the operations engineers who will use it. They learn that deploys happen at 2 AM during maintenance windows, on laptops with poor connectivity, often under stress. This context drives design decisions: large click targets, offline-capable status checks, and confirmation dialogs that are hard to dismiss accidentally. None of this would have emerged from the customer conversation with the VP of Infrastructure.

A product manager asks an AI agent to design an onboarding flow. The first version is exhaustive: twelve steps covering every feature. After observing actual users, the PM discovers most new users have a single urgent task on day one. The revised prompt tells the agent: “The user is a new hire who needs to submit their first expense report within an hour of account creation. Design an onboarding flow that gets them to that goal immediately and introduces other features later.” The agent produces a focused, effective flow.

Consequences

Understanding the user leads to products that people actually use, recommend, and integrate into their work. High usage strengthens the case for renewal and expansion with the Customer.

The risk is user capture: optimizing so heavily for current users that the product becomes hostile to new ones. Power users accumulate influence and request features that raise the complexity floor for everyone. Balancing the needs of new users, experienced users, and the customer requires ongoing judgment.

User research takes time, too. In fast-moving markets, the cost of thorough user understanding must be weighed against the cost of shipping late. Agentic coding helps here. An AI agent can rapidly prototype multiple versions for different user segments, letting you test assumptions faster than traditional development allows.

  • Contrasts with: Customer — the customer pays; the user uses.
  • Depends on: Problem — the user is defined by the problem they experience.
  • Enables: User Story — stories express what the user needs to accomplish.
  • Enables: Use Case — use cases describe the user’s interaction in detail.
  • Refined by: Product-Market Fit — fit is measured partly by whether users would be disappointed if the product disappeared.
  • Uses: Bottleneck — the user’s workflow often reveals where the real bottleneck lies.