Playbook: The Single Most Important Step in Building an AI Agent

Every great AI agent, from a simple customer service bot to a complex GUI automation tool like UI-TaRS, begins not with a line of code, but with a line in the sand. It begins with a clear, disciplined, and often ruthless definition of its scope. The allure of building an all-powerful, general-purpose agent is a siren song that has led countless ambitious projects to failure. The path to a successful agent is not about building everything you can imagine, but about building the right thing first.

Executive Overview

In AI product management, the discovery and scoping phase is the most critical predictor of success. An agent without a well-defined scope is a solution in search of a problem. It will lack focus, be impossible to evaluate, and will likely fail to deliver tangible value. This playbook introduces the Three-Axis Framework, a simple mental model for defining your agent’s scope. By deliberately choosing a position on each of these axes—Domain Breadth, Task Complexity, and Autonomy Level—you can create a clear, defensible project plan that maximizes your chances of success.

Axis 1: Domain Breadth (Laser Focus vs. Pan-Knowledge)

The first and most important decision is how much your agent needs to know.

Niche Agents operate in a narrow, specific domain. They are experts in one thing, such as answering questions about a single company’s HR policies or processing a specific type of invoice. This laser focus makes them easier to build, train with high-quality data, and evaluate.
Generalist Agents attempt to operate across a wide range of domains. While this is the long-term vision for AI, building a successful generalist agent from scratch is extraordinarily difficult and resource-intensive.

The Play: Always start with a niche. Identify a high-value, narrow domain where your agent can become a true expert. You can always broaden the domain later, but you must first win a specific beachhead.

Axis 2: Task Complexity (Simple Actions vs. Compound Workflows)

Next, define the complexity of the tasks your agent will perform.

Simple Tasks are discrete, single-step actions. Examples include retrieving a single piece of information (“What is our Wi-Fi password?”) or classifying an incoming email.
Compound Tasks require multiple steps, context, and reasoning. Planning a business trip, for example, involves searching for flights, finding hotels, checking calendars, and then booking, all while keeping constraints in mind.

The Play: Begin by mastering simple tasks. Prove your agent’s value and reliability by automating single, high-frequency actions. Once you have a robust library of simple task capabilities, you can begin to chain them together into more complex, compound workflows.

Axis 3: Autonomy Level (Co-Pilot vs. Fully Autonomous)

Finally, decide how much independence your agent will have.

Human-in-the-Loop (Co-pilot) Agents assist human users. They suggest actions, automate steps with confirmation, or handle routine work while escalating exceptions. This is the most common and successful model for enterprise agents today.
Fully Autonomous Agents operate without human intervention. This requires an extremely high level of confidence in the agent’s reliability and safety, as the cost of an error can be significant.

The Play: Start with a human-in-the-loop approach. This not only creates a crucial safety net but also provides an invaluable feedback loop. Every time a human corrects the agent or takes over, you are gathering data on its weaknesses, which is essential for future improvement. Only grant full autonomy for low-risk, highly predictable tasks.

Implementation Guidance: Your First Scoping Session

Gather your team and use this framework to ask critical questions:

Problem Definition: What specific, measurable problem are we solving? Is it a problem best solved by AI?
Domain: What is the single, narrowest domain we can target to prove value? (Axis 1)
Tasks: What are the top 3-5 simple, high-frequency tasks within that domain? (Axis 2)
Autonomy: Can we start with the agent acting as a co-pilot, suggesting actions to a human? What is the clear, safe escalation path for tasks it can’t handle? (Axis 3)

Answering these questions will give you a clear, defensible Minimum Viable Product (MVP) for your agent.

What’s Next: An Action Checklist

Before you write any code, complete this checklist:

Define Your Axes: Write a single sentence for your agent’s position on each of the three axes.
Write a Mission Statement: Based on the above, write a one-sentence mission for your agent (e.g., “To act as a co-pilot for hotel staff by autonomously answering the 10 most common guest questions.”).
Identify Your Data: What data do you need to accomplish this mission? Where will it come from?

Scoping is not a one-time event, but an ongoing process. By starting with a disciplined, focused approach, you build a foundation for success, allowing your agent to grow in capability and value over time.

References

Product Management for AI/ML: A Framework for Product Managers to Navigate the AI/ML Product Lifecycle. (2023). Towards Data Science.
Scoping & Discovery: A Guide to the Discovery Phase for Machine Learning Projects. (2022). Nexocode.
ML Project Design: Designing Machine Learning Systems. (2022). O’Reilly Media.