Towards a Framework for AI Agent Design, Part 1

Everyone is interested in AI Agents these days. They hold great promise in automating work, saving time, and delivering better, faster service. How can we make AI Agents that deliver on these hopes?

AI Agent Design Framework

I'm developing an approach for how AI builders can approach Agentic AI design strategically. This is based on my ongoing experience in [MindStudio.ai](http://mindstudio.ai/)'s AI Agent Builder Bootcamp, now underway with Cohort 2. It's also based on my years of experience with strategic user experience and product discovery. What I've seen is that successful software products usually have structured thinking about purpose, audience, constraints, and outcomes. What we need for AI agents is a similar framework to maximize our chances for a successful agent.

Who Can Benefit

This advice is for AI professionals, Product Managers, UX Designers, or anyone who is creating AI Agents.

A life cycle point of view helps us plan for the big job picture.

A useful structure for AI Agent design should help builders account for the complete business environment the AI Agent is designed for. Thinking in terms of an AI agent life cycle pushes you to think holistically. When you understand the big picture environment that your AI agent will work in, you can find the best configurations and treadeoffs. You can also account for problems and edge cases. A life cycle model helps us understand all of these factors. This raises our chances of developing AI Agents that are useful, usable, and delightful to use.

AI Agents are workers whose employment history we create in advance

To put it another way, think of AI Agents as workers that we intend to hire. The twist is that with AI Agents, we can plan the whole story of their work history in advance. As with any story, our AI agent's tour of duty will have a beginning, a middle, and an end. We can use this story to plan in advance for effective training, launch, and usage. Everyone affected by the AI agent will know what the

The AI Agent Life Cycle

As part of your AI Agent design and development strategy, think about each of the following stages. Determine what you need to know and do at each stage. Define what the inputs and outputs are for each, and how each connects to its following stage. Audit what you know and don't know. Make a plan to get what's missing.

Discovery

• What is the problem you're trying to solve? For whom? What are the business "stakes"? Discover your MSCW ("Moscow"): Must-haves, should-haves, could-haves, won't-haves.

Design

• Solve the problem with workflows & screens/voice/etc.. This is the interaction design, the graphic design, and the content that people will see when using the Agent. Also design how the agent will interface with other systems to read and write data.

Build

• Create your agent. Cover the main workflows but test for the edge cases. Prototype it with real users and real data until it's solid.

Launch

• Consider a beta launch or a pilot program rollout rather than all at once. Update if needed after the pilot. Treat the launch like a film: Previews, internal press release, full demos/walk-throughs, training, owner, a support plan.

Maintain

• Update your agent as its components or services change: New or updated API calls and service connections. The AI model that the agent uses itself may be reviewed and changed. You may need to update based on policy or legal changes.

Upgrade/Sunset

• Over time, you may add more features and workflows to your agent to add more value: Adding some should-haves and could-haves, maybe a new must-have. A roadmap is handy for planning and communication. It's also likely that the situation will change so dramatically that a new agent using new systems is the answer. In that case, plan for keeping the old agent running while the replacement agent gets a thorough test drive. Broadcast a schedule and plan for how the old agent will be deactivated.

What are your thoughts on how we can approach AI Agent design for maximum effectiveness and success?

Comments, suggestions and criticisms welcome.

Follow me for Part 2, where we look at the functional and topic aspects of AI Agent design.