How to Build and Ship your First AI Agent: Building the Right Team

0:04Even the best scoped AI projects fail without the right people behind them. Most early agent initiatives stall because nobody knows who owns what. These kinds of ownership gaps create slowdowns, approval loops, and confusion once the project starts to move. An AI agent isn't just a piece of software. It sits at the intersection of business

0:25goals, data access, and user experience. This means it touches a lot of different teams, each of whom have really different priorities. Without coordination, even simple changes can turn into huge roadblocks. Before you start building, define the team that's going to bring the agent to life and keep it running. These could be two different teams. At a minimum, you need four key roles. A subject matter expert, the person who understands the real world tasks the agent is going to handle. A project manager, someone who tracks progress, deadlines, and alignment across teams. A technical imple, usually a front-end or full stack developer who can integrate the agent into your systems, and an AI specialist,

1:08someone who knows how to structure prompts, manage models, and troubleshoot LLM behavior. You don't need a machine learning PhD or a large research team. But what you do need are people who understand how their part of the system works and who can collaborate effectively. In other words, people who know your business very well. At Terminal Roast, Taran takes the lead on organizing the team. She knows that the agent is going to touch multiple parts of the business, so she calls a short

1:36planning session with everyone involved. Adrien, the lead barista, is naturally the subject matter expert. He provides real examples of how customers talk about coffee flavors and what kinds of feedback are most useful for him when he's developing new recipes. Gideon, the tech lead, handles the technical setup on the website and manages all of the integrations. Ross, the cafe's accountant, will later track how much value the project creates compared to its operating costs. Taran keeps everyone aligned and ensures that the project continues to serve a clear business purpose. By getting everyone in the same room early, the team avoids the handoff problems that derail a lot of

2:16projects later on. This step might seem like project management 101, but it matters more for AI than other kinds of software projects you might be used to implementing. Unlike traditional systems, an agent's behavior can change as data or prompts evolve. That flexibility is really valuable, but it also introduces new risks. If nobody owns oversight, those

2:40risks become costly surprises. Establishing clear ownership before you start ensures that every part of the life cycle has an accountable owner. Who trains the model, who monitors performance, and who approves updates to tone, policy, or data sources. This clarity keeps projects moving smoothly and prevents delays whenever you feel like your agent is ready for deployment. When assembling your team, focus on two questions. Who understands the real problem the agent is solving? And who has the technical access and authority to implement it? You can supplement these roles with consultants, agencies or vendors as needed, but those two responsibilities should always live inside your organization. You need to

3:25understand them very well. That balance ensures that you maintain control while still benefiting from external expertise whenever you want to bring that in. Teams that set this up early are far more likely to reach production because they know exactly who to call when a decision needs to be made. Terminal Roasts project works because every part

3:46of the workflow has a very clear owner. The subject matter expert decides what good looks like. The technical lead makes sure that the agent can deliver good. The project manager connects those efforts to a real business outcome. Most failed pilots we see never identify the structure. A lot of teams try to move fast, which is great, and try to figure it out later, which is great. But AI projects really don't tolerate unclear ownership. A small, well-aligned team will always outperform a very big,

4:17loosely organized one. [music]