Even the best-scoped AI projects fail without the right people behind them. Most early agent initiatives stall because no one knows who owns what.
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 goals, data access, and user experience. That means it touches multiple teams, each with different priorities. Without coordination, even simple changes can turn into roadblocks.
Before you start building, define the team that will bring the agent to life and keep it running. At a minimum, you need four key roles:
- A subject-matter expert: the person who understands the real-world task the agent will handle.
- A project manager: someone who tracks progress, deadlines, and alignment across teams.
- A technical implementer: usually a front-end or full-stack developer who can integrate the agent into your systems.
- An AI specialist: someone 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. What you do need are people who understand how their part of the system works and who can collaborate effectively.
At Terminal Roast, Taryn takes the lead on organizing the team. She knows that the agent will touch multiple parts of the business, so she calls a short planning session with everyone involved.
Adrian, the lead barista, becomes the subject-matter expert. He provides real examples of how customers talk about coffee flavors and what kinds of feedback are most useful.
Gideon, the tech lead, handles the technical setup on the website and manages integration.
Ross, the accountant, will later track how much value the project creates compared to its operating costs.
Taryn 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 most projects later on. This step might seem like project management 101, but it matters more for AI than for other kinds of software projects. Unlike traditional systems, an agent’s behavior can change as data or prompts evolve. That flexibility is valuable, but it also introduces new risks. If no one owns oversight, those risks become costly surprises.
Establishing clear ownership before you start ensures that every part of the lifecycle 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 when the agent is ready for deployment.
When assembling your team, focus on two questions:
- Who understands the real problem the agent is solving?
- 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. That balance ensures that you maintain control while still benefiting from external expertise when necessary. Teams that set this up early are far more likely to reach production because they know exactly who to call when decisions need to be made.
Terminal Roast’s project works because every part of the workflow has a clear owner.
The subject-matter expert defines what good looks like.
The technical lead makes sure the agent can deliver it.
The project manager connects those efforts to a real business outcome. Most failed pilots never identify that structure. A lot of teams try to move fast and figure it out later, but AI projects really don’t tolerate unclear ownership. A small, well-aligned team will always outperform a large, loosely organized one.
Action: List the key people who need to be involved in your agent project.
Assign clear responsibility for business goals, technical implementation, and ongoing oversight before any development begins.
