Tony Ojeda 4H AGO

Having a team of specialized AI agents creates a new problem: someone still needs to decide which agents should work on a project, what order they should work in, what each one needs from the others, and whether the project is actually finished. Without coordination, the human becomes the project manager, manually moving context and outputs between agents. I created a project-manager agent that acts as the orchestrator for my AI team. I give it an objective, and it determines what work needs to happen, selects the appropriate specialist agents, sequences their work based on dependencies, and presents the execution plan to me before anything starts. Once I approve the plan, it coordinates the agents, manages their handoffs, tracks project state, and maintains enough persistent context for the work to continue across sessions. Step-by-step: 1. I create several specialized agents with clearly defined responsibilities, capabilities, and expected outputs. 2. I create a project-manager agent that knows what each specialist does and is instructed to orchestrate the work rather than perform specialist work itself. 3. I give the project manager a high-level objective. It analyzes the goal, inspects the project context, identifies the required work, and selects the appropriate agents. 4. I have it create an execution plan showing which agents will be used, what each one will do, their dependencies, and the order of execution. 5. I require human approval before execution begins. I can approve the plan, narrow the scope, change the sequence, or redirect the project as needed. 6. Once the plan is approved, I let the project manager delegate each task to the appropriate specialist and pass relevant context and prior outputs between agents through structured handoffs. 7. I track progress and project state as the agents complete their assignments. If an agent uncovers new work, fails review, or changes the project assumptions, the project manager updates the plan and routes the next work accordingly. 8. At the end of the session, I save the current state, completed work, important decisions, and next actions so another session can continue without reconstructing the project from scratch. Instead of personally coordinating every AI agent, I manage the project at a higher level: I define the objective, approve the plan, review important decisions, and evaluate the result. The AI project manager handles the coordination layer, turning a collection of specialized agents into a team that can execute complex, multistep projects coherently.