AI Agent Loops Work Like Managed Teams
The useful work in agent automation is defining the goals, operating rules and handoffs that let specialised agents coordinate without constant instruction.
Developed from a conversation between Pete Winn and Andy David
A conventional software loop repeats an operation a set number of times or until a condition becomes true. An agent loop lifts that pattern to the level of a goal. The agent keeps working until the stated outcome is reached, while the person designing the workflow defines how success is judged. That makes loop engineering less like issuing individual prompts and more like managing a team through rules and processes.
In a coding workflow, one agent might monitor GitHub for new issues and trigger a job whenever a change appears. The agent handling that job can iterate until its goal is satisfied, while another watches for the resulting pull request and starts the next stage. Each role may contain its own loop, but together they form a coordinated chain of specialised responsibilities.
Calling the whole arrangement a loop can obscure what has actually been built. At the organisational level, it’s a set of triggers, processes and handoffs, much like the machinery used to coordinate human teams. AI agents make that machinery executable at greater speed, but they don’t remove the need to design it. A new issue triggers work, a defined condition ends it, and the resulting pull request activates what comes next.