Concepts
Team-based agentic development
A model where teams, artifacts, and agents are coordinated together instead of optimizing only for individual output.
Team-based agentic development treats the team as the unit of optimization.
Problem
Most AI workflows focus on helping one person produce output faster.
That is useful, but it does not solve:
- unclear ownership of agent-produced work
- artifact drift between chats and shipped code
- inconsistent review standards across a team
- fragile environments that only work on one engineer's machine
- organizational knowledge stuck in one person's head
What changes
ClosedLoop.ai uses artifacts, plans, loops, critics, judges, and self-learning to keep execution connected to team intent.
- Artifacts are the durable unit of work, not chat history.
- Loops are executed by agents against registered compute targets with audit trails.
- Critics review plans before implementation.
- Judges score outputs against artifacts.
- Self-learning turns individual run patterns into organizational knowledge through
org-patterns.toonand/push-learnings//pull-learnings.
Why it matters
The result is not just faster code generation. It is a tighter system for turning intent into shipped work:
- Tickets become tasks. A ticket maps cleanly to a plan with acceptance criteria and a loop.
- Epics become features. A PRD generates a plan that can decompose into many loops, potentially in parallel.
- Sections of your quarterly roadmap land in a few PRs. When the system is running, review and approvals scale, not typing speed.
Team-based agentic development is what produces the step change from "AI helped me code this module" to "the team shipped this feature while engineers reviewed."