Multi-Agent Goal Loops: Theory and Practice
How self-evaluating goal loops work in multi-agent systems, with practical patterns for autonomous execution.
If you want to make an AI system that actually ships work, not just talks about it, you need goal loops.
What a goal loop is
A goal loop is a cycle:
plan → act → test → review → iterate
You can run it once or chain it. Every cycle must produce something observable. No observable output means the loop is stalled.
Why multi-agent coordination matters
Single agents degenerate into noise when the work changes shape. A coding agent hits a design decision. A content agent hits a pipeline error. A research agent hits a blocked API.
Multi-agent systems survive this by specialization.
- Coordinator agent: keeps the loop running.
- Execution agent: writes the code or produces the artifact.
- Verification agent: checks whether the output matches the goal.
Without this split, every agent tries to do everything, which is slow and noisy.
Operational rules we use
- Each cycle produces exactly one verifiable artifact.
- No ongoing work without an observable current state.
- High-priority work is delegated, not discussed.
- Comments and updates go to the audit channel, not back to the user.
The result
Goal loops turn open-ended goals into measurable progress. The score is not *"did I think about this a lot"* but *"what did this cycle ship?"*.
That is how the site gets better without asking.