The adaptive loop

Bridging the gap between open and closed loops. A new pattern designed to adapt and evolve.

Loop comparison example: Watering the lawn

Log lines below are conceptual, and NOT yet real output of a
              system

Open loop: System activates regardless of environment

[LOG 07:00PM] → Executing sprinkler program: Duration 10min
              
[NOTE] → Schedule triggered. No environmental input
              considered.

Closed loop: System conditionally activates if the environmental definitions are met

[LOG 07:00PM] → Checking conditions: Rain = False 
[LOG
              07:00PM] → Executing sprinkler program: Duration 10min
              
[NOTE] → Conditional execution. Weather input verified.

Adaptive loop: System analyzes non-planned actions and suggests changing the goal to align with the observed actions.

[LOG 08:00AM] → Manual activation detected. 
[LOG 08:00AM] →
              Repetition pattern established: 4 of last 5 days 
[INFERENCE]
              → Behavior deviates from programmed 07:00PM schedule
              
[SUGGESTION] → Recommend adjusting goal: 
→ Option A:
              Add 08:00AM to daily schedule 
→ Option B: Reevaluate
              objective — replace scheduled watering with optimize soil hydration

Adaptive Loops are intelligent systems that don't just react—they reflect. They use contradictions, unexpected outcomes, and epistemic tension as inputs to guide their evolution. From gathering more data, to adjusting internal beliefs, all the way to reframing their original goals, Adaptive Loops treat “being wrong” as a signal—not a failure.

About Cogniscient

Cogniscient is an open-source initiative exploring the design and application of adaptive loop control systems. While the GitHub repository is currently a placeholder, it will soon host:

Foundational research
Proof-of-concept demonstrations
Reference implementation of an exception-driven control system

GitHub Repo