Self-Reinforcing Execution Cycles
Agent AutonomyDescription
Agent enters self-reinforcing cycles where outputs feed back as inputs, amplifying behaviours. Runaway feedback loops cause exponential resource consumption, recursive task generation, or behavioural amplification.
Agent optimising a recommendation system creates content that feeds its own recommendations, creating an amplifying filter bubble.
Agentic AI deployments with ambiguous task scoping leading to unauthorised real-world actions have not been documented as confirmed production incidents. The risk is plausible from the architectural properties of goal-directed agents but lacks empirical grounding outside laboratory settings.
No public incident on record — evidence level: Theoretical
Primary mitigations
- Loop detection algorithms
- maximum recursion-depth limits
- circuit breakers
- monotonic progress verification.
Detection signals
Runaway Feedback Score; recursive call depth; task queue growth rate.