AI Feedback Loops: Applying Cybernetics and Control Theory to Agent Design

# Why feedback matters

# The control-system mapping

Treat the agent as one component inside a control loop rather than as a standalone controller. The article maps common control concepts to agent architecture:

• Reference / set point = approved goal or target state.
• Controller = model, planner, and orchestration logic.
• Plant = the application, infrastructure, or external environment the agent affects.
• Sensor = telemetry, API readback, evaluation checks, or human observation.
• Actuator = authorized tool, command, transaction, or workflow.
• Error signal = evidence of the gap between required and observed outcomes.
• State estimator = logic that assembles observations and context.
• Supervisor = accountable human or higher-authority control system.

A thermostat frames the requirement clearly: you need a measurement, a reference, and repeated checks of whether actions closed the gap.

# Concrete design priorities

1) Operational definition of success. Objectives must include the measurement, acceptable range, evaluation window, and constraints. "Improve performance" is insufficient without those specifics.

2) Stable correction. Retries and automatic correction can themselves become incidents if they mask unresolved faults or cause oscillation.

3) Observability versus controllability. Observability is having measurements that reliably reflect the effect of actions. Controllability is having enough authorized responses to address likely disturbances. The two are distinct and both required.

4) Supervisory loop and bounded authority. A higher-authority supervisor should be able to intervene. Ashby's Law is invoked: having enough response options matters more than unlimited autonomy.

5) Failure-path validation before authority expansion. Test the whole coordination loop and validate how the agent behaves when sensors, tools, or the environment are wrong.

# Open-loop versus closed-loop and feedback types

# What feedback should not automatically do

# Relationship to reinforcement learning and governance

Reinforcement learning relates to feedback but is not the same as operational control. Governance must decide what corrective signals are routed into learning pipelines versus what remains transient, supervised, or human-reviewed.

# Illustrative scenario

The article uses a capacity-management example: an agent adds instances when high utilization appears, sees a successful tool response, and reports resolution. Without measuring end-to-end performance after the change, the agent cannot determine whether the problem is actually fixed. The design must specify what to measure, when, and how long to wait before declaring success.

# Practical takeaway