Agent Governance and Feedback

This section covers the patterns that govern how agents are controlled, evaluated, and steered toward correct outcomes. Where Agentic Software Construction describes the building blocks of agent-driven workflows, this section describes the control systems that keep those workflows on track.

The core challenge is that AI agents produce plausible output, not provably correct output. They need guardrails before they act, checks after they act, and a closed loop connecting the two. They also need human oversight calibrated to the risk of each action: tight for irreversible operations, loose for safe and reversible ones.

The patterns here form a natural progression. Feedforward controls shape what the agent does before it writes a single line. Feedback Sensor checks report what happened after it acted. The Steering Loop connects both into a system that converges on correct output. Harnessability describes the codebase properties that make all of this work well. And the governance patterns (Approval Policy, Human in the Loop, Eval) define when humans intervene and how you measure whether the whole system is improving.

This section contains the following patterns:

• Approval Policy — When an agent may act autonomously vs. when a human must approve.
• Eval — A repeatable suite to measure agentic workflow performance.
• Human in the Loop — A person remains part of the control structure.
• Feedforward — Controls placed before the agent acts to steer it toward correct output on the first attempt.
• Feedback Sensor — Checks that run after the agent acts, telling it what went wrong so it can correct course.
• Steering Loop — The closed cycle of act, sense, decide, and adjust that turns feedforward and feedback into a convergent control system.
• Harnessability — The degree to which a codebase’s structural properties make it tractable for AI agents.
• Bounded Autonomy — Graduated tiers of agent freedom calibrated to the consequence and reversibility of each action.