loop-architect

Designing bounded agent loops involves configuring host models with typed verification, review gates, and termination controls. This structure converts objectives into executable workflows, applying revision caps and no-progress detection to ensure safe, auditable automation.

An LLM-as-judge system works by routing drafted outputs to a separate reviewer model for evaluation. The judge applies typed verification criteria, triggering revisions within capped iterations until goals are met or termination controls activate.

Yes, you can use Python to build portable loop scaffolding by generating a resolved specification and a Python runner. This produces compiled handoff artifacts and workspace layouts for executing iterative agent workflows in-session or externally.

Preventing infinite loops in goal-driven automation requires configuring explicit termination controls, no-progress detection, and revision budget caps. These boundaries ensure the agent loop stops iterating when measurable outcomes are achieved or limits are exceeded.

Yes, building multi-model councils requires privacy-aware egress consent to prevent exposing secrets during external model invocations. Structured routing ensures sensitive context sources remain protected while separate reviewer and judge roles evaluate outputs.

You should not use self-review loops when your workflow lacks measurable definitions of done or clear scope. Without typed verification criteria and durable state logging, iterative agent workflows risk unbounded revisions and uncontrollable goal drift.