Implementing Autonomous Agent Loops with Hermes

The Shift to Loop Engineering

Loop engineering moves the developer role from crafting individual prompts to designing autonomous systems that drive agents. Instead of manually checking outputs and re-prompting, developers define an end goal and a system of constraints that allow the agent to iterate, correct errors, and manage state across turns. This approach relies on five core components: context management, feedback quality, verification gates, termination conditions, and cross-turn state tracking.

Deterministic Loops for Automated Testing

Deterministic loops are used for tasks with clear success criteria, such as passing test suites or successful compilation. By deploying the Hermes agent with self-evolving skills, developers can monitor production environments for failures. When a commit breaks production, the agent triggers Claude Code in non-interactive mode. The agent then executes a workflow that identifies the breaking issue, applies fixes, and runs tests repeatedly until all checks pass, at which point it uses the GitHub CLI to commit the resolution.

Non-Deterministic Loops and Adversarial Verification

Non-deterministic tasks, such as UI design, lack binary success conditions and often suffer from generic AI output. To solve this, the author implements an adversarial loop using an 'AI Slop Detector' skill. This setup pairs a builder model (Claude) with a separate verifier model (GPT). The verifier checks the output against a library of 'slop' patterns. If the verifier identifies issues, the agent iterates on the UI until the verifier finds no further patterns. Because Hermes supports self-evolving skills, the agent updates the detector's instructions based on feedback, strengthening the verification process over time.