Building Reliable AI Agent Loops

The Loop Architecture

Reliable agent loops require a centralized structure to maintain state and prevent redundant work. Every loop should be defined by a single markdown file containing a 'loop contract' and a persistent log. The contract acts as the system constitution, defining the goal, operational boundaries (what requires human escalation), and standard operating procedures (SOP). The state and log sections track previous attempts, current hypotheses, and completed tasks, which prevents the agent from re-processing the same noise or wasting tokens on failed approaches.

Trigger Strategies and Execution

Choosing the correct trigger mechanism is critical for cost efficiency and reliability. While continuous loops (like standard while-loops) work for well-defined tasks like bug fixing, they are often inefficient. Event-based triggers, such as webhooks from services like Render, allow agents to react to specific incidents. The most effective approach is a 'combo' or workflow trigger, where a script fetches data from a source (e.g., Intercom or server logs) to verify if actual work exists before waking the agent. For complex tasks, the agent architecture should follow a three-stage pattern: an orchestrator agent that plans, executor agents that perform tasks in isolated work trees, and a verifier agent that tests results and attaches evidence for human review.

Self-Optimization

Loops should not remain static. By implementing an 'evolve' loop, the agent can periodically inspect its own configuration, past logs, and conversation history to suggest improvements. This process allows the agent to refine its SOP, update its own trigger scripts, and clean up outdated state. This self-optimization ensures that the loop becomes more cost-effective and accurate over time without requiring constant manual intervention.