I was building loops wrong...
The video details the architecture and implementation of 'loops'—autonomous AI systems designed for continuous development and operations. These loops move beyond simple agent prompting by establishing a structured system where an agent can independently decide on work, execute tasks, verify results, and self-improve over time. The core structure involves defining a Loop Contract (Goal, Boundaries, SOP), maintaining State/Logs, utilizing specific Trigger mechanisms, and employing multi-role agents with mandatory verification steps.
Key takeaways
-
The Anatomy of an AI Loop
3:30
Every internal loop is structured around a markdown file containing the 'Loop Contract' (Goal, Boundaries, SOP), 'State' (current hypothesis/backlogs), and 'Logs' (append-only record). This serves as the living documentation for the system.
-
Advanced Trigger Mechanisms
6:30
Beyond standard continuous (`while` loop) or cron job triggers, effective loops often use 'Event-based' (reactive to external events like new emails/incidents) or 'Combo/Workflow' triggers. The latter is highly efficient as it programmatically checks data sources (e.g., Intercom updates) before waking the agent, preventing unnecessary runs.
-
The Evolve Loop Concept
10:30
Loops are designed to improve themselves. An 'Evolve Loop' is a dedicated session where the AI analyzes its own past run state, logs, and configuration to suggest improvements—such as optimizing triggers or refining the SOP—making the system self-optimizing.