I was building loops wrong...
Summary
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
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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.
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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.
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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.
Technical details
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Loop Contract Components
240s
The contract defines three critical elements: 1) The Goal (what success looks like), 2) Boundaries (what the agent can do autonomously vs. what requires human escalation), and 3) Standard Operating Procedures (SOPs).
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Agent Architecture for High-Stakes Tasks
490s
For complex tasks, the agent structure is broken down into roles: an Orchestrator (planning/research), multiple Sponsor Agents (isolated work trees for parallel execution), Executors, and a Verifier. The Verifier is crucial for quality assurance, testing results, and attaching evidence to PRs.
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Verification Systems
540s
To ensure high-quality output, the process requires verification. Tools like Playwright or CRI are used to allow agents to test work and record video/image evidence, while remote sandboxes (e.g., Crabbox) prevent local resource limitations.
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Example: React Doctor Loop
280s
A practical example is the 'react-doctor' loop, which runs an open-source CRI tool on the codebase daily. The loop identifies critical issues and automatically attempts to fix the most severe one, following defined boundaries and verification processes.
Mentioned resources
Channel & topics
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