ralph-autonomous-loops

Autonomous OpenCode loops use a two-tier orchestrator and sub-agent architecture that persists context and self-corrects across iterations. This pattern enables AI agents to tackle long-running development tasks with structured verification gates after each cycle.

Autonomous AI development loops are persistent, self-correcting cycles used for complex software engineering tasks like building OAuth flows or API integrations. You need them when tasks require long-running, iterative improvement with measurable progress and structured verification.

You maintain context persistence across AI agent iterations by using checkpointing within the autonomous loop pattern. This prevents information loss between cycles, allowing the orchestrator to distribute work to sub-agents while maintaining fresh contexts.

Yes, you need the OpenCode Ralph framework to run these self-correcting autonomous loops. It provides the required two-tier agent setup, orchestrator distribution, and checkpointing infrastructure to drive safe, measurable progress.

You add safety limits to autonomous AI agent loops by defining explicit success criteria, maximum iteration counts, and cost controls before starting the cycle. These constraints ensure safe, measurable progress and prevent runaway processes during complex tasks.

You should avoid using autonomous loops for simple, well-defined software engineering tasks that do not require iterative improvement or long-running execution. Without clearly defined success criteria and verification needs, the two-tier agent overhead provides no measurable benefit.