DEVOURED
Self-Evolving Autoresearch Workflow Loops
The 'Evo' project transitioned its autoresearch logic from in-context prompt memory into deterministic, scoped JavaScript workflows via Claude Code.
What: Developer Alok Bishoyi ported an autoresearch orchestrator to Anthropic's dynamic workflows, using code to manage loop phases, fan-out, and gates while leaving judgment to the LLM.
Why it matters: Hard-coding workflow logic into deterministic scripts instead of relying on LLM 'memory' improves reliability and reduces hallucinated deviations in complex, long-running agent loops.
Deep dive
- Moves autoresearch loop coordination out of the model's context window.
- Leverages deterministic JavaScript to manage phase transitions, stopping rules, and CLI interactions.
- Uses the LLM solely for decision-making and judgment within a pre-defined structural framework.
- Enables cleaner state management for multi-step research agent tasks.
Decoder
- Autoresearch: AI agent workflows tasked with recursive searching, data gathering, and analysis.
- Claude Code: Anthropic’s developer tool for autonomous code interaction.
Original article
Self-Evolving Autoresearch Workflow Loops
In this article we explain how we ported evo's autoresesarch loop to use workflows and then also made it dynamic. On June 2 Anthropic shipped dynamic workflows in Claude Code: Claude writes a small...
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