The Loop Is the Product — Roland Gavrilescu, Introspection
This post introduces a blueprint for autoresearch in 2026, centered on three core ideas: 1) 'The loop is the product,' emphasizing the quality of signals and verifiers within agentic loops and how their outputs feed subsequent loops. 2) 'System distillation is the moat,' proposing the capture of lessons learned from agent loops (including evals, judges, skills, and human judgment) into portable, versioned 'agent recipes' that are owned by the creator, independent of models or providers. Introspection is releasing an early version of these recipes called Pi recipes. 3) 'Valued work per watt' as the primary optimization score, demonstrated through a talent-sourcing agent example that illustrates spotting patterns in traces, calibrating judges with human-in-the-loop feedback, and A/B testing user taste in production before promoting changes.
More from the Agentic Research and Discovery Loops trail
- Build a Research Agent with Claude Managed Agents (Part 2 of 4) — Anthropic
- Scheduled Agents, Dreaming and Q&A: Claude Managed Agents (Part 3 of 4) — Anthropic
- Hill-Climbing Skills: Improve Agents Without Changing the Model — Shubhankar Srivastava, Browserbase
- Parameter Golf with AutoResearch — Vayum Arora, Zhengyao Jiang, Dixing Xu & Dhruv Srikanth, Weco AI
- From Zero to Leaderboard: Agent Evaluation — Wolfram Ravenwolf, Weights & Biases