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Agentic Experimentation Platform

3 posts · Jun 2026 – Sep 2026

What this trail covers

The platform now supports agentic experimentation loops, enabling AI agents to autonomously hypothesize, experiment, evaluate, and iterate on tasks. This includes structured experiment tracking via an API, leaderboards for performance comparison, idea branching for exploring different hypotheses, and history logging for maintaining context. The system is designed to leverage otherwise idle compute resources for continuous research and development. This post introduces the_lab.api, an API-first experiment tracking system that allows agents to manage experiments, compare results, and branch ideas programmatically, enabling autonomous research cycles. It also highlights the use of spare GPU capacity for these experiments, demonstrating cost-effective utilization.

All Lambda trails
  1. OpenResearcher: a reproducible and scalable pipeline for training deep research agentsIntroduces OpenResearcher, a pipeline for training deep research agents. It addresses the data bottleneck by building a reproducible offline research environment over 15 million documents, using GPT-OSS-120B to generate over 97,000 long-horizon research trajectories, and distilling these into a smaller Nemotron-3-Nano-30B-A3B model. The pipeline involves building the environment, generating trajectories with tools like search, open, and find, and then distilling the knowledge into a smaller model. Demonstrates significant performance gains on benchmarks like BrowseComp-Plus, outperforming larger proprietary models.
  2. ReviewGrounder: grounding AI-assisted peer review in evidenceIntroduces ReviewGrounder, an AI-assisted peer review system that divides the review task into specialized stages: a Review Drafter, grounding agents (Literature Searcher, Insight Miner, Result Analyzer), and a Review Aggregator. ReviewGrounder significantly outperforms existing LLM reviewers in evidence-grounded quality and demonstrates the compute-intensive nature of AI research agents, highlighting Lambda's role in providing the necessary cloud infrastructure.
  3. What happens when Claude Code gets an experiment trackerThis post introduces the_lab.api, an API for experiment tracking designed for AI agents. It details how this API enables autonomous experimentation by providing capabilities for starting/queuing/canceling experiments, inspecting logs, querying leaderboards for best-performing runs, branching ideas as distinct git branches, and maintaining historical context. The post showcases a live demo where Claude Code used the_lab.api to teach Gemma 4 to play Tetris, iterating through 468 experiments across 90 ideas with zero human intervention. It emphasizes the importance of structured tracking for agents to avoid re-running experiments or losing context, and highlights the cost-effectiveness of using idle GPU resources for these agentic research loops.