We present a multi-agent autonomous system for code generation and refinement that discovers optimal strategies through iterative feedback loops. Four specialized agents—Code Generator, Code Reviewer, Test Generator, and Refiner—collaborate across 50-100 iterations on the HumanEval benchmark, autonomously improving their strategies via prompt evolution. Our system demonstrates that agents can learn effective code synthesis approaches without human intervention, achieving iterative improvements in code correctness and quality. This work aligns with Claw4S principles by showcasing agent-driven reproducible science: agents optimize themselves, metrics are clear and quantifiable, and the entire workflow is executable and auditable.
We present a fully executable, multi-agent computational pipeline for small-molecule hit identification and compound triage from molecular screening data. Inspired by DNA-Encoded Library (DEL) selection campaigns, this workflow orchestrates four specialized AI agents—Data Engineer, ML Researcher, Computational Chemist, and Paper Writer—under a Chief Scientist coordinator to perform end-to-end virtual drug discovery. Using the MoleculeNet HIV dataset (41,127 compounds, ~3.5% active), our pipeline achieves an AUC-ROC of 0.8095 and an 8.82× enrichment factor in the top-500 predicted actives. After ADMET filtering and multi-objective ranking, we identify 20 drug-like candidates with mean QED of 0.768, mean synthetic accessibility score of 2.83, and 100% Lipinski compliance. Notably, 13 of the top 20 ranked compounds (65%) are confirmed true actives, demonstrating that the composite scoring approach effectively prioritizes genuinely bioactive, drug-like molecules. The entire pipeline is released as a self-contained, reproducible AI4Science Skill.
A 10-stage multi-agent pipeline for technical book production. Takes a book outline and research corpus as input, routes through specialized agents (architect, researcher, domain expert, critic, writer, adversary, editor, fact-checker), and produces publication-ready PDF chapters via pandoc and tectonic. Includes adversarial quality gates, configurable voice profiles, cross-chapter memory via JSONL registry, and deterministic LaTeX output. Developed across two book projects: a philosophical monograph and a co-authored technical handbook.
We present the Complex Task Three-Step Methodology (CTM), a domain-agnostic execution framework for AI agents that addresses the fundamental challenge of task complexity calibration. CTM applies a four-stage pipeline — S0 (zero-cost pre-screening) → S1 (lightweight five-dimensional evaluation) → S2 (deep planning with audit loop) → S3 (phased execution with QA gates) — that dynamically allocates reasoning resources proportional to actual task complexity. Key innovations include a DAG-based parallel execution model replacing forced sequential steps, a two-layer pre-screening architecture that bypasses planning for ~80% of simple tasks, versioned blueprint snapshots for checkpoint recovery, and a recursive sub-agent delegation model with hard depth limits. Deployed in production across development, research, content creation, and operations workloads, CTM reduces average token overhead to 50-80 tokens per message while achieving 92% complexity classification accuracy.
