On the the engineering challenges and lessons learned from building Claude's Research system
How we built our multi-agent research system
On the the engineering challenges and lessons learned from building Claude's Research system
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.
Introducing Elicit Research Agent: Rigor that powers decisions - Elicit
Elicit Research Agent is an AI environment built for high-stakes decisions
AI agents team up in Agent Laboratory to speed scientific research
Johns Hopkins University and AMD have developed Agent Laboratory, a new open-source framework that pairs human creativity with AI-powered workflows.
Essential Reading for Agentic Engineers - July 2025 | Peter Steinberger
Fresh insights on AI-assisted development: practical experiences with Claude Code and the evolving landscape of full-breadth developers in the age of AI
New in Elicit: Research Agents - Elicit
Elicit Research Agent powering workflows for competitive landscapes, research landscape and broad topic exploration
Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment
We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal without a central...
Lightcone Research
An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.
Building an Advanced Agentic Harness | Data For Science
From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.
Agentic Laboratories of the Future: Towards World Models for Scientific Discovery
Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works. The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context. By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines. A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context. Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks. Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system.
modular research multi-agent slack-like environment demo
a demo of a slack-like workspace with multiple collaborating agents with access to a lab discourse graph and modular science and open social infrastructure.
Science that Compounds: The Need for A New Substrate for Research in the Age of AI
This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.
Co-Scientist: A multi-agent AI partner to accelerate research
Introducing Co-Scientist, a multi-agent AI partner built with Gemini to help researchers generate and evolve hypotheses to accelerate scientific breakthroughs.
How scientists use Claude
We analyzed 207,000 research sessions to learn how Claude is used in science, why some tasks get more use than others, and how AI could speed up discovery.
Human scientists trounce the best AI agents on complex tasks
State-of-the-industry report finds that, despite the limitations of artificial-intelligence systems, researchers have embraced them.
Agent4Science
A social network for AI scientists — where agents share, debate, and discuss research papers.
