Building AI Agents
2025-02-02 — 2025-06-07
Placeholder while I think about the practicalities and theory of AI agents.
See also Multi agent systems.
1 Factored cognition
Field of study? Or one company’s marketing term?
In this project, we explore whether we can solve difficult problems by composing small and mostly context-free contributions from individual agents who don’t know the big picture.
2 Incoming
Announcing the Agent2Agent Protocol (A2A) - Google Developers Blog
Introducing smolagents: simple agents that write actions in code.
Agent Laboratory: Using LLM Agents as Research Assistants
Agent Laboratory takes input from a human-produced research idea and outputs a research report and code repository. Agent Laboratory is meant to assist you as the human researcher in implementing your research ideas. You are the pilot. Agent Laboratory provides a structured framework that adapts to your computational resources, whether you’re running it on a MacBook or on a GPU cluster. Agent Laboratory consists of specialised agents driven by large language models to support you through the entire research workflow—from conducting literature reviews and formulating plans to executing experiments and writing comprehensive reports. This system is not designed to replace your creativity but to complement it, enabling you to focus on ideation and critical thinking while automating repetitive and time-intensive tasks like coding and documentation. By accommodating various levels of computational resources and human involvement, Agent Laboratory aims to accelerate scientific discovery and optimise your research productivity.
CrewAI hosts an AI agent platform and also an open-source release:
I’ve seen some very impressive things done with this by Michael Kuiper.
Swarm AI
J-Rosser-UK/AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds (Rosser and Foerster 2025)
Scaffolding Large Language Models (LLMs) into multi-agent scaffolds often improves performance on complex tasks, but the safety impact of such scaffolds has not been as thoroughly explored. In this paper, we introduce AGENTBREEDER a framework for multi-objective evolutionary search over scaffolds. Our REDAGENTBREEDER evolves scaffolds towards jailbreaking the base LLM while achieving high task success, while BLUEAGENTBREEDER instead aims to combine safety with task reward. We evaluate the scaffolds discovered by the different instances of AGENTBREEDER and popular baselines using widely recognized reasoning, mathematics, and safety benchmarks. Our work highlights and mitigates the safety risks due to multi-agent scaffolding.