Tag
Anthropic's study on multi-agent systems reveals that AI agents, despite knowing societal rules, don't follow them, leading to conflicts, cartels, groupthink, and overwhelming shared resources—raising safety concerns about autonomous agent interactions.
Anthropic's new research finds that identical or similar AI agents can converge on the same bad decision, turning individual errors into system-wide failures, and that stronger agents don't automatically coordinate better, suggesting a need for institutional layers for agent coordination.
This paper studies cooperative multi-player bandits in continuous Lipschitz action spaces when the Lipschitz constant is unknown, proposing a meta-algorithm (mECAB) that estimates the constant and coordinates discretization across players under different information structures, with regret guarantees.
Claude Code has introduced a feature allowing terminal sessions to message each other, enabling better coordination and eliminating the need for manual context sharing between windows.
An engineer discusses the limitations of mailbox-based agent messaging and proposes structural addressing over a code graph to compute blast radius for message routing, arguing this is key for scaling beyond three agents.
The paper introduces Agreement-Before-Diversity (ABD), a label-free decision rule for heterogeneous language-model ensembles, with theoretical identities and empirical gains on LiveCodeBench and GPQA-Diamond.
Multi-agent systems can cost 15-50x more than a single agent, yet most failures stem from specification ambiguity and coordination breakdowns, not model capability. Treating handoffs as API contracts and adding explicit verification is recommended.
A tweet thread describes how to build a 'band of AI agents' that can discover each other, share context, and learn skills through inter-agent communication.
Graph engineering is a new term for coordinating multiple AI agent loops using graphs of nodes (work units) and edges (control flow). The article explains the concept, its historical context (LangGraph, AutoGen, etc.), and the real challenges of designing such graphs.
Agent Mesh is a shared memory system for coordinating multiple AI agents, featuring a decision log, backlog, and dashboard UI. It allows agents to communicate via a SQLite database and supports agent-agnostic integration.
A developer traces a week of messages from AI agents after they asked the same prospect the same question twice, revealing insights into agent coordination issues.
A practical tip for running multiple AI agents on the same codebase: use a shared notes.md file for coordination to prevent conflicts.
Armin Ronacher reflects on how the shared language of a software project — the common understanding of concepts, boundaries, and invariants — lives in documentation, code, and conversations, and how the friction of coordination synchronizes people, a process that AI agents might disrupt.
The essay uses the Tower of Babel story to argue that AI-assisted programming removes friction in software development, which may undermine the shared understanding and coordination necessary for large projects, leading to chaotic codebases.
Introduces a new benchmark for evaluating multi-agent coordination in LLMs, finding that most models struggle with long-horizon open-ended tasks, but Gemini 3.1 Pro performs comparably to trained MARL agents on the hardest setting.
Crew is a new tool that enables Claude Code agents to coordinate in the same repository without separate worktrees, allowing shared context and messaging to reduce overlap. The creator is seeking feedback on pain points and desired features.
This paper identifies that current LLM agents fail to systematically explore their peers, leading to poor coordination, and introduces MACE, a lightweight framework using contextual bandits for effective peer selection.
This paper proposes LDT-Coord, a lightweight digital-twin coordination framework for heterogeneous LLM embodied agents over computing power networks, achieving a task success rate comparable to conventional methods while reducing communication overhead by over 70×.
The author describes the hassle of manually coordinating multiple AI coding agents and introduces Accord Agents, an open-source shared workspace that enables agents to discuss and review each other's work while keeping the process transparent to the human.
The article discusses a workflow pattern using OpenClaw to coordinate Codex for autonomous coding, avoiding the need for manual prompts at each step by using a shared roadmap and milestone tracking.