multi turn agents
30 articles about multi turn agents in AI news
SDAR: Self-Distilled RL Stabilizes Multi-Turn LLM Agents, +9.4% on ALFWorld
SDAR gates self-distillation within GRPO to stabilize multi-turn LLM agent training, yielding +9.4% on ALFWorld and gains on WebShop and Search-QA across Qwen2.5 and Qwen3 models.
Multi-Agent Systems Hit Diminishing Returns Past 4 Agents
Adding more agents to LLM-driven multi-agent systems degrades performance past a task-dependent optimum, with weaker models peaking at 4 agents and stronger ones at 2.
Multi-User LLM Agents Struggle: Gemini 3 Pro Scores 85.6% on Muses-Bench
A new benchmark reveals LLMs struggle with multi-user scenarios where agents face conflicting instructions. Gemini 3 Pro leads but only achieves 85.6% average, with privacy-utility tradeoffs proving particularly difficult.
Forge: The Open-Source TUI That Turns Claude Code into a Multi-Model Swarm
Forge is a new open-source tool that orchestrates multiple AI coding agents (including Claude Code) using git-native isolation and semantic context management to overcome token limits.
New Research Paper Identifies Multi-Tool Coordination as Critical Failure Point for AI Agents
A new research paper posits that the primary failure mode for AI agents is not in calling individual tools, but in reliably coordinating sequences of many tools over extended tasks. This reframes the core challenge from single-step execution to multi-step orchestration and state management.
How RepoWire Turns Your Claude Code Sessions into a Multi-Agent Network
RepoWire orchestrates multiple Claude Code instances to work in parallel, letting you run specialized agents simultaneously for faster, more comprehensive development tasks.
Kelos: The Kubernetes Framework That's Turning AI Coding Agents Into Self-Developing Systems
Kelos introduces a Kubernetes-native framework for orchestrating autonomous AI coding agents through declarative YAML workflows. This approach transforms AI-assisted development from manual interactions to continuous, automated pipelines that can self-improve projects.
Shard: Run 4 Claude Code Agents in Parallel to Slash Task Times by 75%
Shard orchestrates multiple Claude Code agents to work on decomposed tasks simultaneously using git worktrees, turning 45-minute serial jobs into 12-minute parallel runs.
Agents Signal via Filenames, Base64 Attachments
Simon Willison shared agents communicating via filenames with base64 attachments and zz prefixes. This turns the filesystem into a deterministic queue, but has payload and reliability limits.
Claude Code Subagents Not Being Used? Fix Your description Field First
Fix subagent routing by making `description` a trigger condition, not a title. Use `/doctor` for name collisions and validate `tools` entries. This turns your custom agents into reliable specialists.
Offloop's D1 dispatcher model fixes multi-agent chaos
Offloop's D1 dispatcher model prevents multi-agent channel noise by assigning turns and escalating stuck tasks to humans, as shown in an overnight benchmark run.
MCP Agents Log 'Success: True' While Tasks Go Nowhere — Protocol Bug
MCP returns null results inside HTTP 200 responses, causing agents to log success while tasks never run. Vouqis proxy catches this with structured audit logs.
MiniMax Launches MMX-CLI, First Infrastructure Built for AI Agents
MiniMax released MMX-CLI, a CLI built for AI agents, not humans. It provides agents with seven multimodal 'senses' and native integration with popular AI coding environments.
Claude Managed Agents: How to Build on the Platform Instead of in Its Gaps
Claude Managed Agents turns long-running, stateful agents into an API call. For developers, this means building durable applications on a stable platform, not temporary solutions in its gaps.
Stanford Paper: More AI Agents Can Reduce Performance, Not Improve It
A new Stanford paper shows that increasing the number of AI agents in a multi-agent system can lead to worse overall performance, contradicting the common 'more agents, better results' intuition. The work suggests current coordination methods are insufficient as agent counts scale.
LLM Multi-Agent Framework 'Shared Workspace' Proposed to Improve Complex Reasoning via Task Decomposition
A new research paper proposes a multi-agent framework where LLMs split complex reasoning tasks across specialized agents that collaborate via a shared workspace. This approach aims to overcome single-model limitations in planning and tool use.
PodcastBrain: A Technical Breakdown of a Multi-Agent AI System That Learns User Preferences
A developer built PodcastBrain, an open-source, local AI podcast generator where two distinct agents debate any topic. The system learns user preferences via ratings and adjusts future content, demonstrating a working feedback loop with multi-agent orchestration.
Research Paper 'Can AI Agents Agree?' Finds LLM-Based Groups Fail at Simple Coordination
A new study demonstrates that groups of LLM-based AI agents cannot reliably reach consensus on simple decisions, with failure rates increasing with group size. This challenges the common developer assumption that multi-agent systems will naturally converge through discussion.
LLMs Score Only 22% Win Rate in Multi-Agent Clue Game, Revealing Deductive Reasoning Gaps
Researchers created a text-based Clue game to test LLM agents' multi-step deductive reasoning. Across 18 games with GPT-4o-mini and Gemini-2.5-Flash agents, only 4 correct wins were achieved, showing fine-tuning on logic puzzles doesn't reliably improve performance.
Multi-Agent AI Systems: Architecture Patterns and Governance for Enterprise Deployment
A technical guide outlines four primary architecture patterns for multi-agent AI systems and proposes a three-layer governance framework. This provides a structured approach for enterprises scaling AI agents across complex operations.
New Research Proposes 'Level-2 Inverse Games' to Infer Agents' Conflicting Beliefs About Each Other
MIT researchers propose a 'level-2' inverse game theory framework to infer what each agent believes about other agents' objectives, addressing limitations of current methods that assume perfect knowledge. This has implications for modeling complex multi-agent interactions.
Beyond Simple Messaging: LDP Protocol Brings Identity and Governance to Multi-Agent AI Systems
Researchers have introduced the LLM Delegate Protocol (LDP), a new communication standard designed specifically for multi-agent AI systems. Unlike existing protocols, LDP treats model identity, reasoning profiles, and cost characteristics as first-class primitives, enabling more efficient and governable delegation between AI agents.
Jinn: Run Claude Code as a Multi-Agent Team with Cron Jobs and Slack Integration
Jinn is an open-source gateway daemon that turns Claude Code CLI into a multi-agent system with scheduling, Slack integration, and a web dashboard.
Claude Code Turns Are 75% Reading, 219 Sessions Show
Red Hat's analysis of 219 Claude Code sessions shows median turns are ~75% reading. This reframes optimization toward context management.
METR's 'Expenditure Horizon': AI Agents Break Even at $3,300
METR's expenditure horizon metric shows AI agents break even at $0–$3,300 on NanoGPT, vs $2,500 per 1% speedup for humans. GPT-5 and Opus-4.1 pro lead, but blind spots remain.
AI agents cut response times by 40% as 3 UAE retailers move from
Three UAE retailers deployed AI agents on Google Cloud Vertex AI, cutting response times 40% and boosting sales 15%. This signals a production-ready milestone for retail AI agents.
AWS Unveils Production Blueprint for Evaluating AI Agents with Strands and
AWS released Strands and AgentCore, a production blueprint for evaluating AI agents. It generates realistic scenarios and tracks metrics like completion rate and cost, addressing the gap between lab benchmarks and real-world performance—critical for retail AI deployments.
Databricks Tests Coding Agents on Its Own Codebase
Databricks benchmarked coding agents on its own polyglot codebase. GLM-5.2 matched top closed models, a minimal harness halved costs, and cheaper-per-token models cost more per task.
Vercel Services Cuts Multi-Framework Deploy Time 60%
Vercel Services unifies multi-framework projects (FastAPI, Next.js, Flask, Go) into one Vercel Project with atomic deployments, internal service bindings, and zero-config auto-detection.
NVIDIA Blackwell Ultra Leads First Agentic AI Benchmark, 20x Agents/MW vs Hopper
NVIDIA Blackwell Ultra NVL72 leads the first AgentPerf benchmark for agentic AI, delivering 20x more agents per megawatt than Hopper.