Anthropic expanded dynamic workflows in Claude Managed Agents into a public beta. > Users can now configure their agents for multiagent orchestration, letting Claude plan and operate a fleet of agents to achieve the goal. Agentic loops invoking multiagent orchestration workflows will be insane. A new SWE norm 👀

Sol 5.6 Ultra gives surprisingly similar outputs every time, to the point where I've been investigating whether there's some illicit memory use thing going on... Don't think it's secret memory, just a bit RL fried + multiagent stuff collapses the randomness a bit (?)

In 2027, will most internet traffic be agents talking to agents? If yes — who wins: 1⃣the best models 2⃣the best infrastructure 3⃣the best governance? SVP Chain purpose-built for the agentic economy are positioning to capture all three. #AIAgents# #AgenticAI# #AgenticEconomy# #SVPChain# #MultiAgent# #FutureOfInternet#

TL;DR: A multi-agent "prove-verify loop" solved five genuinely open math problems, spanning auction theory to online learning, with every result independently verified by domain experts. Title: Cogentic: Multi-Agent Orchestration for Automated Proof Discovery URL: Points 🧠 An orchestrator assigns multiple "provers" to different proof directions, mimicking a research group with adversarial verifiers that assume every step is wrong until justified 📚 A persistent "ledger" accumulates verified intermediate lemmas, so progress survives across rounds instead of being lost between attempts 🎯 Solved 5 open problems across online learning, auction theory, and mechanism design 📊 Improved the simple-vs-optimal revenue approximation factor from 5.2 to 3.52, and hit the optimal 1.5 price of anarchy for 2-bidder autobidding auctions 💰 Built on Gemini, with a modest inference budget — around O(100) Gemini calls for most problems ✅ All 5 results passed independent verification by domain experts and were developed into companion papers It feels genuinely significant that a properly orchestrated language model can tackle real open research problems, not just textbook exercises. #MathResearch# #MultiAgent#

TL;DR: To fix the scalability problem of single-agent harnesses, this paper introduces Raven, an open-source multi-agent ecosystem that automatically builds and evolves its own harnesses. Title: Raven: The Harness of Harnesses for Composable Agentic Intelligence URL: Points 🧩 Treats each model-harness pair as a unit of composition; a Host Agent decomposes tasks and assigns them to specialist agents as a DAG 🧠 EverOS (memory) and Skill Forge (skill retrieval/reuse) let the system carry past experience across tasks 🔬 Proves a "composition soundness" theorem where error rates simply add up across operations, and shows with an XOR example that composed agents can exceed what any single agent can do 📊 Ranks #1# on all four metrics of the new MAOB benchmark, beating the strongest baseline by +10.4–10.5 points on Exact Match 🛠️ Specialist agents — Raven-Research, -Code, -Design, -Oncall — all consistently beat their baselines too 🔁 A diagnosis-driven harness self-evolution mechanism improves performance across domains even with a frozen model backbone It's compelling to see theory and empirical results both back up why multi-agent composition actually works. #MultiAgent# #AgentHarness#

Are your subagents re-reading the same files the supervisor already read? LangChain just shipped a feature to fix exactly that waste. Title: Organizing Context in a Multi-Agent Harness URL: 📝 Overview The deepagents framework now supports "forked subagents" — you can choose whether a subagent inherits the supervisor's full conversation history or starts from a blank context. ❗ Problem it solves Fully isolated subagents had to redo investigation and context-gathering the supervisor already completed, wasting both tokens and latency. ⚙️ Methodology Two modes are offered: Isolated Mode (fresh context) and Fork Mode (inherits the supervisor's full state). Fork Mode stays cost-efficient thanks to prompt caching. 🔧 Use cases ・Worker agents: use fork to continue fixing work the supervisor started ・Reviewer agents: use isolated for an unbiased evaluation ・Researcher and memory agents also pick modes based on their role 📊 Results No hard benchmark numbers are given, but the post reports reduced duplicate context-gathering and tool calls. I think this is a genuinely new lever for designing subagent roles well. #MultiAgent# #LangChain#

Packed house last night for our Omnigent & Multi-Agent Coding Meetup at the @databricks NYC office! 🗽 Highlights from the night: • Omnigent Overview • Training Custom Agents for Enterprise Tasks • Panel Q&A with @SpaceXAI, @Modal, and @Databricks A big thank you to our speakers and everyone who came out! 👏 🔗 Stay in the loop on future events: #Omnigent# #OpenSource# #NYC# #AI# #MultiAgent#