venturebeat
Stanford's DeLM cuts multi-agent task costs 50% — without a central orchestrator

One of the assumptions behind today’s AI frameworks is that agents require a “boss” at the center; this orchestrator runs the show, routes requests, and makes sure the whole system doesn’t descend into chaos. That assumption may be wrong, and the cost of carrying it could be measured in inference dollars and coordination latency. A new Stanford framework called a decentralized language model, or DeLM, is built on the premise that agents can coordinate directly, without routing every update through a central controller.DeLM's shared knowledge base serves as a “common communication substrate” so that agents can build upon one another’s verified progress without having to route every interaction through a main agent to “merge, filter, and rebroadcast,” Yuzhen Mao and Az [...]

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venturebeat
Most enterprises can't stop stage-three AI agent threats, VentureBeat survey finds

A rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain br [...]

Match Score: 167.88

venturebeat
Are you paying an AI ‘swarm tax’? Why single agents often beat complex systems

Enterprise teams building multi-agent AI systems may be paying a compute premium for gains that don't hold up under equal-budget conditions. New Stanford University research finds that single-age [...]

Match Score: 163.86

venturebeat
Frontier models are failing one in three production attempts — and getting harder to audit

AI agents are now embedded in real enterprise workflows, and they're still failing roughly one in three attempts on structured benchmarks. That gap between capability and reliability is the defin [...]

Match Score: 138.66

venturebeat
Nvidia's new AI framework trains an 8B model to manage tools like a pro

Researchers at Nvidia and the University of Hong Kong have released Orchestrator, an 8-billion-parameter model that coordinates different tools and large language models (LLMs) to solve complex proble [...]

Match Score: 136.01

venturebeat
Research shows ‘more agents’ isn’t a reliable path to better enterprise AI systems

Researchers at Google and MIT have conducted a comprehensive analysis of agentic systems and the dynamics between the number of agents, coordination structure, model capability, and task properties. W [...]

Match Score: 125.18

venturebeat
Intent-based chaos testing is designed for when AI behaves confidently — and wrongly

Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomali [...]

Match Score: 113.00

venturebeat
Airtable's Superagent maintains full execution visibility to solve multi-agent context problem

Airtable is applying its data-first design philosophy to AI agents with the debut of Superagent on Tuesday. It's a standalone research agent that deploys teams of specialized AI agents working in [...]

Match Score: 112.74

venturebeat
Claude’s next enterprise battle is not models: it’s the agent control plane

New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]

Match Score: 111.08

venturebeat
Anthropic introduces "dreaming," a system that lets AI agents learn from their own mistakes

Anthropic on Tuesday unveiled a suite of updates to its Claude Managed Agents platform at its second annual Code with Claude developer conference in San Francisco, introducing a new capability called [...]

Match Score: 102.81