All posts tagged: orchestration

Anthropic wants to own your agent’s memory, evals, and orchestration — and that should make enterprises nervous

Anthropic wants to own your agent’s memory, evals, and orchestration — and that should make enterprises nervous

Published by skeptic

Just a few weeks after announcing Claude Managed Agents, Anthropic has updated the platform with three new capabilities that collapse infrastructure layers like memory, evaluation, and multi-agent orchestration, into a single runtime. This move could threaten the standalone tools that many enterprises cobble together. The new capabilities — ‘Dreaming,’ ‘Outcomes,’ and ‘Multi-Agent Orchestration’ — aim to make agents inside Claude Managed Agents “more capable at handling complex tasks with minimal steering,” Anthropic said in a press release.   Dreaming deals with memory, where agents “reflect” on their many sessions and curate memories so they learns and surface unknown patterns. Outcomes allows teams to define and set specific rubrics to measure an agent’s success, while Multi-Agent Orchestration breaks jobs down so a lead agent can delegate to other agents. Claude Managed Agents ideally provides enterprises with a simpler path to deploy agents and embeds orchestration logic in the model layer. It’s an end-to-end platform to manage state, execution graphs, and routing. With the addition of Dreaming, Outcomes and Multi-agent Orchestration, Claude Managed Agents expands capabilities even further …

AWS Quick’s personal knowledge graph is making orchestration decisions most control planes can’t see

AWS Quick’s personal knowledge graph is making orchestration decisions most control planes can’t see

Published by skeptic

Enterprise AI teams running centralized orchestration stacks now have a new variable to account for: AWS Quick, which expanded this week to a desktop-native agent that builds a persistent personal knowledge graph and executes actions across local files and SaaS tools — outside the visibility of most control planes. Unlike chat-based copilots that reset with each session, Quick now maintains a continuously updated knowledge graph built from the user’s local files, calendar, email and connected SaaS apps. It uses it to proactively trigger actions without waiting to be asked. AWS launched Quick in October last year as an alternative to AI workflow and productivity platforms coming from Google, OpenAI and Anthropic. It was a way for enterprise employees to access insights from connected applications, an agent builder, deep research, and workflow automation. Now, it’s grown beyond a simple AI assistant and acts more as a proactive workflow agent with a stateful, real-time knowledge graph of the user. It integrates with third-party apps like Google Workspace, Microsoft 365, Zoom, Salesforce and Slack — and now local …

Mistral AI launches Workflows, a Temporal-powered orchestration engine already running millions of daily executions

Mistral AI launches Workflows, a Temporal-powered orchestration engine already running millions of daily executions

Published by skeptic

Mistral AI, the Paris-based artificial intelligence company valued at €11.7 billion ($13.8 billion), today released Workflows in public preview — a production-grade orchestration layer designed to move enterprise AI systems out of proofs of concept and into the business processes that generate revenue. The product, which launches as part of Mistral’s Studio platform, is the company’s clearest articulation yet of a thesis that is quietly reshaping the enterprise AI market: that the bottleneck for organizations adopting AI is no longer the model itself, but the infrastructure required to run it reliably at scale. “What we’re seeing today is that organizations are struggling to go beyond isolated proofs of concept,” Elisa Salamanca, who leads go-to-market for Mistral’s enterprise products, told VentureBeat in an exclusive interview ahead of the launch. “The gap is operational. Workflows is the infrastructure to run AI systems reliably across business-critical processes.” The release arrives at a pivotal moment for both Mistral and the broader AI industry. The dedicated agentic AI market has been valued at approximately $10.9 billion in 2026 and is …

Kimi K2.6 runs agents for days — and exposes the limits of enterprise orchestration

Kimi K2.6 runs agents for days — and exposes the limits of enterprise orchestration

Published by skeptic

Most orchestration frameworks were built for agents that run for seconds or minutes. Now that agents are running for hours — and in some cases days — those frameworks are starting to crack. Several model providers, such as Anthropic with Claude Code and OpenAI with Codex, introduced early support for long-horizon agents through multi-session tasks, subagents and background execution. However, these systems sometimes assume agents are still operating within bounded-time workflows even when they run for extended periods.  Open-source model provider Moonshot AI wants to push beyond that with its new model, Kimi K2.6.  Moonshot says the model is designed for continuous execution, with internal use cases including agents that ran for hours and, in one case, five straight days, handling monitoring and incident response autonomously. But this growing use of this type of agent is exposing a critical gap in orchestration: most orchestration frameworks were not designed for this type of continuous, stateful execution. Open-source models, such as Kimi K2.6, that rely on agent swarms are making the case that their orchestration approach comes …

8 billion tokens a day forced AT&T to rethink AI orchestration — and cut costs by 90%

8 billion tokens a day forced AT&T to rethink AI orchestration — and cut costs by 90%

Published by skeptic

When your average daily token usage is 8 billion a day, you have a massive scale problem. This was the case at AT&T, and chief data officer Andy Markus and his team recognized that it simply wasn’t feasible (or economical) to push everything through large reasoning models. So, when building out an internal Ask AT&T personal assistant, they reconstructed the orchestration layer. The result: A multi-agent stack built on LangChain where large language model “super agents” direct smaller, underlying “worker” agents performing more concise, purpose-driven work. This flexible orchestration layer has dramatically improved latency, speed and response times, Markus told VentureBeat. Most notably, his team has seen up to 90% cost savings. “I believe the future of agentic AI is many, many, many small language models (SLMs),” he said. “We find small language models to be just about as accurate, if not as accurate, as a large language model on a given domain area.” Most recently, Markus and his team used this re-architected stack along with Microsoft Azure to build and deploy Ask AT&T Workflows, …

Shared memory is the missing layer in AI orchestration

Shared memory is the missing layer in AI orchestration

Published by skeptic

The key to successful AI agents within an enterprise? Shared memory and context.  This, according to Asana CPO Arnab Bose, provides detailed history and direct access from the get-go — with guardrail checkpoints and human oversight, of course.  This way, “when you assign a task, you’re not having to go ahead and re-provide all of the context about how your business works,” Bose said at a recent VB event in San Francisco.  AI as an active teammate, rather than a passive add-on Asana launched Asana AI Teammates last year with the philosophy that, just like humans, AI agents should be plugged directly into a team or project to create a collaborative system. To further this mission, the project management company has fully integrated with Anthropic’s Claude.   Users can choose from 12 pre-built agents — for common use cases like IT ticket deflection — or build their own, then assign them to project teams and immediately provide a historical record of what tasks have already been completed and what is still yet to be resolved. Agents …

Bolna nabs .3M from General Catalyst for its India-focused voice orchestration platform

Bolna nabs $6.3M from General Catalyst for its India-focused voice orchestration platform

Published by skeptic

Industry reports and the growth of voice model companies in the Indian market suggest that there is a growing demand for voice AI solutions in the country. Voice is a popular medium for communication among people and businesses in India. That’s why enterprises and startups are eager to use voice AI to be more efficient at customer support, sales, customer acquisition, hiring, and training. But recognizing market demand is one thing — proving businesses will pay is another. Y Combinator rejected the application from Bolna, a voice orchestration startup built by Maitreya Wagh and Prateek Sachan, five times before finally accepting it into the fall 2025 batch, skeptical that the founders could turn interest into revenue. “When we were applying for Y Combinator, the feedback we got was, ‘great to see that you have a product that can create realistic voice agents, but Indian enterprises are not going to pay, and you are not going to make money out of this,’” Wagh told TechCrunch. The startup applied with the same idea for the fall batch …

AI agents can talk — orchestration is what makes them work together

AI agents can talk — orchestration is what makes them work together

Published by skeptic

Rather than asking how AI agents can work for them, a key question in enterprise is now: Are agents playing well together?  This makes orchestration across multi-agent systems and platforms a critical concern — and a key differentiator.  “Agent-to-agent communications is emerging as a really big deal,” G2’s chief innovation officer Tim Sanders told VentureBeat. “Because if you don’t orchestrate it, you get misunderstandings, like people speaking foreign languages to each other. Those misunderstandings reduce the quality of actions and raise the specter of hallucinations, which could be security incidents or data leakage.” Allowing agents to talk and coordinate Orchestration to this point has largely been around data, but that’s quickly turning to action. “Conductor-like solutions” are increasingly bringing together agents, robotic process automation (RPA), and data repositories. Sanders likened the progression to that of answer engine optimization, which initially began with monitoring and now creates bespoke content and code.  “Orchestration platforms coordinate a variety of different agentic solutions to increase the consistency of outcomes,” he said.  Early providers include Salesforce MuleSoft, UiPath Maestro, and …

Orchestral replaces LangChain’s complexity with reproducible, provider-agnostic LLM orchestration

Orchestral replaces LangChain’s complexity with reproducible, provider-agnostic LLM orchestration

Published by skeptic

A new framework from researchers Alexander and Jacob Roman rejects the complexity of current AI tools, offering a synchronous, type-safe alternative designed for reproducibility and cost-conscious science. In the rush to build autonomous AI agents, developers have largely been forced into a binary choice: surrender control to massive, complex ecosystems like LangChain, or lock themselves into single-vendor SDKs from providers like Anthropic or OpenAI. For software engineers, this is an annoyance. For scientists trying to use AI for reproducible research, it is a dealbreaker. Enter Orchestral AI, a new Python framework released on Github this week that attempts to chart a third path. Developed by theoretical physicist Alexander Roman and software engineer Jacob Roman, Orchestral positions itself as the “scientific computing” answer to agent orchestration—prioritizing deterministic execution and debugging clarity over the “magic” of async-heavy alternatives. The ‘anti-framework’ architecture The core philosophy behind Orchestral is an intentional rejection of the complexity that plagues the current market. While frameworks like AutoGPT and LangChain rely heavily on asynchronous event loops—which can make error tracing a nightmare—Orchestral utilizes …

Brex bets on ‘less orchestration’ as it builds an Agent Mesh for autonomous finance

Brex bets on ‘less orchestration’ as it builds an Agent Mesh for autonomous finance

Published by skeptic

Fintech Brex is betting that the future of enterprise AI isn’t better orchestration — it’s less of it. As generative AI agents move from copilots to autonomous systems, Brex CTO James Reggio says traditional agent orchestration frameworks are becoming a constraint rather than an enabler. Instead of relying on a central coordinator or rigid workflows, Brex has built what it calls an “Agent Mesh”: a network of narrow, role-specific agents that communicate in plain language and operate independently — but with full visibility. “Our goal is to use AI to make Brex effectively disappear,” Reggio told VentureBeat. “We’re aiming for total automation.” Brex learned that for its purposes, agents need to work in narrow, specific roles to be more modular, flexible, and auditable.  Reggio said the architectural goal is to enable every manager in an enterprise “to have a single point of contact within Brex that’s handling the totality of their responsibilities, be it spend management, requesting travel, or approving spend limit requests.” The journey from Brex Assistant The financial services industry has long embraced …