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#agents

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AI For Business

Lan Guan, Chief AI Officer at Accenture, shared that 10–15% of clients currently use multi-agent systems (including BWM, Unilever, and ESPN) - and that number is expected to *double* in two years.

What should your readiness strategy include?
✅ Start with standalone agents
✅ Invest in flexible data sources, and data governance models
✅ Explore how agents can interact with your other business systems.

Read more here (gift link): wsj.com/articles/ai-agents-are

#AI#GenAI#GenerativeAI
Public
Richard MacManus

I realize there's a lot of AI-skepticism on Mastodon, but I think it's important to track how AI is being used more widely. So I spoke to GitLab's head of Field CTOs Brian Wald about how enterprises are implementing AI technology. Among other things, we discussed whether IT departments allow #VibeCoding, what types of AI #agents enterprises are building, how the #developer role is shifting to an architect-type role, and GitLab's own AI strategy. thenewstack.io/the-field-cto-v

The New Stack · The Field CTO View: AI, Vibe Coding, and Developer SkillsetsHow is AI actually being implemented in enterprise IT departments? And how are developers adapting? We ask GitLab's head of Field CTOs.
Public
Ecologia Digital

"The next layer of the internet—one built for autonomous #agents collabing across systems. 4 open components:
. Google’s Agent2Agent (A2A): A protocol for agents to discover and communicate.
. Anthropic’s Model Context Protocol (MCP): A standard for tool use and external context.
. Apache Kafka: An event-driven communication fabric for reliable, decoupled coordination.
. Apache Flink: A real-time processing engine to enrich, monitor and act on streams of agent activity."
thenewstack.io/a2a-mcp-kafka-a

The New Stack · A2A, MCP, Kafka and Flink: The New Stack for AI AgentsFour open components — Agent2Agent, Model Context Protocol, Apache Kafka and Apache Flink — provide the infrastructure for collaborative, autonomous AI agents.
Continued thread
Public
Kornel

Energy usage of LLMs used through an individual chat interface doesn't seem worrying to me. It's not worse than energy usage of a high-end PC, and it's limited by how much a human can ask and read.

But deployment of LLMs as "autonomous #agents seems like a floodgate for wasteful large-scale usage. It makes it easy to run lots of them, without time limits. Getting only summarized outputs hides how much effort was spent in the process of getting it.

Public
☮ ♥ ♬ 🧑‍💻

“To some American observers, fixated on the idea that #China might be stealing a march on the West, the fact that Manus is Chinese is especially threatening. But Manus’s success is nowhere near the scale of that of #DeepSeek, a Chinese firm that stunned the world with its cheap ai model.

Any company, be it American, Chinese or otherwise, could produce a similar #agent, provided it used the right off-the-shelf components and had a large enough appetite for risk.

👉 Fortunately, there is little sign yet that Manus has done anything dangerous.

But safety can no longer be just a matter of big labs conducting large-scale testing before release.

Instead, regulators and companies will need to monitor what is already used in the wild, rapidly respond to any harms they spot and, if necessary, pull misbehaving systems out of action entirely. Whether you like it or not, Manus shows that the future of ai development will play out in the open.”

#AI / #software / #agents <archive.md/9UvQm> / <economist.com/leaders/2025/03/> #paywall

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