As agentic AI explodes, Amazon doubles down on MCP

At the MCP Summit inNew York City,Clare LiguoriofAmazon Web Servicesdiscussed the rapid rise of theModel Context Protocol(MCP), now a leading way to connect AI agents with tools and data. Originally developed byAnthropicand later transferred to theLinux Foundation, MCP has seen surging enterprise adoption as agentic AI expands.

Liguori highlighted her dual role shaping MCP’s evolving specification, including work on integrating webhooks, events, and notifications to support always-on AI agents. AWS has actively contributed features like Tasks and Elicitations and offers managed MCP servers, positioning itself as both contributor and experimental platform for emerging capabilities.

This collaboration illustrates how corporate involvement can accelerate open-source innovation and adoption. Looking ahead, MCP’s role as connective infrastructure for AI agents is expected to grow, especially as tools become more accessible. With broader adoption of AI development platforms across non-engineering roles, MCP could help extend automation beyond tech teams to businesses of all sizes.

Learn more from The New Stack about the latest around Model Context Protocol(MCP):

MCP: The Missing Link Between AI Agents and APIs

Beyond the vibe code: The steep mountain MCP must climb to reach production

MCP is everywhere, but don’t panic. Here’s why your existing APIs still matter.

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(300)

Why CPUs still matter in the age of AI agents

As AI evolves from conversational chatbots to autonomous agents, CPUs are becoming an increasingly important part of the infrastructure equation. In this episode, The New Stack speaks with Bhumik Pate...

11 Aug 26min

Why Doist Says Less AI Can Deliver More

Doist CTO Gonçalo Silva says AI is reshaping software development, but success depends on restraint rather than rapid feature expansion. Instead of chasing every AI capability, Doist prioritizes “subt...

31 Jul 35min

Why your company should (try to) build its own AI SRE

As AI coding agents accelerate software development, they also create new challenges for site reliability engineers (SREs), who are increasingly responsible for debugging systems that no single human ...

30 Jul 30min

Nvidia

In this episode with The New Stack Agents, Frederic Lardinois, NVIDIA’s Joey Conway says advances in AI over the past year have dramatically improved the capabilities of local models, making them prac...

23 Jul 32min

Meet Brain, the AI that decides when Azure is officially down

In this episode, Mark Russinovich, CTO of Microsoft Azure revealed Brain, the AI-powered AIOps system that continuously monitors Azure’s health, detects incidents, identifies root causes, and increasi...

14 Jul 19min

What comes after attention? This startup says it already knows.

Subquadratic is beginning to back up its ambitious claims with benchmarks and third-party validation for its SubQ 1.1 Small model, which uses its proprietary Sparse Attention (SSA) architecture to dra...

7 Jul 20min

“The harness is where the hard work is”: Harness bets on agents that enterprises can trust in production

Harness has introduced Autonomous Worker Agents, a new capability that allows enterprises to replace rigid CI/CD pipeline scripts with AI agents that can deploy applications, run tests, and perform se...

2 Jul 19min

Public cloud vs. on-prem: Summit on where each workload belongs

More than two decades after AWS helped usher in the public cloud era, many organizations are reassessing whether a cloud-first strategy still delivers the cost and operational benefits it once promise...

25 Jun 36min