This is how Google wants AI agents to actually work

Alibaba Group, ScaleOps, and Google are all moving in the same direction, even if it doesn’t look obvious at first. For years, the AI conversation has been dominated by smarter models, better responses, and more impressive demos. Alibaba is building AI that can run business tasks like digital employees, ScaleOps is making sure the infrastructure behind AI is actually used efficiently, and Google is focusing on how multiple agents can operate together reliably. Different angles, same underlying trend: AI is moving from intelligence to execution.

In today’s post:

• Everyone’s building AI agents but few can control them

• The hidden problem behind AI’s biggest boom

• AI that doesn’t just think, it works

SPONSORED BY

88% resolved. 22% stayed loyal. What went wrong?

That's the AI paradox hiding in your CX stack. Tickets close. Customers leave. And most teams don't see it coming because they're measuring the wrong things.

Efficiency metrics look great on paper. Handle time down. Containment rate up. But customer loyalty? That's a different story — and it's one your current dashboards probably aren't telling you.

Gladly's 2026 Customer Expectations Report surveyed thousands of real consumers to find out exactly where AI-powered service breaks trust, and what separates the platforms that drive retention from the ones that quietly erode it.

If you're architecting the CX stack, this is the data you need to build it right. Not just fast. Not just cheap. Built to last.

See the data

What’s Trending Today

RESEARCH

The hardest part of AI isn’t intelligence, it’s coordination

Google just released something subtle but important. A toolkit to actually manage AI agents, not just build them.

Here’s everything you need to know:

• Most people focus on making agents smarter, not more reliable.

• Google’s ADK introduces tools to connect agents with real-world data.

• It adds memory, sessions, and context control across long workflows.

• Human-in-the-loop features let agents pause for approval before acting.

• Plugins create system-wide rules instead of one-off agent behavior.

• Agents can now collaborate across systems using the A2A protocol.

• The shift is from isolated agents to coordinated ecosystems.

We’re entering the messy phase of AI. Not where models improve, but where systems get complicated. One agent is easy to build. A network of agents that behaves predictably is not. The companies that solve coordination, not intelligence will quietly define how AI actually gets used.

BREAKTHROUGH

AI isn’t running out of power, it’s wasting it

ScaleOps is betting on a quiet truth. The real AI problem isn’t shortage, it’s inefficiency.

Here’s everything you need to know:

• AI demand is exploding, but much of the compute sits idle.

• GPUs are over-provisioned, underused, and still massively expensive.

• Most tools show problems, but stop short of fixing them.

• ScaleOps built software that reallocates resources in real time.

• Its system connects application needs directly to infrastructure decisions.

• The result can cut cloud and AI costs by up to 80%.

• This shifts infrastructure from static setups to fully autonomous systems.

We assume progress comes from building more. More chips. More data centers. More power. But sometimes progress comes from using what already exists better. If AI keeps scaling, efficiency won’t be optional. It will quietly decide who survives the cost curve.

STRATEGY

The shift from answers to action has already started

Alibaba Group is quietly building a different kind of AI. Not smarter chatbots actual digital workers.

Here’s everything you need to know:

• Most AI today generates text, images, or predictions, but it stops there.

• A new category, called agentic AI, goes further and completes tasks end-to-end.

• These systems can run workflows, make decisions, and act with minimal input.

• Alibaba is already building tools like Wukong to manage multiple AI agents.

• Its Accio Work platform can handle sourcing, taxes, and operations autonomously.

• This shifts AI from “assistant” to something closer to a digital employee.

• The real opportunity isn’t better answers, it’s less work humans need to do.

We’re used to judging AI by how well it talks. That’s the wrong metric now. The real shift is whether it can do. If AI starts running parts of businesses, not just supporting them, then the winners won’t be the smartest models. They’ll be the ones that actually replace effort.

Free Guides

My Free Guides to Download:

Thinking about launching your own newsletter? Stop overcomplicating it.

We use Beehiiv to write, grow, and monetize all in one place.

If you’ve been waiting for the “right time” this is it.

🚀 Founders & AI Builders, Listen up!

If you’ve built an AI tool, here’s an opportunity to gain serious visibility.

Nextool AI is a leading tools aggregator that offers:

• 500k+ page views and a rapidly growing audience.

• Exposure to developers, entrepreneurs, and tech enthusiasts actively searching for innovative tools.

• A spot in a curated list of cutting-edge AI tools, trusted by the community.

• Increased traffic, users, and brand recognition for your tool.

Take the next step to grow your tool’s reach and impact.

Reach 150,000+ READERS:

Expand your reach and boost your brand’s visibility!

Partner with Nextool AI to showcase your product or service to 140,000+ engaged subscribers, including entrepreneurs, tech enthusiasts, developers, and industry leaders.

Ready to make an impact? Visit our sponsorship website to explore sponsorship opportunities and learn more!