Cloud Wars Live with Bob Evans

In today's Cloud Wars AI Minute, I examine how GitHub Copilot squads and Claude Code agent teams are changing the way AI agents communicate, collaborate, and solve complex problems.
Highlights
00:12— And today, we want to talk about AI coding squads. We're seeing this change about how we're seeing agents being able to go and work on different, really complex projects. And we used to have this one assistant that would go out and everything else, but now we're starting to see teams of agents going and doing things, and we see different configurations of this.
00:54 — So you could have a front-end developer, a back-end developer, an auditor, all of these different things, and they're just basically different personas. And then the beauty of this is where we talk about something like Claude Code subagents, which has been out for a while. This is more the concept of a task agent, where you have a parent that is going to spin off subagents underneath it.
01:34 — Well, when we get into GitHub Copilot squads, we actually see this changing, where what we're having is that each of these different personas are created as members of a team, and the teams work together to be able to come with, like, a common shared team memory and allow them to be able to communicate back and forth, to be able to assign tasks and collaborate and communicate with one another.
02:02 — And this makes it where you end up with a more robust way of actually solving problems, where you're not having to orchestrate it all through a single, a single agent that's supposed to make the decisions on everything. Now you have different personas that can weigh in their opinions and play their role. You can kind of think of it just the same way you would have a human team.
03:15 — But we're going to start seeing more and more of the different AI creators or the different providers of agents starting to take this tack, where they're going to have more of a squad or a team of agents versus having a parent-child relationship, like we've seen in the past. So, something to keep your eye on and pay attention to as you continue to move forward in your AI journey itself.

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In this episode of Cloud Wars Live, Bob Evans speaks with G2 Krishnamoorthy, Vice President of AWS Databases, about how agentic AI is reshaping enterprise data strategies and accelerating business innovation. Krishnamoorthy explains why AI-ready data foundations, knowledge graphs, and purpose-built databases are critical to unlocking the potential of intelligent agents. The conversation explores how AWS customers are dramatically reducing processing times, why forward deployed engineering is transforming technology partnerships, and how security, interoperability, and infrastructure economics will influence the next generation of enterprise AI applications and database innovation.
AWS's AI Data Strategy
The Big Themes:
AI-Ready Data Foundations Drive Innovation: As enterprises embrace agentic AI, establishing a strong data foundation is becoming essential to unlocking sustainable business value. G2 Krishnamoorthy compares agentic AI to a powerful car and the data foundation to the road it needs to travel. Regardless of how sophisticated AI becomes, organizations must ensure their data is accessible, well-structured, and ready to support innovation. Importantly, these investments extend beyond AI applications.
Forward Deployed Engineers Accelerate Transformation: AWS is investing in forward deployed engineering (FDE) to help customers translate emerging technologies into production-ready business solutions. Rather than simply providing database products and technical guidance, AWS engineers work directly within customer environments to understand specific challenges, demonstrate solutions, and support implementation. Krishnamoorthy explains that this approach can span cloud migration, data integration, cataloging, contextual knowledge development, and AI-enabled process redesign.
Agentic AI Requires Stronger Security Governance: As AI agents gain the ability to perform actions on behalf of users, security and governance become increasingly critical. Krishnamoorthy identifies two major priorities for AWS: ensuring trusted agentic operations and strengthening defenses against emerging vulnerabilities. AI agents must operate within appropriate authorization, authentication, and auditing controls while maintaining the identity and permissions of the users they represent. AWS is working with Amazon Bedrock and AgentCore capabilities to establish guardrails around agentic experiences.
The Big Quote: “You need to have the elasticity. You really need to actually be able be truly effortless in terms of managing capacity. You really want to reduce the cost of operations dramatically."
More from G2 Krishnamoorthy:
Follow G2 Krishnamoorthy on LinkedIn or learn about knowledge graphs and AgentCore Identity.

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In this episode of Cloud Wars Live, Bob Evans speaks with Gary Miller, Oracle Customer Success Officer and Executive Vice President of Oracle Customer Success Services, about how AI is reshaping enterprise operations and customer expectations. Miller explains why businesses are shifting from AI experimentation to measurable outcomes. He also discusses how Oracle is evolving its customer success strategy to help organizations accelerate adoption while managing costs and security. Looking ahead to Oracle AI World, Miller previews Fusion Claw, AI Success Navigator, and new capabilities designed to speed implementation and deliver business value.
Industrializing AI for Business
The Big Themes
AI Becomes a Business Imperative: Gary Miller explains that enterprise AI conversations have changed dramatically over the past year. Rather than simply exploring AI assistants or experimenting with emerging technologies, organizations are prioritizing measurable business outcomes. Competitive pressure is pushing companies to adopt agentic AI faster, rethink workflows, and demonstrate returns on their investments. Miller describes this transition as moving from the democratization of AI to its industrialization, emphasizing repeatability, efficiency, and disciplined implementation.
Industrializing AI Requires Coordination: Miller says that industrializing AI involves much more than providing employees with access to intelligent tools. Organizations need structured processes for discovering, deploying, and reusing existing AI capabilities across departments. Without coordination, companies risk duplicating development efforts, increasing expenses, and undermining their expected returns. For large enterprises, the challenge becomes especially significant when thousands of employees can independently create agents.
AI Governance Enables Faster Innovation: As companies accelerate AI adoption, security, governance, and cost management become increasingly important. Miller argues that organizations should establish governance structures before deploying agents extensively. These frameworks must define which data agents can access, what processes they can execute, and when human involvement is required. Oracle's embedded governance capabilities within Fusion provide identity management, access restrictions, and operational boundaries that organizations can use as a foundation for broader AI policies.
Big Quote: "Do a pilot, get it into production. Many many initiatives fail between pilot and production, and then scale it and learn from that in continuous innovation. So it's building. I call it building the muscle for ongoing innovation."
More from Gary Miller and Oracle:
Follow Gary on LinkedIn or learn more about Oracle AI Success Navigator, Fusion Claw, and Oracle AI for Fusion Applications.

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In today's Cloud Wars Minute, I discuss how Christian Klein is transforming SAP from an enterprise applications leader into an AI-powered cloud powerhouse.
Highlights
00:01 — We got some big numbers yesterday from SAP for Q3, and one of the highlights of this, I would say, we see acceleration in its cloud revenue growth, acceleration in its current cloud backlog growth, and also in its cloud ERP suite numbers. So, a powerful quarter for SAP across Q3, and you can really see the evidence here of SAP's ambitious AI strategy starting to kick in.
00:36 — Cloud revenue for Q3 is up 26% to $7.5 billion. Last quarter, I think it was 24% growth. Cloud backlog, 28% to $27 billion. Last quarter, it was 27% growth. Cloud ERP suite up 29% to $6.61 billion. Cloud ERP Suite is a subset of their overall cloud revenue. And the company's total revenue, I thought this was interesting, up 13% to $11.5 billion.
01:08 — Now, certainly its traditional license business is going down. We see the rapid cloud growth pulling the whole company upward. Very, very impressive number there, 13%. Also, SAP announced that its full-year cloud guidance is going to hit what it said will be toward the upper end of the range that the company gave earlier this year.
02:19 — The Autonomous Enterprise, the big new brand promise that SAP announced at Sapphire this year. He said it's gaining customers' confidence. It's trying it in parts. It's feeling more and more comfortable with it. And Klein believes that the Autonomous Enterprise is really starting to kick in with the momentum here.
03:40 — So, as the company gets bigger, its growth rate is increasing — a very difficult thing to do. The growth rates that SAP has been putting up for the last several quarters are much higher than its primary competitors in this category. No other big enterprise — I used to call them apps company, but now we have to call them enterprise apps, agents, data, and platform company — is growing nearly as fast as SAP is.

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In today's Cloud Wars Minute, I discuss how Google Cloud's $3 million in computing credits could help European researchers tackle some of science's most persistent challenges.
Highlights
00:03 — Google Cloud, alongside Google DeepMind, are planning to give up to 200 leading European scientific researchers direct access to Google's frontier models, as well as $3 million in Google Cloud computing credits. The credits are essentially there to ensure researchers have the computing capacity that they need for their research.
00:27 — So, beyond the models, researchers will also have access to some incredibly powerful tools like AlphaFold 3, a well-known tool that helps predict molecular structures, WeatherNext, a weather forecasting tool, and AlphaGenome, which helps researchers study DNA. This is part of a European initiative supporting the EU's AI for Science initiative called RAISE, the Resource for AI Science in Europe.
01:08 — So, there are the obvious geopolitical impacts here. This is certainly going to give the EU's science and technology sector a boost. But for me, this is actually a lot more significant than those potential economic gains. I see this as a really outstanding demonstration of how we can pivot AI towards solving some of the problems that we've been unable to solve without this model infrastructure.
01:34 — Now, I think we've all been promised a great deal in terms of the potential social impact of AI, targeting those really difficult problems, but I think the onus has been on the developers of this technology, the companies, to make those discoveries. For me, this is a much more targeted and scalable approach.
01:54 — Google's committing resources to the RAISE initiative, but the EU is putting the wider framework in place. This, I think, is how we get to the root of some of our most persistent challenges using AI: by not only developing the technology, but also building the structures that enable it to be put to work where it's needed most, and not making it the sole responsibility of the model developers.

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