If you're looking for a user-friendly Linux distribution, your destination could depend on your starting point.

At HumanX, Ryan is joined by Philip Rathle, CTO at Neo4j to discuss what knowledge context means for AI agents, how limitations like stale training data make the model-only approach to agents a bad fit for enterprise environments, and how Graph RAG raises the bar for accuracy and reduces context rot by combining vectors with a knowledge graph so agents are more targeted and connected.

Instead of pressing buttons on your monitor or hunting through your Windows settings, here's how you can now adjust your display directly from the system tray - plus other new PowerToys perks.

A Georgia data center developed by QTS used nearly 30 million gallons of water through two unaccounted-for connections before residents complained about low water pressure and the county utility discovered the issue. "All told, the developer, Quality Technology Services, owed nearly $150,000 for using more than 29 million gallons of unaccounted-for water," reports Politico. "That is equivalent to 44 Olympic-size swimming pools and far exceeds the peak limit agreed to during the data center planning process." From the report: The details were revealed in a May 15, 2025 letter from the Fayette County water system to Quality Technology Services, which outlined the retroactive charge of $147,474. The letter did not specify how many months the unpaid bill covered, but when asked about it Wednesday, Vanessa Tigert, the Fayette County water system director, said it was likely about four months. A QTS spokesperson said the timeframe was 9-15 months. Once the data center was notified, it paid all retroactive charges, a QTS spokesperson said in an email, noting the unmetered water consumption occurred while the county converted its system to smart meters. The Fayette County water system confirmed the data center's meters are now fully integrated and tracked. Tigert, the water system director, blamed the issue on a procedural mix-up. "Fayette County is a suburb, it's mostly residential, and we don't have much commercial meters in our system anyway," she said. "And so we didn't realize our connection point wasn't working." The incident became public last week when a county resident obtained the 2025 letter to QTS through a public records request and posted it on Facebook, prompting outrage from residents concerned about the data center's water consumption. [...] Tigert, who sent the 2025 letter to QTS, said the utility didn't know about the water hookups because the connection process "got mixed up" as the county transitioned to a cloud-based system while also trying to accommodate an industrial customer. Tigert also said her staff is small and at capacity. "Just like any water system, we don't have enough staff. We can't keep staff," she said. "I've got one person that's doing inspections and plan review, and so he's spread pretty thin." She said it's possible her staff did know about hookups but that she hadn't been able to locate the inspection report. "I may have hit 'send' too soon," she said about the 2025 letter to QTS. While the utility charged the data center a higher construction rate for the unapproved water consumption, Tigert confirmed the utility did not penalize or fine the data center. For what it's worth, the Blackstone-owned company says its data centers use a closed-loop cooling system that does not consume water for cooling. The reason for last year's high water use, according to QTS, was the temporary construction work such as concrete, dust control, and site preparation. Once the campus is fully operational, it should only use a small amount of water for things like bathrooms and kitchens. But that point could still be years away, as construction and expansion in Fayetteville may continue for another three to five years.

Read more of this story at Slashdot.

Microsoft’s Customer Zero blog series gives an insider view of how Microsoft builds and operates Microsoft using our trusted, enterprise-grade agentic platform. Learn best practices from our engineering teams with real-world lessons, architectural patterns, and operational strategies for pressure-tested solutions in building, operating, and scaling AI apps and agent fleets across the organization.

Challenge: Scaling agents to production changes the requirements

As teams move from experimenting with AI agents to running them in production, the questions they ask begin to change. Early prototypes often focus on whether an agent can reason to generate useful output. But once agents are placed into real systems where they continuously need to serve users and respond to events, new concerns quickly take center stage: reliability, scale, observability, security, and long‑running operations.

A common misconception at this stage is to think of an agent as a simple chatbot wrapped around an API. In practice, an AI agent is something very different. It is a service that listens, thinks, and acts, ingesting unstructured inputs, reasoning over context, and producing outputs that may span multiple phases. Treating agents as services means teams often need more than they initially expect: dependable compute, strong security, and real-time visibility to run agents safely and effectively at scale.

When we kick off an agent loop, we provide input that informs the context it recalls for the task, the data it connects to, the tools it calls, and the reasoning steps it outlines for itself to generate an output. Agent needs are different from traditional services in hosting, scaling, identity, security, and observability; it’s a product with a probabilistic nature that requires secure, auditable access to many resources at the same lightspeed performance that users expect from any software.

This isn’t the first time that the software industry needed to evolve its thinking around infrastructure. When modern application architectures began shifting from monolithic apps toward microservices, existing infrastructure wasn’t built with that model in mind. As systems were reconstructed into independent services, teams quickly discovered they needed new runtime architecture that properly accommodated microservice needs. The modern app era brought new levels of performance, reliability, and scalability of apps, but it also warranted that we rebuild app infrastructure with container orchestration and new operational patterns in mind.

AI agents represent a similar inflection. Infrastructure designed for request‑response applications or stateless workloads wasn’t built with long‑running, tool‑calling, AI‑driven workflows in mind. As the builders of Foundry Agent Service, we were very aware that traditional architectures wouldn’t hold up to the bursty agentic workflows that needed to aggregate data across sources, connect to several simultaneous tools, and reason through execution plans for the output that we needed. Rather than building new infrastructure from scratch, the choice for building on Azure Container Apps was clear. With over a million Apps hosted on Azure Container Apps, it was the tried-and-true solution we needed to keep our team focused on building agent intelligence and behavior instead of the plumbing underneath.

Solution: Building Foundry Agent Service on a resilient agent runtime foundation

Foundry Agent Service is Microsoft’s fully managed platform for building, deploying, and scaling AI agents as production services. Builders start by choosing their preferred framework or immediately building an agent inside Foundry, while Foundry Agent Service handles the operational complexity required to run agents at scale.

Let’s use the example of a sales agent in Foundry Agent Service. You might have a salesperson who prompts a sales agent with “Help me prepare for my upcoming meeting with customer Contoso.” The agent is going to kick off several processes across data and tools to generate the best answer: Work IQ to understand Teams conversations with Contoso, Fabric IQ for current product usage and forecast trends, Foundry IQ to do an AI search over internal sales materials, and even GitHub Copilot SDK to generate and execute code that can draft PowerPoint and Word artifacts for the meeting. And this is just one agent; more than 20,000 customers rely on Foundry Agent Service.

At the core of Foundry Agent Service is a dedicated agent runtime through Azure Container Apps that explicitly meets our demands for production agents. Agent runtime through flexible cloud infrastructure allows builders to focus on making powerful agent experiences without worrying about under-the-hood compute and configurations.

This runtime is built around five foundational pillars:

1. Fast startup and resume. Agents are event‑driven and often bursty. Responsiveness depends on the ability to start or resume execution quickly when events arrive.
2. Built‑in agent tool execution. Agents must securely execute tool calls like APIs, workflows, and services as part of their reasoning process, without fragile glue code or ad‑hoc orchestration.
3. State persistence and restore. Many agent workflows are long‑running and multi‑phase. The runtime must allow agents to reason, pause, and resume with safely preserved state.
4. Strong isolation per agent task. As agents execute code and tools dynamically, isolation is critical to prevent data leakage and contain blast radius.
5. Secure by default. Identity, access, and execution controls are enforced at the runtime layer rather than bolted on after the fact.

Together, these pillars define what it means to run AI agents as first‑class production services.

Impact: How Azure Container Apps powers agent runtime

Building and operating agent infrastructure from scratch introduces unnecessary complexity and risk. Azure Container Apps has been pressure‑tested at Microsoft scale, proving to be a powerful, serverless foundation for running AI workloads and aligns naturally with the needs of agent runtime.

It provides serverless, event‑driven scaling with fast startup and scale‑to‑zero, which is critical for agents with unpredictable execution patterns. Execution is secure by default, with built‑in identity, isolation, and security boundaries enforced at the platform layer. Azure Container Apps natively supports running MCP servers and executing full agent workflows, while Container Apps jobs enable on‑demand tool execution for discrete units of work without custom orchestration.

For scenarios involving AI‑generated or untrusted code, dynamic sessions allow execution in isolated sandboxes, keeping blast radius contained. Azure Container Apps also supports running model inference directly within the container boundary, helping preserve data residency and reduce unnecessary data movement.

Learnings for your agent runtime foundation

Make infrastructure flexible with serverless architecture. AI systems move too fast to create infrastructure from scratch. With bursty, unpredictable agent workloads, sub‑second startup times and serverless scaling are critical.

Simplify heavy lifting. Developers should focus on agent behavior, tool invocation, and workflow design instead of infrastructure plumbing. Using trusted cloud infrastructure, pain points like making sure agents run in isolated sandboxes, properly applying security policy to agent IDs, and ensuring secure connections to virtual networks are already solved. When you simplify the operational overhead, you make it easier for developers to focus on meaningful innovation.

Invest in visibility and monitoring. Strong observability enables faster iteration, safer evolution, and continuous self‑correction for both humans and agents as systems adapt over time.

Want to learn more?

• Learn about building and hosting agents with Foundry Agent Service
• Discover agent runtime through Azure Container Apps
• Read about best practices for managing agents

1184. This week, we look at the history of lingua francas, from the original mix of Italian, French, Spanish, Arabic, and Turkish used during the Crusades to today's global English. Plus, we look at whether it's wrong to use "who" for animals, "that" instead of "who" for people, and "whose" for inanimate objects.

The lingua franca segment was written by Alexandra Aikhenvald, a Professor and Australian Laureate Fellow at Jawun Research Institute, CQ University in Australia. It originally ran on The Conversation and appears here through a Creative Commons license.

AI systems confusing dog faces with blueberry muffins.

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