I found the following article particularly interesting... Scale isn't the problem, it is complexity, because we keep adding layers of abstraction, microservices, orchestration, and AI, but not enough ways actually to observe what's going on under the hood. Observability isn't enough. We need understandability. Therefore, teams don't need to measure everything; they need to understand how systems behave without drowning in data. AI is the problem, and the solution, because AI-generated code is a black box, but AI is the only thing that's really going to be able to deal with the firehose of logs and traces and events that we're generating today. For AI systems, don't look at the guts. I/O is more important than internals, because looking inside the model yields diminishing returns, while consistent, deep monitoring of inputs and outputs in the wild is more actionable. Automation can't just be about alerting, because remediation is going to have to be a first-class capability, and handwritten runbooks won't scale, therefore, AI-assisted remediation is going to be the default. The next competitive advantage is explainability and trust, because teams that blend human intuition with the pattern recognition of AI, using the "centaur model", will understand systems faster and respond more effectively. https://lnkd.in/dBuMz6eZ
Why AI is the key to understanding complex systems
This title was summarized by AI from the post below.
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🧠The next frontier in AI-driven software delivery isn’t more code generation — it’s context. In this diginomica piece, Jyoti Bansal explains how Harness AI uses knowledge graphs to give AI agents deep understanding of how systems connect — mapping relationships across testing, security, and reliability to enable truly trustworthy automation. 🤝 The result: a foundation for safe, autonomous software delivery at scale. 🔗 https://lnkd.in/g4aTWSGg
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The era of AI agents is upon us, but trust remains a crucial concern for developers. This article by Mark Cavage highlights how over 20 million developers leverage Docker to build secure software that instills confidence in AI systems. I found it interesting that as we embrace this technology, the emphasis on trust and security becomes even more critical. What strategies do you think are essential for maintaining trust in AI applications?
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Senior IT Manager | Digital Transformation Expert | Agile Leadership | IT Strategy
Lately, I’ve been thinking a lot about how software feels different than it used to. We have AI-assisted development, faster pipelines, and more automation than ever — yet reliability seems to be going in the opposite direction. I came across this piece, “The Great Software Quality Collapse,” and it really resonated: https://lnkd.in/gXeSEJy9 It captures something I’ve seen firsthand — that in our race to accelerate delivery, we’ve quietly accepted a lower standard of quality. We’ve optimized for speed, not sustainability. From a leadership and AI perspective, that trade-off is dangerous. AI won’t fix systemic cultural issues. If anything, it amplifies them — faster. Quality doesn’t fail in one big moment; it erodes through small compromises, justified one sprint at a time. It’s a thoughtful read — and a timely reminder that true progress isn’t just about how fast we can build, but how well we can sustain quality while using new tools. #AI #SoftwareQuality #EngineeringLeadership #TechCulture #DevOps #Architecture
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We all know that AI can’t deploy infrastructure, not today. This isn’t because AI can’t generate IaC, it’s because infrastructure is so much more than IaC. And the free-form, hallucinatory side of AI can't be unleashed on your critical infra. But there is a future where AI Agents can and will deploy infrastructure. It isn’t that far off, and environment orchestration can deliver that by providing agents with: - A controlled set of pre-approved options, not infinite possibilities - Clear dependency and deployment sequences; and - Standards and guardrails https://lnkd.in/dRYFjVEn
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Gen AI / ML Lead @Transporeon (Trimble Company) | Architecting Enterprise-Scale LLM & AI Platforms | Driving Responsible AI Innovation
Building production AI agents at scale revealed a critical insight: single middleware ⛙ patterns aren't enough. Hence, I've published guide on about building AI agents that don't break in production 😄. The issue: Most agents fail at scale because you're mixing compliance, state management, and cost all in one place. It becomes a mess. I give a deep-dive to LangChain Middleware architecture: how to separate concerns so compliance, logging, caching each work independently. Deep Agents pattern: why splitting work into specialized agents actually makes things simpler. Handling 1M+ records without exploding. Cost optimization that cuts API bills by 75%. Real example: Financial compliance system analyzing 1M transactions, $400 total cost, zero violations. If you're building AI at scale, the patterns here will save you months. ⏳ Full analysis: https://lnkd.in/eYer3TwR
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Have you ever postponed the application of product updates because you didn’t have the time? Let’s say you want to take advantage of Xperience by Kentico’s October 2025 Refresh that brings support for .NET's ILogger<T> as our primary logging API. Although the existing IEventLogService has not yet been deprecated, adopting ILogger<T> across an Xperience project brings many benefits. Traditionally, these benefits could come at a cost - the developer's time in adapting their code to use new APIs. Today there's a way to reduce these costs, often significantly - agentic AI software development. Our Product Evangelist, Sean Wright, has written a step-by-step article that shows you how to take an agentic approach to updating your projects and taking advantage of these new capabilities: https://lnkd.in/eYMP3VCc
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Unlocking higher productivity with context-aware AI To unlock higher productivity with AI-assistance, the AI itself needs to be context-aware. Red Hat Developer Hub 1.8 introduces the foundational architecture to make this happen, turning the platform into an intelligent partner that understands your environment.
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When engineers can simply chat with their infrastructure and say something like "I need a new cache cluster for staging and production" and that just happens. No ticket, no code change, no fighting for priority, it just happens.
We all know that AI can’t deploy infrastructure, not today. This isn’t because AI can’t generate IaC, it’s because infrastructure is so much more than IaC. And the free-form, hallucinatory side of AI can't be unleashed on your critical infra. But there is a future where AI Agents can and will deploy infrastructure. It isn’t that far off, and environment orchestration can deliver that by providing agents with: - A controlled set of pre-approved options, not infinite possibilities - Clear dependency and deployment sequences; and - Standards and guardrails https://lnkd.in/dRYFjVEn
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The article explores how Docker is revolutionizing the development of trusted AI, emphasizing the importance of security and trust in deploying AI agents. What stood out to me was the assertion that over 20 million developers trust Docker for secure software solutions. It’s fascinating to consider how this foundational technology can enhance the reliability of AI systems. How do you see trust evolving in the AI landscape as more teams adopt these technologies?
