Devtron Addresses Complexity in Modern Infrastructure and Application Deployments

Sharing a simple, low-level view of how we can deploy AI Agents on Red Hat OpenShift. This architecture brings together GitOps-driven delivery, secure agent orchestration, and a complete RAG pipeline (ingestion, embeddings, vector DB) with optional GPU-based LLM inference, backed by strong observability and cluster security. If you are planning enterprise grade GenAI on Kubernetes, this is the baseline that keeps things scalable, governed, and production-ready. #OpenShift #Kubernetes #GenAI #AIAgents #RAG #LLM #MLOps #PlatformEngineering #DevSecOps #GitOps #Observability #CloudNative #EnterpriseAI

Deploying Scalable LLM Tools via Remote MCP on Kubernetes By Pratik A production-ready architecture for running Model Context Protocol servers remotely on Kubernetes, ensuring scalability, isolation and observability. Scale LLM Tools With a Remote MCP Architecture on Kubernetes As AI systems move from experimentation to production, developers are starting to disc... https://lnkd.in/ewKWj8BD

Software knowledge is siloed across different code repositories and scattered SaaS applications. Building a complete system of context requires connecting multiple sources and unifying the data—capturing code, existing documentation, daily PR descriptions, people metadata, permissions, and discussions. This unified index generated by xhawk gives AI the foundation it needs to truly understand software work.

The latest update for #Lightrun includes "Runtime context: The key to reliable #AI-generated code" and "Lightrun 'Runtime Context' Empowers AI Coding Agents to Build Software That Works in the Real World". #monitoring #DevOps #logging https://lnkd.in/dATxs8s

📝 🎯 Key Takeaway: Docker Desktop 4.22 Launch: Exploring the New AI‑Powered Dev Environments ## Introduction: Docker Desktop 4.22 Takes a Leap Forward Docker has been the cornerstone of modern container development for over a decade. With the release of **Docker Desktop 4.22**, the platform s... Read more: https://lnkd.in/g3rKQDHs #Categorydocker

How do your Kubernetes operations compare to others running it at scale? The Kubernetes in the Enterprise: 2025 Trends report offers new benchmarks on architecture, tooling, developer experience, and how AI/ML is changing the game. Find out what’s working, what’s shifting, and what’s next for enterprise Kubernetes ➡️ https://bit.ly/4hYiaik

How do your Kubernetes operations compare to others running it at scale? The Kubernetes in the Enterprise: 2025 Trends report offers new benchmarks on architecture, tooling, developer experience, and how AI/ML is changing the game. Find out what’s working, what’s shifting, and what’s next for enterprise Kubernetes ➡️ https://bit.ly/4hYiaik

Part 27 of DEV Track Spotlight 🤖 Together with Allen Helton (AWS Serverless Hero and Ecosystem Engineer at Momento), I demonstrated how to build autonomous multi-agent systems that actually work in production at AWS re:Invent 2025. Session: Building Scalable, Self-Orchestrating AI Workflows with A2A and MCP The challenge: How do you move beyond single-agent demos to build scalable, reliable multi-agent systems that handle complex workflows autonomously? The answer lies in structured protocols, disciplined architecture, and treating AI agents like distributed systems. What we demonstrated: • Agent-to-Agent (A2A) Protocol - Dynamic discovery without hardcoded integrations • Model Context Protocol (MCP) - Standardized, typed interfaces for tools • RISEN Framework - System prompts for deterministic behavior • SwiftShip Platform - Multi-agent logistics handling delivery exceptions Key insights from the session: 🤖 Agents choreograph themselves - Discover each other at runtime, delegate work autonomously, adapt workflows based on real-time context 🔧 The 4-phase agent loop - Compose (load context), Query (call LLM), Execute (perform actions), Continue (decide next steps) 🏗️ Domain isolation prevents hallucinations - Each agent has specialized tools for its domain 📋 RISEN prompts ensure determinism - Clear structure removes ambiguity about what the agent is, should do, and should never attempt 🔒 Multi-tenant security by design - Framework injects tenant ID from authenticated context 🎯 Treat agents like microservices - Service discovery becomes agent discovery, circuit breakers become health checks Best demo moment: Allen's "destroyed package" scenario: "Package caught on fire, then were run over by my car, and the car behind me. Then the ashes caught on fire." The agents reasoned about this absurd input and coordinated refund processing, inventory allocation, and replacement order creation autonomously. As Allen noted, "Multi-agent systems require the same operational rigor as traditional distributed systems. The LLM provides reasoning, but the architecture provides reliability." 📝 Read the full deep dive on AWS Builder Center: https://lnkd.in/e5wUW2XU 📺 Watch the session: https://lnkd.in/eCgseBrJ #AWSreInvent #DEVTrack #DeveloperCommunity #AWSCommunity #AWSHeroes

Navigating the Agentic Mesh: Design Patterns for Refactoring and Orchestrating In software architecture, migration is a constant. After spending the last decade breaking down monolithic services into microservices for scalability, we now face a pivotal moment with Artificial Intelligence. The ‘Chatbot’ has become the new Monolith. https://lnkd.in/dTp7btDQ

Is your application architecture ready for the next decade? ☁️ The shift to cloud-native development is no longer just a trend—it’s a fundamental requirement for building scalable, resilient, and future-proof software. From microservices and containerization to the rise of GitOps and AI integration, staying ahead of these shifts is critical for digital transformation. Our latest article explores the key trends shaping the future of development and how your business can leverage them for greater agility. Read the full article here: https://lnkd.in/gY4uTdfW #CloudNative #SoftwareDevelopment #Microservices #DigitalTransformation #Aqon #TechTrends