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From Prompt Loops to Systems: Hosting AI Agents in Production (Mon, 23 Feb 2026)
An agent can reason well and still fail badly. Most teams do not notice this during early experiments because nothing is under pressure yet. The model calls tools, answers questions, and produces outputs that look correct. From the outside, the system works. The problems surface later, once the agent is expected to run continuously instead of intermittently. Restarts become normal, context has to survive across runs, external services are often involved, and their actions are not always closely monitored. That is where the difference shows. At that point, outcomes depend far less on how the agent reasons and far more on how it is hosted, because hosting determines what happens when execution is interrupted, state disappears, or permissions suddenly block an action.
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Building a Sentiment Analysis Pipeline With Apache Camel and Deep Java Library (DJL) (Mon, 23 Feb 2026)
Sentiment analysis is now a key part of many applications. Whether you’re processing customer feedback, sorting support tickets, or tracking social media, knowing how users feel can be just as important as knowing what they say. For Java developers, the main challenge isn’t finding machine learning models, but applying them within the existing or new Java applications without relying on Python. Most NLP models are shown in Python notebooks, while real systems use file pipelines, routing, retries, fallbacks, and monitoring. Many teams find it hard to connect these pieces smoothly.
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Azure AI Search at Scale: Building RAG Applications with Enhanced Vector Capacity (Mon, 23 Feb 2026)
In the rapidly evolving landscape of Generative AI, the Retrieval-Augmented Generation (RAG) pattern has emerged as the gold standard for grounding Large Language Models (LLMs) in private, real-time data. However, as organizations move from proof of concept (PoC) to production, they encounter a significant hurdle: scaling. Scaling a vector store isn’t just about adding more storage; it’s about maintaining low latency, high recall, and cost efficiency while managing millions of high-dimensional embeddings. Azure AI Search (formerly Azure Cognitive Search) has recently undergone major infrastructure upgrades, specifically targeting enhanced vector capacity and performance.
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Observability Without Cost Telemetry Is Broken Engineering (Fri, 20 Feb 2026)
I've run production systems where we could tell you the p99 latency of any endpoint down to the microsecond, but couldn't explain why our AWS bill jumped $40,000 in a single weekend. That disconnect — between operational visibility and financial reality — is where most observability strategies quietly fail. The orthodox telemetry trinity (metrics, logs, traces) gives you performance. Error rates. Request volumes. Latency distributions that let you argue about whether 250 ms is acceptable for a search API. What it won't tell you is that the microservice you just optimized for speed now costs $0.03 per invocation instead of $0.002, and at scale, that rounding error becomes someone's quarterly budget.
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DevOps Cafe Podcast

DevOps Cafe Ep 79 - Guests: Joseph Jacks and Ben Kehoe (Mon, 13 Aug 2018)
Triggered by Google Next 2018, John and Damon chat with Joseph Jacks (stealth startup) and Ben Kehoe (iRobot) about their public disagreements — and agreements — about Kubernetes and Serverless. 
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DevOps Cafe Ep 78 - Guest: J. Paul Reed (Mon, 23 Jul 2018)
John and Damon chat with J.Paul Reed (Release Engineering Approaches) about the field of Systems Safety and Human Factors that studies why accidents happen and how to minimize the occurrence and impact. Show notes at http://devopscafe.org
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DevOps Cafe Ep. 77 - Damon interviews John (Wed, 20 Jun 2018)
A new season of DevOps Cafe is here. The topic of this episode is "DevSecOps." Damon interviews John about what this term means, why it matters now, and the overall state of security.  Show notes at http://devopscafe.org
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