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DZone.com Feed Recent posts on DZone.com From Pilot to Production: The Six Agent Patterns That Determine Whether Your AI Program Scales or Stalls Jul 2nd 2026, 15:00 by BALAJI BARMAVAT We've been running AI agents in production across enterprise cloud support for several years now. I've watched the same pattern play out dozens of times across organizations of every size: a team builds a compelling pilot, leaders get excited, and then... it stalls. Not because the technology failed. Because the operating model was never designed for what agents actually do when they stop assisting humans and start executing work on their behalf. This isn't a failure of ambition. It's a failure of classification. Organizations treat all agent initiatives the same way, same governance, same ownership model, same success metrics — and then wonder why agents that draft emails scale easily while agents that process workflows create governance crises by agent fifty. ‍ Multi-Agent Software Engineering: One Coding Agent Isn't Enough Jul 2nd 2026, 14:00 by Jithu Paulose Coding agents are good now. They can write a function, fix a failing test, or walk you through a chunk of legacy code you'd rather not read. That part is settled. The harder question is what happens when you hand one a real piece of delivery work, something that has to change the database and the API and the UI and the tests all together, and keeps running long after you've stepped away from your desk. That's usually where a single agent starts to struggle, and it isn't because the model isn't smart enough. The limit is human attention. A team might have fifty things sitting in its backlog that an agent could help with, but somebody still has to scope each one, keep an eye on it, review what comes back, and confirm it actually works. So you can generate code far faster than before and still ship at about the same pace. The slow part just moved. ‍ Dead Letter Queue Patterns in Apache Flink: Handling Poison Messages Without Stopping Your Stream Jul 2nd 2026, 13:00 by Rohit Muthyala Streaming systems usually fail in one of two ways: Loudly, when infrastructure breaks Quietly, when one bad record keeps replaying until the pipeline is effectively dead The second failure mode is more dangerous because it often starts with something small: malformed JSON, an unexpected schema change, a missing required field, or a downstream timeout that was never handled correctly. ‍ Apache Spark Query Optimization on Databricks: Catalyst, AQE, and Photon Engine Jul 2nd 2026, 12:00 by Jubin Abhishek Soni Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization — is what separates a job that runs in 3 minutes from one that runs in 3 hours on identical hardware. Databricks compounds Spark's native Catalyst optimizer with two additional layers: ‍ ‍ You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our terms. If you no longer wish to receive these emails, you can unsubscribe from this feed, edit this subscription, or manage all your subscriptions.