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DZone.com Feed Recent posts on DZone.com Why LLM Pipelines Fail in Production and How Temporal and Kafka Fix Them 5 Aug 2026, 15:00 by Akhil Madineni A production LLM pipeline is rarely just a prompt and a response. It typically combines retrieval, prompt rendering, model inference, output shaping, validation, persistence, and downstream actions. That broader shape is why many systems look stable in a demo and then become fragile under live traffic. The model call is only one component; the operational problem is the workflow around it. Provider APIs impose rate limits, structured outputs still need application-level checks, and external calls introduce failure ambiguity that ordinary request-response code does not handle well. Where the Breakage Starts Most production failures happen between steps, not inside the prompt. A request enters an API, context is loaded, a model call is sent, the response is parsed, a downstream action is triggered, and a record is written. If the provider generated output but the network dropped before the caller saw it, the system no longer has a clean answer to whether the operation should be retried or treated as complete. Kafka’s default delivery model is at least once, and Temporal’s documentation is explicit that activities may be retried and therefore should be idempotent. That combination makes duplicate side effects the default risk unless the pipeline is designed around durable state and idempotent writes.  ‍ The Retry Budget Pattern: How to Stop Retry Storms in API-Led and Microservice Systems 5 Aug 2026, 14:00 by Manjeera Chanda The Production Story Several years ago, my team made a decision that felt obviously correct: If a downstream call fails, retry it. More retries, more resilience. We set three retries on every integration touching our order-fulfillment platform, shipped it on a Thursday, and went home feeling good about our reliability posture. Six weeks later, retries were the single largest source of traffic in the platform. ‍ Securing AI Agents at the API Layer: 5 Controls That Actually Matter 5 Aug 2026, 13:00 by Priyanka Jayavel Most API security programs were built for predictable consumers: mobile apps, backend services, partner integrations, and the occasional script. Each of those calls your APIs in fairly bounded ways. AI agents do not fit that model. An agent does not just call an API. It decides which APIs to call, in what order, and often keeps going until it reaches a result. That autonomy is the point of using an agent, but it is also what makes it dangerous: a single misconfigured agent can generate thousands of requests in minutes, reach systems it was never meant to touch, or chain APIs together in a sequence no human ever designed. ‍ Building an Async Validation API With AWS Bedrock Agents and Serverless Architecture 5 Aug 2026, 12:00 by Rohit Nagpal As a data engineer, I’ve noticed business teams submitting intake forms, compliance documents, and project proposals that a tech team then manually validates against a set of predefined business rules stored in a database that gets updated quarterly. The time it takes to validate a single form is typically in the hours, and by the time you’ve validated the form, the submitter has moved on to other work. When I needed to validate project intake forms against 60+ business rules of financial, compliance, and other types of business rules and guidelines (some of them to be used in a deterministic way and others to be used in a more nuanced manner), I knew that a simple if-else logic-based manual review process would not scale. ‍ ‍ 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.