#production
9 articles tagged production, from AI, AI Engineering, Java. Latest: “5 Security Checks Every App Builder Must Run Before Launch”.
5 Security Checks Every App Builder Must Run Before Launch
A practical checklist of 5 AI-assisted security audits — secrets, data flow, production hardening, deep vulnerabilities, and the attacker's perspective.
I Rebuilt Our AI Agents on Kafka Instead of REST — Here's the Full Java Architecture
REST between AI agents works until agent #4 shows up. The full Java/Spring Kafka topic design, the ReAct loop as a state machine, and a free audit trail.
Designing the AI Request Pipeline: 8 Layers Between User Input and Your LLM
A production architecture blueprint for every layer between a user's message and a safe, reliable LLM response — with Spring Boot code you can actually use.
We Let Claude Code Refactor Our 200K-Line Java Monolith. Here's the Honest Result.
A real post-mortem on 6 weeks of AI-assisted refactoring — what it got right, what it silently broke, and the hybrid model we actually shipped.
Feature Flags for AI: The Deployment Pattern That Saved Us From 3 Production Disasters
Why LLM features need flags more than anything you've shipped — the four flag patterns that let us swap models, A/B test prompts, and kill broken features fast.
10 Spring Boot Mistakes That Silently Kill Production Apps
A practical breakdown of the most common Spring Boot anti-patterns causing degraded performance, memory leaks, and outages — with before/after code.
Event-Driven AI: How Kafka Streams Changed the Way We Run LLMs in Production
From blocking HTTP calls to async event streams — the architecture shift that halved latency, eliminated 3AM pages, and lets us process 2M AI requests a day.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything once the request ends. How production systems build short-term, long-term, and semantic memory — with Spring AI and pgvector.
Why Most Enterprise AI Projects Die in Production (And the 5 Patterns That Actually Survive)
Why enterprise AI projects fail in production: it is rarely the model. The failures happen earlier, in scope, data, cost control and ownership.