Static analysis for LLM agents that flags prompt-injection risks—like confused deputy flows and dynamic prompts—before runtime, improving security and auditability.
RAG answers can stay stable while evidence shifts. Learn why evidence stability matters for reproducibility, auditability, and debugging — and how to check it.
Moving a hardcoded LangGraph React agent into LaunchDarkly AI Configs so prompts, models, tools, tracking, and rollout testing can be changed without redeploying.
My goal here is to experiment with an alternative approach leveraging Java's tried-and-tested, robust functionalities that have been available since JDK 1.5.
Many MVPs get too big because teams treat several user-facing systems and vendor-dependent workflows as one app instead of planning one complete path first.
Agentic Agile Office uses autonomous AI agents to cut admin overhead, detect risks early, and shift teams from manual tracking to intelligent, high-velocity delivery.
AI models do not fail due to bad coding; they fail due to an upstream change in the input. Combine contracts with circuit breakers to stop bad data from entering models.
MuleSoft IDP uses AI to extract and structure data from documents like invoices and PDFs, helping automate workflows, reduce errors, and improve processing speed.
Jakarta EE 12 introduces the Data Age of Enterprise Java with Jakarta Query, improved data access, and a unified model for cloud-native and polyglot systems.
A practical guide to SaaS architecture decisions that determine whether platforms scale cleanly or collapse under technical debt, security, and growth pressure.