In 2026, software teams scale delivery safely and efficiently using AI agents, semantic layers, platform engineering, supply-chain security, observability, and FinOps.
Agentic AI can transform testing—but only if it’s controlled. Start small, add guardrails, integrate tools, and scale autonomy once reliability and cost are proven.
Legacy systems are full of free-text fields where valuable business data goes to die. NLP pipelines turn messy maintenance logs into structured, actionable insights.
DPoP binds access tokens to a client's key so even if intercepted, they can't be misused. It's mandatory for EUDI/HAIP 1.0 and supported since Spring Boot 3.5.
This study examines raw agent systems, from single-agent frameworks to multi-agent networks, and discusses LangGraph implementations and their significant challenges.
Java 8’s java.time API finally fixed the long-standing problems of Date and Calendar, but real applications still require constant conversion between time zones.
Legacy modernization fails because teams try to decipher millions lines of code. Here is a pattern to slim down systems by reverse-engineering the data.
While large language models (LLMs) dominate the AI conversation, AutoML remains the king for structured data. Here’s how to choose the right tool for your infrastructure.
Token costs are bottlenecking AI systems. Learn how TOON, a token-oriented format, cuts LLM costs and boosts efficiency at scale for high-volume pipelines.
Automatically transform legacy code into efficient microservices. This approach balances design with hardware limits, ensuring software runs fast on cars and IoT devices.
LLMs reshape data engineering by automating ETL tasks, enabling natural language analytics, and empowering faster, smarter decision-making without replacing engineers.