An architectural pattern where multiple specialized AI agents collaborate through a central orchestrator and leverage tools to solve complex user objectives.
RAG helps AI retrieve relevant data. GraphRAG connects entities and relationships. Context engineering turns both into accurate, safe, production-ready AI systems.
Use workflows for control, agents for flexibility, and multi-agent systems only when complexity truly demands it. Add intelligence only where it makes a real difference.
Learn how Conversational Risk Accumulation (CRA) helps detect session-level risks in long AI chats using telemetry, drift tracking, and soft guardrails.
AI can create frontend code in a matter of seconds. However, subsequently, the team has to deal with reviews, accessibility, performance, and maintenance.
The article focuses on moving away from traditional, "imperative" ETL processes to a modern, "declarative" approach using the Databricks Lakeflow platform.
Graph-RAG accuracy is only the starting point; evaluate the evidence path, rule compliance, latency, and feedback loop before calling it production-ready.
Store large and cold datasets in Iceberg on S3, query them through Spectrum, and reserve Redshift local tables for workloads that need low latency or high concurrency.
Enterprise AI success depends on scalable architecture, governance automation, AI operations, observability, and developer-first enablement strategies.
Learn how to implement the Planning Pattern with Enterprise Java, Jakarta EE, CDI, and LangChain4j, enabling AI to transform business goals into executable workflows.
REST APIs waste tokens. UMA uses MCP to bridge agents to local Wasm/WASI-NN, slashing costs and latency by replacing raw data with deterministic, executable intent.