A practical framework for evaluating your multi-agent context management strategy. From ad-hoc string concatenation to self-evolving context systems - where does your architecture stand?
Enterprise data platforms face a 100,000x query increase from agentic AI. Introducing Symbiotic Agent-Ready Platforms (SARPs) - the architectural paradigm shift needed to survive the transition to machine intelligence.
AI companies are getting sued over training data, agents operate with no permission framework, and users can't control their AI profiles. I wrote four open standards (LLMConsent) to create a decentralized consent protocol for AI—like HTTP but for data rights, agent permissions, and user sovereignty. This is an RFC, not a product.
Explore how Large Language Models (LLMs) are revolutionizing ETL pipelines. Discover advanced techniques like context-driven transformations, semantic joins, and multimodal integration, redefining data engineering with smarter, adaptive, and intelligent workflows.
Buckle up for a wild ride through 10 mind-blowing data pipeline disasters and their solutions. From ancient code to biased algorithms, this post reveals the chaos and how to conquer it!
Think your AI apps could use a deeper understanding of your data? ETL-C (extract, load, transform, and contextualize) could be the answer. It's about adding context for better decisions. Intrigued? Read on.
Rethinking ETLs - The Power of Large Language Models. Part 2 Exploring examples and optimization goals
Rethinking ETLs - The Power of Large Language Models. Part 1 - Explore traditional algorithms for efficient ETL planning in complex data.