#ai-agents
9 articles
-
Snowflake and Databricks Converged on AI Agents in 2026. Here Is What Changes for Data Engineers
Both platforms now ship agents as governed objects, managed MCP servers, AI gateways and SQL extraction functions. The work moving to data engineers is context, permissions and cost control.
-
The Harness Is Everything
What Cursor, Claude Code, and Perplexity actually built -- and why the model was never the real product.
-
The Last Generation of Data Engineers?
How agentic platforms are quietly making the pipeline-builder's job description obsolete -- and what survives the transition.
-
AI Engineer vs Data Engineer vs MLE: Who Actually Ships Agentic Systems?
Org charts are lying to you. The job title says 'AI Engineer' but the work varies wildly. Here's the honest picture of who actually builds and ships agentic systems in 2026.
-
Your Data Stack Wasn't Built for This: Architecting for AI Agents
DuckDB has an MCP server. Databricks bakes LLM functions into ETL. AI agents are becoming the most demanding query clients your infrastructure has ever seen.
-
Build a RAG System Without Embeddings or Vector Databases
PageIndex turns documents into navigable trees. An LLM reasons through the hierarchy to find answers — no embeddings, no similarity search, just structured retrieval.
-
The AI Doesn't Need to Read Your Codebase. It Needs a Map.
Context Hub, Code Review Graph, and the emerging discipline of giving AI agents less to make them smarter.
-
Databricks Agent Bricks Is Quietly Changing How Data Engineers Work
Describe the task. Connect your data. Let the platform handle the rest. That is the promise of Agent Bricks — and for a specific, important set of data engineering problems, it is actually delivering on it.
-
Claude Code Puts an AI Agent in Your Terminal — And It Actually Works
Anthropic's agentic CLI reads your codebase, edits files, runs commands, and commits changes. No IDE plugin. No web UI. Just a terminal that understands what you're building.