Agentic lakehouse gateways face prompt injection and exfiltration through query results. A threat model and defenses for the layer in front of data.
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
A threat model for the agentic lakehouse gateway covering prompt injection, exfiltration, and the firewall that reads sentences.
How to build stateless AI tool gateways with FastMCP, the 2026 MCP specification, and Kubernetes, and why statelessness finally makes MCP scale.
The five layers of an agentic lakehouse and where the MCP server sits: storage, catalog, semantic layer, MCP gateway, and agent surface, plus identity, session isolation, and budgets.
Why agentic AI needs a governed semantic layer behind the Model Context Protocol: metric consistency, access control, Apache Ossie for portable definitions, and Apache Polaris for enforcement.
Moving from supply chain dashboards to decision loops with MCP: sense, decide, act, and verify, with typed action tools, idempotency keys, and graduated human approval.
Defending the lakehouse gateway against prompt injection and data exfiltration: per-user identity, no-SQL tool surfaces, volume bounds, and detection in query behavior.
The catalog is the right attachment point for AI agents working against a lakehouse. Here's how to wire the official Polaris MCP Server and add the read path it deliberately leaves out.
An in-depth exploration of gsa mcp servers for open data ai agents
An in-depth exploration of mcp stateless gateways for data agents
A deep dive into custom AI harness architecture: model layers, tool design, context management, permissions, control budgets, persistence, orchestration, and evaluation systems.
The agentic AI protocol stack is solidifying in 2026 — MCP for tools, A2A for agents, WebMCP for the web, OSI for semantics, payments, identity, and security.
What the 2026-07-28 Model Context Protocol Release Candidate means for data platforms and agentic analytics.
A comprehensive comparison of AI agent tooling across Dremio, Snowflake, Databricks, Microsoft Fabric, AWS, Google Cloud, ClickHouse, VeloDB, SpiceAI, Bauplan, and Qlik.
Sampling and Prompts in MCP : Making Agent Workflows Smarter and Safer
Tools in MCP : Giving LLMs the Power to Act
Resources in MCP : Serving Relevant Data Securely to LLMs
Under the Hood : The Architecture of MCP and Its Core Components
Enter the Model Context Protocol (MCP) : The Interoperability Layer for AI Agents
AI Agent Frameworks : Benefits and Limitations
What Are AI Agents : And Why They're the Future of LLM Applications
Boosting LLM Performance : Fine-Tuning, Prompt Engineering, and RAG
How LLMs Work : Embeddings, Vectors, and Context Windows
What Is AI and How It Evolved Into LLMs
The Basics of Building a Basic MCP Server