Insights & Engineering
AI agents, data platforms, MCP, RAG pipelines — what we're building and what we're learning.
From Demo to Production: The Agentic AI Patterns That Actually Work
Everyone has an agent demo. Few have agents in production. Here are the architectural patterns separating toy agents from systems that run real workloads.
Read moreDeclarative AI Transformations: Building Data Pipelines Agents Can Understand
How config-driven pipelines and AI transformations let agents autonomously build, execute, and optimize data workflows without writing code.
Read moreAgent-to-Agent Protocols Are Coming. Your Data Layer Isn't Ready.
As A2A, MCP, and ACP mature, AI agents won't just use tools — they'll coordinate with each other. The data infrastructure underneath needs to catch up fast.
Read moreYour Data Pipeline Has an MCP Server. Now What?
Datris Platform ships with a built-in MCP server, turning every pipeline into a tool that AI agents can discover, invoke, and orchestrate autonomously.
Read moreWrite Data Quality Rules in Plain English — and Actually Mean It
Datris lets you define data validation rules in natural language. No regex, no custom code — just describe what valid data looks like and let the AI enforce it.
Read moreWhy Agent-Native Data Platforms Matter
AI agents are becoming autonomous operators of data infrastructure. But most data platforms weren't built for them. Here's why that needs to change — and what agent-native actually means.
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