Insights & Engineering
AI agents, data platforms, MCP, RAG pipelines — what we're building and what we're learning.
Agent Benchmarks Are Missing the Boring Part: Data Readiness
Agent evals reward reasoning and tool use. Most production agents fail somewhere a benchmark never looks: stale data, schema drift, silent dropped rows, no lineage.
Read moreThe Recovery Loop Is the Real Agent Architecture
Agent demos stop at the first tool call. Production systems live or die on what happens after that.
Read moreYour RAG Chunker Is Measuring the Wrong Thing
Document chunkers count characters. Embedding APIs count tokens. The mismatch quietly kills RAG pipelines whenever the content gets dense — tables, JSON, code, numbers, non-Latin text. Here's how to stop the bleeding.
Read moreStop Giving Agents Master Keys
Most agent deployments hand out one broad API key and call it auth. The next step is scoped, capability-bound keys — the API key itself becomes the boundary for what an agent can read, write, and run.
Read moreAn Agent Should Build the Path, Not Just Narrate It
Most AI in data tools is a dressed-up chat box. A real data Agent creates the pipeline, generates the fetcher, handles credentials safely, runs the job, and shows you actual completion — not just a polite summary.
Read moreAI-Generated Taps Turn Data Sources Into Pipelines
Datris taps let agents turn APIs, files, feeds, and databases into scheduled data pipelines without writing custom integration code every time.
Read moreOpenClaw has Dementia. Let's Fix It.
Your agent isn't amnesiac — its retrieval is broken. Run a Datris MCP server next to OpenClaw to give it a real semantic memory layer over the markdown notes you already write.
Read moreAn AI Agent Can Run the Whole Data Loop Now: Taps, Pipelines, and Queries Through MCP
For the first time, an AI agent can stand up its own pipeline, provision its own credentials, build a data tap, run it, watch the data land, and query the result — end to end through MCP, with no human in any of the steps.
Read moreI Stopped Waking Engineers Up at 3 AM. An Agent Lets Them Sleep
AI agents are now good enough at tool use to operate data infrastructure — reroute flows, adjust transformations, quarantine bad records, and resume operations. Autonomous data operations is closer to production-ready than most people think.
Read moreFrom Demo to Production: Building an MCP Control Plane Inside Your Agent Architecture
Most AI agent demos break down the moment someone asks who authorized a tool call. A control plane between your agent and every MCP tool — discovery, identity, policy, observability — is what closes the gap between demo and production.
Read moreValidate Once. Deliver Everywhere.
Modern data pipelines need to validate, transform, and route to many destinations at once — Postgres, object storage, vector databases, and more — driven by AI and a single config.
Read moreMCP Needs a Control Plane. That’s Where the Agent Ecosystem Is Heading.
Model Context Protocol made tool use practical. The next big shift is making MCP manageable at scale with discovery, auth, policy, observability, and data-aware operations built in.
Read moreMulti-Agent Architectures: What Real-World Orchestration Actually Looks Like
Single agents hit a ceiling fast. Organizations shipping multi-agent systems are learning hard lessons about coordination, data handoffs, and why your orchestration layer matters more than your model.
Read moreFrom PDF to Searchable Knowledge Base — Without Writing a Single Line of Code
Datris Platform turns unstructured documents into RAG-ready vector embeddings with zero custom code. Pick your vector DB — Qdrant, pgvector, Chroma, Weaviate, or Milvus — and let config do the rest.
Read moreFrom 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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