Nexla Blog

Agentic AI

Agentic AI refers to autonomous, goal-driven AI systems that can plan, take actions, and use tools to complete multi-step tasks, rather than simply responding to a single prompt.

Nexla gives agentic AI systems governed, real-time access to enterprise data and tools via MCP, Agentic RAG, and Helix, so agents can act reliably instead of guessing from incomplete context.

Agentic Data Integration Announcement

Nexla Earns the AWS AI Software Competency in Agentic AI Tools

Nexla is now an AWS AI Software Competency Partner in Agentic AI Tools, and available to buy through AWS Marketplace.

By Jayashree Rajan

Nov 3, 2026

Nexla Blog: Which ETL Tool Is in Demand in 2026?

ETL/ELT Guide

Which ETL Tool Is in Demand in 2026? Why Agentic, Metadata-Driven Platforms Are Replacing Fixed-Connector ELT

The ETL tool in demand in 2026 is the one that gets data ready for AI. Compare Fivetran, Airbyte, Nexla, and more by workload.

By Jayashree Rajan

Oct 16, 2026

MCP Announcement

Nexla and Snowflake®: Connecting 1,000+ Enterprise Systems to Cortex Agents and the AI Data Cloud

Nexla extends the Snowflake AI Data Cloud to more of the enterprise, with connectivity to 1,000+ systems, delivered as data pipelines, zero-copy access, and task-specific MCP servers for Snowflake agents.

By Jayashree Rajan

Oct 14, 2026

Nexla Blog: Context Layer Series: Context Is Enterprise IP

Agentic Data Integration Context Engineering

Context Is Enterprise IP: Why AI Context Needs Its Own Security Model

Context is concentrated know-how, often more sensitive than the data it describes. Why it needs task-specific delivery, two identities per call, and lineage.

By Saket Saurabh

Oct 12, 2026

Nexla Blog: How Task-Specific MCP Helped Us More Than Just Connecting Everything Into LLMs

MCP Use Case

How Task-Specific MCP Helped vs. Just Connecting Every System Into LLMs

One LLM chat can’t know every system. Task-specific MCP servers scope data to each job, so answers are accurate and traceable.

By Jayashree Rajan

Sep 24, 2026

Nexla Blog: Context Layer Series: Building the Context Engine

Agentic Data Integration Context Engineering

The Context Engine: How Enterprises Should Gather, Organize, and Retrieve Context for AI

How a context engine grounds, assembles and delivers context by task: 7 responsibilities, a task model, typed tools over MCP, and lineage you can inspect.

By Saket Saurabh

Sep 24, 2026

Agentic Data Integration Context Engineering

Don’t Turn Context Into the Next Data Swamp

Indexing everything for AI repeats the data lake mistake. Why useful enterprise context is task-specific, and four questions to ask before you fund it.

By Saket Saurabh

Sep 13, 2026

Agentic Data Integration Context Engineering

The Context Layer for AI Agents: Definition, Five Capabilities, and How It Works

What a context layer for AI agents is, the five capabilities that separate one from relabeled ETL, how it works at runtime, and a test for each capability you can run against any vendor.

By Niket Sourabh

Sep 3, 2026

Nexla Blog: Tools to Build a Context Layer From Multiple Data Sources

Agentic Data Integration Context Engineering

Tools to Build a Context Layer From Multiple Data Sources: A Buyer’s Framework

Connectors are necessary and insufficient. A provider-blind buyer’s framework for building a context layer from multiple data sources: five capabilities beyond ingestion, a scoring rubric, TCO per source, where CDC fits, and a reference architecture.