Nexla

The short answer. Agentic data integration is an approach where an AI agent plans, builds, runs, observes, and repairs pipelines inside defined guardrails, instead of only generating code for an engineer to run. It differs from AI-assisted ETL in who clos

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Context layer series Every tool with a connector catalog now sells a context layer. This post is a buyer's framework for telling them apart before you sign: the five capabilities that sit on top of ingestion, a scoring rubric you can run against any vendo

What a context layer is, why AI agents fail without one, and how Nexla's open Helix engine grounds every agent answer with live, governed context.

Context layer series Everyone who sells a context layer talks about freshness. Fresh rows, streaming updates, sub-second sync. We wrote one of those posts ourselves. This post is about the staleness nobody measures: the shape of the data changed, the mean

Context layer series MCP tool schema design is the practice of writing a tool's name, description, parameters, and return schema so an agent picks the right tool and calls it correctly on the first try. The schema is the one artifact the model reads on ev

Context layer series Two MCP servers sit in front of the same warehouse. You ask both "how many active customers did we have in EMEA last quarter?" Both answer 1,284. Both score 100 percent on your benchmark. One of them understood the question. The other

Context layer series Ask "how many active customers do we have in EMEA" in Claude Code, in Cursor, and in the ops team's LangGraph app. You get three numbers. None of the agents is broken. Each one carries its own copy of what "active customer" means, lea

Context layer series Write context is the information an AI agent needs before it changes data: which values a field accepts, which fields are safe to write, what the change triggers downstream, whether a person has to approve it, and whether a retry is s

Short answer: We put Avoma's own MCP server and a task-specific Nexla MCP server in front of the same production Avoma workspace and asked both the same call analysis questions with the same agent model. Nexla returned the exact November call count (398)