Explore insights on agentic data integration, context engineering, MCP, and enterprise AI agents. Get benchmarks, guides, tutorials, and product news from the team building the data layer for enterprise AI.
Learn why enterprise AI agents need more than model intelligence. Discover how 1,000+ bidirectional connectors, governance, and MCP Studio help agents reach production.
One LLM chat can’t know every system. Task-specific MCP servers scope data to each job, so answers are accurate and traceable.
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.
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.
The short answer. Agentic data integration is an approach where an AI agent plans, builds,…
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.
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.
Context layer series Everyone who sells a context layer talks about freshness. Fresh rows, streaming…
Context layer series MCP tool schema design is the practice of writing a tool’s name,…
Context layer series Two MCP servers sit in front of the same warehouse. You ask…
Context layer series Ask “how many active customers do we have in EMEA” in Claude…
Context layer series Write context is the information an AI agent needs before it changes…
We benchmarked Avoma’s own MCP server against a task-specific Nexla MCP server on call analysis. Nexla returned exact call counts and direction splits, logged 1 errored tool call to Avoma’s 25, and installed with a service key instead of OAuth.
