Blog
Thinking out loud.
Notes from building the context layer for AI agents. Engineering choices, architecture trade-offs, the failure modes we keep seeing in production agent stacks, and how we think about the problem space. Written by the team, unfiltered.
ENGINEERING2026-04-167 min read
The context-starvation problem in enterprise AI agents
Every enterprise AI agent project we've watched has failed for the same boring reason. Not because the models were weak, because they were context-starved.
ReadPRODUCT2026-04-145 min read
Why entity resolution is the missing primitive for AI agents
RAG solved retrieval. Nobody solved understanding. Entity resolution, collapsing the same record across systems into one node, is the part no connector does.
ReadARCHITECTURE2026-04-104 min read
Zero-copy vs ETL: why we don't move your data
Traditional data integration copies your data into a warehouse. mantle reads it in place. Here's why that architectural choice matters for AI agent infrastructure.
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