Every enterprise AI agent project we've watched has failed for the same boring reason. Not because the models were weak — because the models were context-starved.
We'd spend six weeks connecting an agent to Salesforce, Slack, Snowflake, and a document store. The agent would come back confident and wrong. It had no idea that "John Smith" in the CRM was the same person as "jsmith@acme.com" in Slack. It couldn't reconcile "$42M" in the deck with "$41.8M" in the warehouse. It'd retrieve 40 documents and put the wrong one first.
The bottleneck was never the LLM. It was that nothing in the stack actually understood what it was pulling from.
This is what we call the context-starvation problem: AI agents that are smart enough to reason but starved of the organizational context they need to reason correctly. The failure mode isn't obvious — the agent doesn't crash or return an error. It returns a confident, well-formatted answer that happens to be wrong. The user either catches it (best case) or makes a decision based on it (worst case).
The usual fix — stand up a vector database, dump everything in, call it RAG — solves retrieval but not understanding. RAG can find documents that mention "Acme Corp." It cannot tell the agent that "Acme Corp" in Salesforce, "acme" in Jira, "ACME Corporation" in the contract repository, and "Acme Inc." in Slack are all the same company. It cannot reconcile conflicting data points from different sources. It cannot rank results by actual business relevance rather than cosine similarity.
This is why we're building mantle. Not as another connector, not as another vector store, but as a semantic context layer — a reasoning layer that sits between raw data sources and the AI agent. mantle reads your data in place (zero-copy, no ETL), resolves entities across every connected source, walks a knowledge graph to find related records, scores everything for freshness and confidence, and delivers pre-digested context to the agent via the Model Context Protocol.
The key architectural bet is that entity resolution should be a first-class primitive in the retrieval stack, not a post-hoc cleanup step. When "John Smith" in six different systems collapses into one node with a confidence score, every downstream query becomes dramatically more accurate. The agent isn't reasoning over six fragments — it's reasoning over one resolved truth.
We're pre-release. No paying customers yet. Connectors rolling out one at a time. We'd rather ship a solid Salesforce + Slack + Snowflake loop than claim a number that doesn't hold up. If you've hit this problem in your own agent stack, we'd love to hear about it — the failure modes are remarkably consistent across industries, and understanding them is how we decide what to build next.
Join the waitlist at mantleai.dev to follow along.