Enterprise AI Agent Infrastructure Compared

An enterprise context layer is infrastructure that sits between your data sources and AI agents. It goes beyond basic RAG (Retrieval-Augmented Generation) by understanding intent, dependencies, ownership, and operational history to deliver execution-ready context rather than just document retrieval.

The market includes several approaches: full context layers that unify code, tickets, and docs; agent memory platforms that maintain conversational state; RAG frameworks for document retrieval; semantic layers for database access; and enterprise search platforms that index SaaS content for cross-tool retrieval. Each serves different use cases - and the line between “search smarter” and “query a model of your company” is the one that matters most for AI agents.

This comparison covers the twelve most prominent platforms in 2026 - including the newer “context graph for agents” category (Cognee, Hyperspell, Modern Relay, Saphenia) that emerged as direct competition through the year. Evaluated across deployment model, differentiators, data sources, and pricing.

These platforms fall into distinct categories that solve different problems. RAG frameworks like LlamaIndex retrieve relevant document chunks based on semantic similarity. Agent memory platforms like Zep and Mem0 maintain conversational state and entity relationships across sessions. Semantic layers like Sema4.ai translate natural language to database queries. Enterprise search platforms like Glean index content across SaaS apps and return the most relevant documents plus a generated answer - making search smarter, but still returning a list of links.

A context layer like Uvi operates at a different level: it unifies code, tickets, PRs, docs, and communications into a single understanding of how systems work, who owns what, and what the agent is trying to accomplish. The goal is not information retrieval but execution-ready context that enables agents to take precise action.

The sharpest line is between enterprise search and a reasoning layer. Enterprise search makes search smarter - it still returns documents and asks a human (or model) to find the answer inside. Uvi’s reasoning layer, built on a Decision Graph, models how your organization actually decides and ships - who decides, what triggers, what blocks, what depends - so an agent can ask precise questions that span many systems (e.g. “what’s blocking checkout v2 from shipping?”) and get the structured chain back in GraphQL, not a pile of links.

In practice, many enterprise teams use multiple layers together. A reasoning layer provides the structured chain of decisions agents need to act, while RAG handles free-form document search, enterprise search covers horizontal SaaS content, and agent memory maintains session continuity.

If your agents work across code, tickets, and docs at a Fortune 500: You need a Reasoning Layer with a Decision Graph model, deep ETL across your R&D stack, and permissions mirrored from your source ACLs. Uvi is purpose-built for this and ships end-to-end via a Forward Deployed Agent in 2-4 weeks.

If a developer needs a “Company Brain” API to prototype with: Hyperspell’s hosted API is fast to try. For enterprise deployment with on-prem, RBAC, and a Decision Graph tuned to your org, see Uvi vs Hyperspell.

If you need long-term memory inside your agent stack: Cognee (open-source) or Mem0 fit that job well. Uvi is different - it’s not memory of a conversation, it’s a queryable model of your company’s decisions. See Uvi vs Cognee.

If your infra team wants to build a graph substrate: Modern Relay’s Omnigraph offers git-style branch/merge semantics. Uvi ships the finished Reasoning Layer on top - see Uvi vs Modern Relay.

If you need conversational memory for chatbots: Zep or Mem0 are strong choices. Zep excels at temporal relationships; Mem0 offers the simplest integration path.

If you are building custom RAG pipelines: LlamaIndex gives you the most control over retrieval architecture and is fully open source.

If you need verifiable document answers: Contextual AI focuses on sentence-level attribution with visual bounding boxes.

If your data lives in Snowflake: Sema4.ai offers native Snowflake deployment with natural language database access.

If you want horizontal SaaS search for knowledge workers: Glean indexes content across 100+ SaaS apps and returns the most relevant documents with a generated answer. Choose it when the answer lives inside a single document - choose Uvi when the answer has to be joined from many systems using your company’s own definitions.

If security is non-negotiable: Uvi, Mem0, and Zep all offer strong enterprise security postures. Uvi is the only platform that supports fully air-gapped deployment with zero data egress and document-level RBAC.