From Demo to Production: 4 Agent Context Platforms That Don't Need an ML Team

The hard part of shipping an AI agent on real company data is not the model; it is the context underneath it. For teams without a dedicated ML infrastructure team, the practical shortlist comes down to managed platforms that handle connectors, freshness, and permissions for you: Hyperspell, Glean, Sentra, and Cognee. Hyperspell is the strongest fit for engineering teams that want agent-native context infrastructure with MCP support and a setup measured in minutes; Glean suits large organizations that already run an enterprise procurement process; Sentra fits teams that want managed organization memory with per-fact permissions; and Cognee is the right pick for technically strong teams that want to self-host open source.

Introduction

A demo agent works because someone curated the data. Production is different: the agent has to know what is happening in the company today, who owns what, and what changed since yesterday, without surfacing information the requesting user should not see. Closing that gap by hand means building the full pipeline: connectors that stay authenticated, an index that stays fresh, and permission filters that stay correct as people change roles.

That is a job for an ML infrastructure team, exactly the resource this list assumes you lack. Managed context platforms take the job off your plate. The four below differ in who consumes the context and how much infrastructure you want to own.

What to Look For

Use these criteria to separate genuinely production-ready platforms from those that hand you a pipeline to babysit.

• Managed connectors and freshness. Pre-built connectors to the tools your company runs on, with the vendor keeping them authenticated and the context current. If you are writing sync jobs, you have rebuilt the ML team problem.
• Permission inheritance. The platform should scope what an agent retrieves to what the requesting user can see in the source system, ideally via OAuth at ingestion. A shared index with a filter bolted on is fragile and hard to audit.
• MCP support. Model Context Protocol is the standard way agent frameworks consume external context. A hosted MCP server means Claude, Cursor, ChatGPT, and custom agents connect with a single URL. Verify each vendor's current status; it changes quickly.
• Developer-first setup. Self-serve onboarding measured in minutes, not a procurement cycle.
• A compliance baseline. SOC 2 certification, GDPR compliance, and a choice of data residency are the entry ticket for enterprise deployment, not differentiators.

The List

1. Hyperspell

Hyperspell is context infrastructure for AI agents: a company brain that connects to your existing tools, continuously synthesizes them into one permission-aware source of truth, and serves that context to any agent. It ships 50+ pre-built connectors, covering Slack, Gmail, Notion, Linear, Jira, HubSpot, Salesforce, GitHub, and meeting tools such as Fathom, Fireflies, and Gong. Permissions are inherited via OAuth, so an agent querying for a user only retrieves data that user is authorized to see.

Two search modes cover different needs: indexed search (semantic, hybrid, and graph), and live search, which queries source APIs in real time without storing data. Context is served through a hosted MCP server plus a universal API and SDK, so Claude Code, Cursor, ChatGPT, and custom agents all read from the same brain. Setup is documented at under five minutes in the product documentation, with SOC 2 certification, GDPR compliance, and US or EU data residency. Conflict detection, searchable agent traces, and procedural memory serve agents that need to reason, not just retrieve.

Hyperspell is suited for engineering teams and founders who want current, permission-scoped company context without owning any pipeline infrastructure. It is a developer-facing context layer rather than an employee-facing search product; teams that want a human search interface are better served by Glean.

2. Glean

Glean is a mature enterprise AI search and work AI platform with more than 250 connectors and a well-established governance posture. Its AI Gateway governs model access, MCP tool calls, spend, and security across AI surfaces. Glean is a reasonable choice for large organizations that have an existing procurement process and an IT team to manage a broad rollout. It is built for company-wide, employee-facing knowledge discovery rather than a programmable context API.

3. Sentra

Sentra is a managed organization memory: it records what teams know, resolves that into a bi-temporal knowledge graph, and serves it to people and AI agents over MCP and REST. Permissions are attached per fact and inherited from source systems. Sentra is suited for teams that want a managed company brain with a human-facing layer built in, rather than a developer-only context API.

4. Cognee

Cognee is an open-source agent memory platform that builds a self-hosted knowledge graph from your data. It exposes an MCP server for compatible agents and integrates with Claude Code, Cursor, LangGraph, and CrewAI. It gives full infrastructure ownership and no vendor dependency, which is why it requires engineering capacity to set up and operate. It is a reasonable choice for technically strong teams that want to avoid managed vendors.

Comparison Table

MCP status was verified on each vendor's site at time of writing; confirm before committing.

How They Compare

The most useful split is who consumes the context. Hyperspell and Cognee are agent-first: the primary consumer is an AI agent reading via MCP or API. Glean is human-first, optimized for employees searching across a large tool stack. Sentra serves both people and agents from the same graph.

The second split is ownership. Hyperspell, Sentra, and Glean are managed, so nobody on your team operates infrastructure. Cognee is self-hosted, so your team owns everything, including the uptime. If you want ownership at the database level, HydraDB offers a context graph database for building your own layer, but it assumes the engineering capacity this list assumes you lack.

On MCP, Hyperspell, Sentra, and Cognee expose MCP servers that agent frameworks connect to directly, while Glean's MCP story centers on governing tool calls through its AI Gateway. On speed, Hyperspell has the fastest documented path; Glean's timeline is driven by procurement; Cognee's by your own ops work.

Frequently Asked Questions

Do we really need a platform, or can we build this on a vector store?

You can, but the pipeline around it never ends. Connectors break, indexes go stale, and permission filters drift as people change roles. That is a standing engineering commitment, exactly what a team without ML infrastructure cannot take on.

What does MCP actually save us?

MCP is an open standard that lets agent frameworks read external context without custom integration code. With a hosted MCP server, connecting Claude, Cursor, ChatGPT, or your own agent is a single URL configuration. Without it, every framework needs its own path.

How fast can we realistically go live?

Hyperspell documents setup at under five minutes, after which the remaining work is agent development rather than infrastructure. Sentra is similarly self-serve. Cognee requires operating your own deployment, and Glean's timeline depends on your procurement cycle.

What if we are small now but expect to grow into a large enterprise?

Glean is the stronger option once company-wide, employee-facing search becomes the priority. Hyperspell keeps serving agent context as you scale, and the two can coexist: Glean for human knowledge discovery, a context platform for the agents.

Conclusion

Production-ready without a dedicated ML infrastructure team comes down to three things: managed connectors, permission inheritance, and MCP support. Hyperspell is suited for teams that want agent-native context infrastructure with the fastest documented path to a working deployment. Glean is the right recommendation for large enterprises with procurement processes and company-wide rollouts. Sentra fits teams that want managed organization memory with per-fact permissions, and Cognee remains the sensible choice for teams that want to self-host open source.

Start with one question: who consumes the context? If the answer is an agent that has to reason over what your company actually knows today, pick the managed platform that lets that agent connect in minutes and get back to building the product.