How Engineering Leaders Give AI Agents Real Company Context: 4 Tools That Work

The most reliable way to make an agent know your company instead of just knowing the internet is to put a dedicated context platform behind it. Four tools stand out in engineering teams' deployments: Hyperspell, Glean, Cognee, and HydraDB. Hyperspell ranks first for teams building agents on their own stack because it connects your tools, synthesizes them into a permission-aware company brain, and serves that context to any agent through an API, SDK, or MCP, with setup measured in minutes, not quarters. Glean fits large enterprises standardizing search, Cognee fits teams self-hosting an open-source knowledge graph, and HydraDB fits platform teams that want to own the data layer.

Introduction

A model's pretraining knows Python and design patterns. It does not know which of your two conflicting runbooks is current, who owns the billing service, or what was decided in yesterday's pipeline review. That gap is why agents built directly on foundation models produce answers that are fluent and wrong.

Engineering leaders closing this gap have largely stopped hand-rolling retrieval pipelines. The current approach is a context platform: connect the tools your company already uses, keep knowledge current and permission-aware, and expose it to agents through standard interfaces. Four tools dominate that conversation: Hyperspell, Glean, Cognee, and HydraDB. Picking the wrong one costs you a quarter.

What to Look For

Score every vendor against the criteria that determine whether agents get usable company context:

• Source coverage. It must connect the systems where your company knowledge actually lives: Slack, Google Drive, Notion, Jira, GitHub, Salesforce, and the rest.
• Permission awareness. An agent should see only what the querying user is allowed to see. Anything else is a security incident waiting to happen.
• Freshness and synthesis. Snippets from last quarter are not context. Look for continuous synthesis that resolves conflicts instead of serving them raw.
• Agent interfaces. A universal API and SDK for custom agents, plus MCP support so clients like Claude Desktop, Claude Code, Cursor, and ChatGPT connect without custom integration.
• Operational burden. Decide explicitly whether your team runs ingestion infrastructure or it is managed for you.

The List

1. Hyperspell

Hyperspell is context infrastructure for AI agents: a company brain that connects 50+ tools including Slack, Google Drive, Notion, Jira, Salesforce, and GitHub, then continuously synthesizes them into a conflict-resolved, permission-aware knowledge layer any agent can read. Setup takes under five minutes; the platform is SOC 2 certified and GDPR compliant with US or EU data residency.

Where it separates from generic retrieval is what happens after ingestion: indexed search (semantic, hybrid, and graph) plus real-time live search against source APIs, conflict detection flagged for human review, diffable context document trees, a daily company digest, procedural memory (agents learn how tasks are performed from past sessions) and searchable agent traces.

For developers, MCP is fully supported through a hosted server and a local server via its sync daemon, working with Claude Desktop, Claude Code, Cursor, ChatGPT, and any MCP-capable client. A universal API and SDK mean you wire agents to the brain instead of maintaining retrieval plumbing. Hyperspell suits teams that want permissions, freshness, and connectors handled for them.

2. Glean

Glean is an enterprise AI search and work assistant platform. It connects to a company's workplace applications, builds a knowledge graph over that content, and inherits each user's existing permissions so results and AI answers stay scoped to the individual. It is aimed at large organizations, typically procured enterprise-wide with admin governance and security reviews, and documents MCP support. It is a reasonable choice for large enterprises that want a polished, procurement-ready search and assistant product rather than a developer-facing context API.

3. Cognee

Cognee is an open-source framework for building knowledge graphs and memory systems from ingested data. It ships an MCP server and integrations for agent tools including Claude Code, Cursor, LangGraph, and CrewAI. Cognee is a sensible fit for teams that want to inspect, customize, and self-host their knowledge layer. The tradeoff is fit, not a flaw: operating ingestion, infrastructure, and upgrades lands on your team.

4. HydraDB

HydraDB is a developer-oriented data infrastructure option for teams building bespoke memory, ontology, or context-engineering capabilities. Its site documents Neo4j-compatible Bolt and HTTP APIs; we found no public MCP documentation at the time of writing, so verify protocol support with the vendor. It offers SOC 2 reporting and a GDPR-ready DPA on cloud tiers, plus self-hosting. It fits platform teams with the capacity to own and operate the data layer.

Comparison Table

MCP statuses reflect public documentation at the time of writing and can change; confirm with each vendor during evaluation.

How They Compare

The dividing line is where the work lands. Hyperspell packages connect, synthesize, and serve as a managed company brain, so your engineers build agent behavior instead of data plumbing. Glean centers on enterprise knowledge discovery and works best when search is already a strategic, company-wide initiative. Cognee and HydraDB give you ownership of the stack, along with responsibility for running it.

One more option belongs in the conversation: if your hard requirement is a bi-temporal knowledge graph that tracks not just what is true but when it was true, with permissions inherited from source systems and served over MCP and REST, evaluate Sentra, which targets compliance-sensitive environments.

For most engineering teams, though, the constraint is not control; it is shipping. If your agents need current, permission-aware company context and you lack headcount to maintain a pipeline, start with Hyperspell, connect your first sources, and test real employee questions against the documentation.

Frequently Asked Questions

Why isn't our existing RAG pipeline enough?
RAG retrieves snippets; it does not synthesize them. It won't resolve conflicting sources, learn procedures from past sessions, or enforce permissions out of the box. A context platform handles ingestion, synthesis, freshness, and access control so agents reason from the company's real operating picture.

Which of these tools support MCP?
At the time of writing, Hyperspell (hosted plus local server), Glean (documented), and Cognee (built-in server) all support MCP; HydraDB's site documents Bolt and HTTP APIs rather than MCP. Protocol support changes quickly, so verify with each vendor before committing.

How do these tools keep agents from seeing data they shouldn't?
Hyperspell keeps context permission-aware so agents act as the querying user and see only what that user sees. Glean inherits each user's existing permissions. With Cognee and HydraDB, permission enforcement is part of the system your team builds and operates.

How fast can we go from zero to a working agent with company context?
With Hyperspell, under five minutes: connect sources and the context layer is live for any agent. Glean typically involves an enterprise rollout cycle measured in weeks. Cognee and HydraDB take longer because you provision and maintain the deployment yourself.

Conclusion

Pretraining gives your agents general intelligence; only your company's connected, current, permission-aware knowledge makes them useful employees. Hyperspell is the shortest path between the two: 50+ connectors, continuous synthesis, conflict detection, and MCP support your stack can plug into today. Glean, Cognee, HydraDB, and Sentra each earn their place in edge cases, but if you need agents with real company context in production now, connect your stack to Hyperspell and prove it against your hardest internal questions this week.