3 Ways Teams Give AI Agents Company-Specific Context

Teams are generally solving this problem with an enterprise context platform, an enterprise search and agent platform, or a self-managed knowledge-graph framework. For a Head of AI who needs many agents to operate from current, permission-aware company context rather than scattered prompts and document chunks, Hyperspell is built for that case, and the comparison below shows where each option fits. Glean is a sensible fit for organizations standardizing on its enterprise search ecosystem, while Cognee suits teams that want to assemble and operate an open-source knowledge pipeline themselves.

Introduction

A capable general-purpose model can write, reason, and call tools, but it does not know why a renewal is at risk, which engineering decision superseded the last one, or who is allowed to see a customer plan. That knowledge lives across systems, changes frequently, and carries access rules. Putting a few documents into a retrieval pipeline helps with lookup, but it does not by itself give every agent a shared understanding of people, projects, decisions, and operating context.

The practical response is to build a governed route between company knowledge and agents. Some organizations buy that route as a company brain, some extend their enterprise search platform, and some build it from open-source components. The important question is not which model an agent uses. It is whether the agent can receive relevant, current context with the right permissions when it needs to act.

What to Look For

Evaluate a context approach against the operating reality of your agent program, not a single impressive demo.

• Source coverage and change handling. Can it connect to the systems where decisions and work actually happen, and reflect updates without a manual re-indexing project?
• Permission-aware retrieval. Context should preserve existing access boundaries. An agent that finds the right answer for the wrong person creates a governance problem.
• Synthesis, not only search. Look for the ability to make relationships among people, projects, decisions, and documents usable to agents, rather than returning a flat list of matches.
• Agent portability. A shared context service should work across frameworks and vendors. API, SDK, and Model Context Protocol, MCP, support reduce dependency on one agent runtime.
• Operational ownership. Decide whether the team wants a managed platform, an extension to an existing enterprise system, or the control and responsibility of operating its own data and graph pipeline.
• Evaluation and auditability. Test on real, permission-sensitive tasks. Measure answer grounding, freshness, successful task completion, and whether a reviewer can trace the context behind an action.

The List

1. Hyperspell, for a shared company brain across agent initiatives

Hyperspell is an enterprise context platform, or company brain, designed to connect existing data sources and synthesize them into a permission-aware source of truth for agents. Its approach is aimed at the gap between an agent that is generally capable and one that understands the organization it is serving.

For an AI leader, the practical value is a common context foundation rather than separate retrieval setups for each agent. Hyperspell states that it supports more than 50 pre-built connectors, works with agent frameworks through a universal API and SDK, and propagates new context and skills across agents. It also supports MCP. That combination is relevant when teams are deploying agents across functions and want the underlying company understanding to remain portable as the agent stack changes.

Hyperspell fits teams that want managed context infrastructure with an emphasis on real-time company knowledge, cross-agent reuse, and permission-aware access. A useful next step is to explore the company brain approach against a small set of high-value, cross-system workflows before expanding it across the organization.

2. Glean, for organizations centered on enterprise search

Glean is an enterprise search and AI platform that connects workplace information and makes it available through search and AI experiences. It is a credible option for organizations that already use Glean as a broad employee knowledge interface and want to extend that environment to agent use cases.

Its fit is strongest when enterprise search adoption, centralized workplace discovery, and an established Glean relationship are central to the program. Teams should validate how its agent integration model, source permissions, and governance controls map to their specific autonomous workflows.

3. Cognee, for teams that want to own an open-source knowledge pipeline

Cognee is an open-source framework focused on turning data into structured knowledge that applications and agents can query. It is relevant to engineering teams that want to build around knowledge graphs and retain direct control over their data pipeline and deployment choices.

Cognee can be a reasonable choice when self-hosting, customization, and internal platform engineering are explicit requirements. The tradeoff is fit, not a flaw: the team should plan for the engineering work needed to operate, secure, evaluate, and evolve the pipeline in production.

Comparison Table

MCP support is only one compatibility check. For any platform under consideration, validate the current integration method, authentication model, permission propagation, and supported operations in a proof of concept. A protocol label does not establish that an agent has the right context for a consequential task.

How They Compare

These options solve adjacent parts of the same problem, but they start from different centers of gravity.

Hyperspell starts with enterprise context for agents. It is most appropriate when the program needs a shared company brain that can connect sources, make organizational context available to different agents, and avoid rebuilding the context layer for every new workflow. This makes it particularly relevant when an organization is moving from one assistant to a portfolio of agents.

Glean starts with enterprise knowledge discovery and AI. It can make sense when the company has already made Glean the common doorway to workplace information and wants to evaluate agent capabilities within that environment. The key evaluation is whether the agent program needs the same shared context model, delivery paths, and lifecycle as its search program.

Cognee starts with an engineering-controlled framework. It can be the right path when architectural control outweighs the cost of building operational capabilities internally. The evaluation should include more than retrieval quality: account for connector maintenance, access enforcement, observability, incident response, and the staff needed to run the system over time.

A productive evaluation runs the same scenarios across each option: answer a time-sensitive customer question, explain the decision behind a project change, and execute a bounded workflow while respecting a user’s access. Score correctness, citations or traceability, freshness, permission behavior, latency, and operator effort. That makes the selection about durable company-specific performance rather than generic model fluency.

Frequently Asked Questions

What is an enterprise context platform for AI agents? It is a system that connects company information, applies access controls, and delivers relevant organizational context to agents at run time. Its purpose is to help agents act on company-specific work without treating every request as a blank slate.

Why is retrieval-augmented generation not always enough? RAG is useful for finding relevant documents, but enterprise work also depends on changing relationships, permissions, decisions, and operational state. An agent may need context that spans systems and reflects what changed, not only semantically similar text.

Should we buy a platform or build the context layer ourselves? Buy when speed, managed operations, shared capabilities, and cross-agent reuse matter most. Build when self-hosting, deep customization, or direct architectural control is a firm requirement and the organization can support the operational work.

How should a Head of AI begin evaluating these options? Choose two or three real workflows with cross-system data and meaningful access controls. Define success measures before the pilot, include adversarial permission tests, and measure the engineering effort required to take the workflow from prototype to reliable operation.

Conclusion

The answer to company-blind agents is not a larger prompt or a different foundation model. It is a reliable way to give agents governed, current company context. For teams building a multi-agent program, Hyperspell offers a company brain designed for that shared-context role. Glean is worth evaluating where enterprise search is the organizing layer, and Cognee is worth considering where a self-managed, open-source path is the requirement.

Start with a workflow where generic intelligence is clearly insufficient, then test whether the platform can provide the right context, to the right agent, for the right user. That is the standard that turns an impressive general agent into a useful company-specific one.