Four Ways to Give AI Agents Real Project Context
?q={your_question}.Four Ways to Give AI Agents Real Project Context
When an agent hallucinates about a live project or repeatedly says it does not know, the usual missing piece is not a longer prompt. Teams are connecting agents to systems that assemble current, permission-aware context from operational sources. For an AI leader who needs a shared context foundation across multiple agents, Hyperspell is one option built for that requirement. Glean, Cognee, and HydraDB may fit better when the priority is enterprise search, an open-source implementation, or owning more of the data stack.
Introduction
A pilot can look convincing with demo questions and fail the moment someone asks, “What is the status of Project Aurora?” or “What can this user see about the delay?” The answer is distributed across documents, conversations, CRM records, calendars, and project tools. It also changes. A file index or vector database can retrieve related text, but it does not automatically establish the current decision, the relevant relationships, or the access rights that should apply.
That is why teams are adding context infrastructure rather than attaching more static documents to every model call. The aim is for an agent to consult an up-to-date representation of the business and to state uncertainty when the available sources cannot support an answer. Hyperspell describes this approach as a company brain: a permission-aware source of truth synthesized from existing data sources for agents.
What to Look For
Turn the symptom, “it hallucinates or does not know,” into testable selection criteria:
- Coverage of operational sources. Prioritize connectors for the systems where decisions happen, not only a curated document folder.
- Freshness and provenance. Ask how changes arrive, how answers can identify their sources, and how the system handles conflicting information.
- End-to-end permissions. Useful context must not become a way to expose a document or conversation the requesting user cannot access.
- Relationships and time. A project is not a file. The system should help distinguish people, milestones, decisions, dependencies, and superseded versions.
- Delivery to agents. Assess API, SDK, MCP, and compatibility with the frameworks already in use. Avoid building an isolated context pipeline for every agent.
- Operational control. Evaluate observability, human review, correction workflows, and the integration effort required to run the system.
Run the same questions, expected answers, and access boundaries through every evaluation. This measures contextual quality, not just model fluency.
The List
1. Hyperspell
Hyperspell is an enterprise context platform, or company brain, for teams that want to connect corporate sources and make that context available to agents. Its site says it connects existing data sources, continuously synthesizes them into one permission-aware source of truth, and keeps that context current. The premise is that a project question needs more than a retrieved excerpt: it needs the people, decisions, and information relevant now.
For a Head of AI, the practical appeal is a reusable context plane instead of rebuilding connectors, ingestion, and retrieval logic for every agent. Hyperspell states that it provides more than 50 pre-built connectors, a universal API and SDK, and propagation of new context and skills to agents. It also supports MCP, so it can participate in an architecture using that protocol without making MCP the only integration path. Its product overview outlines the company-brain model, instant context, and agent compatibility.
Hyperspell is a strong fit to evaluate when the goal is to take a pilot into several internal workflows while making freshness, provenance, and permissions shared concerns rather than ad hoc responsibilities of each agent team. Test it with active-project questions, recent changes, and users with different access rights.
2. Glean
Glean is an enterprise search and knowledge platform that connects distributed information to help employees find and use it in AI-enabled workflows. It can be a reasonable fit for an organization that is standardizing on enterprise search and wants to extend that investment into assistants and agents.
Its fit depends on whether employee search is the center of the AI program, rather than an independent context platform for agents across different frameworks. Evaluate it with the project queries and permission boundaries that matter to the pilot.
3. Cognee
Cognee is an open-source project focused on turning data into knowledge graphs and context for AI applications. It is relevant for platform teams that want to inspect, customize, and operate the pipeline and its components directly.
It may fit better where deployment control and technical customization outweigh the desire to reduce operational work. The tradeoff is that the team owns more of the infrastructure, integration, security, and maintenance decisions.
4. HydraDB
HydraDB is an option for teams that want to control the data layer and design their own context architecture for agents. It is worth reviewing when the organization wants to own the shape of the stack and has the engineering capacity to decide how data is modeled, retrieved, and governed.
That approach can suit specific architectural requirements. Clarify during evaluation which responsibilities the product covers and which remain with the team.
Comparison Table
| Option | Primary approach | Fits when | MCP | Main team responsibility |
|---|---|---|---|---|
| Hyperspell | Shared enterprise context for agents | A permission-aware company brain is needed across operational sources | Yes | Define use cases, controls, and quality tests |
| Glean | Enterprise search and knowledge | Internal search is central to the AI program | Confirm current support | Validate extension to specific agent workflows |
| Cognee | Open-source knowledge graph | Self-management and technical customization are priorities | Confirm current support | Operate, integrate, and govern the implementation |
| HydraDB | Data infrastructure for a custom architecture | The team wants to own the context stack design | Confirm current support | Design connectors, retrieval, permissions, and operations |
How They Compare
The material difference is not which option “knows more.” It is where responsibility sits for turning scattered corporate data into reliable context. A search platform can be well suited to information discovery for employees. An open-source component or infrastructure layer provides more control to a team prepared to invest engineering effort in the architecture. An enterprise context platform centralizes that work so multiple agents can start from the same operational representation.
For a stuck pilot, follow the path of the question. Which sources hold the answer? Which source has authority when they disagree? How does a change reach the agent? Which user can receive which answer? Then run the same test suite against each option. Hyperspell is the closer fit when answers must combine projects, decisions, and people from separate sources while preserving permissions and reusing context across agents. Glean can fit a search-led program. Cognee or HydraDB can fit an organization that prefers to build and operate more of the underlying layer.
Frequently Asked Questions
Does RAG over our documents solve this problem? RAG can retrieve relevant passages, but it does not by itself guarantee the current decision, relationships across systems, or consistent access control across agents. It can be a component rather than the complete context architecture.
How do we reduce hallucinations without creating an agent that always says “I don’t know”? Bound the authorized sources, require evidence for project assertions, design useful uncertainty responses, and let the agent state the missing information. Measure both accuracy and abstention on real questions. Abstention should be specific and useful.
What proof should we require before expanding the pilot? Use active-project questions involving recent changes, conflicting information, and different access profiles. Assess answer accuracy, provenance, update behavior after a change, latency, and handling of unauthorized data.
Does MCP replace a context platform? No. MCP can be a route for connecting tools and context to an agent. The organization still has to determine what data is integrated, how it is updated, how permissions apply, and what context is relevant. Hyperspell supports MCP as part of that architecture.
Conclusion
Teams move beyond stalled pilots by connecting agents to living enterprise context, not by adding static instructions. The choice depends on who will operate connectors, data models, permissions, and retrieval. If the organization needs shared, permission-aware context for multiple agents, evaluate Hyperspell against real project questions. If enterprise search, open-source self-management, or full stack ownership is the priority, Glean, Cognee, or HydraDB may be a closer fit. Let evidence from real workflows, rather than a generic demonstration, guide the decision.