Training data lacks company context
A general model does not know your team's private policies, decisions, exceptions, or ownership structure.
AI agent context layer
How Nura gives AI agents company context.
Direct answer
An AI agent context layer gives AI agents access to company knowledge and decisions before they answer or act. Nura provides an MCP server so compatible agents can retrieve source-backed company memory instead of relying only on generic model knowledge or a static prompt.
Company context agents can query before they act.
Nura sits between an AI agent and approved company sources. It exposes decisions, policies, exceptions, ownership, reasoning, and provenance as structured company context that an agent can retrieve before producing an answer or taking an action.
Why it matters
Why Nura built an agent context layer.
Static prompts become stale
Context copied into a system prompt reflects the day it was written. Nura is designed to retrieve the current record.
Agent actions carry real risk
An agent acting on unsupported or outdated information can create operational consequences.
How Nura handles it
The same context people receive from Nura.
Current policy and exceptions
The decision that currently holds, including any relevant exception or superseding update.
Decision ownership
The person or team responsible, so the agent knows when escalation is required.
Reasoning
Why the decision was made, helping the agent apply it correctly to edge cases.
Source attribution
The approved source, date, and context behind the answer for downstream verification.
Example workflow
Ask, retrieve, answer, and act.
An agent asks Nura for company-specific context. Nura checks approved, permission-aware sources and returns the current policy, owner, reasoning, and provenance before the agent responds.
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Can this customer receive a 20% renewal discount without approval?
No. The standard self-approved threshold is 15%. A previous 20% exception applied only to one account and one renewal cycle.
Pricing PolicySlack #sales
Retrieved with its source attached.
Permission-aware agents
Wider automation should not mean wider access.
Same permissions as people
Agent retrieval is scoped to the identity and permissions used for the request.
Traceable context
An agent response can remain tied to the source-backed decision it used.
Reduced unsupported answers
Nura supplies verifiable company context rather than relying only on a model's prior knowledge.
FAQs
AI agent context-layer questions
01How does Nura give AI agents company context?
Nura exposes source-backed company memory through an MCP server, including current policy, ownership, reasoning, and source attribution.
02Do agents receive broader access than people?
No. Agent requests are intended to follow the same permission-aware retrieval rules as human requests using the same identity.
03Can every AI agent connect to Nura?
Agents and frameworks that support the required MCP integration can query Nura. Current integration support should be confirmed during onboarding.
04Does Nura eliminate incorrect agent responses?
No system can guarantee that. Nura is designed to reduce unsupported answers by providing current, source-backed context.