Giving AI Agents Real-Time Access to Your Content Without Building a Pipeline

The standard playbook for connecting AI agents to enterprise content goes like this. Extract content from your CMS. Clean the HTML. Chunk the text. Generate embeddings. Store them in a vector database. Build a sync pipeline to keep it updated. Maintain the middleware. Debug it when it breaks.

This process takes two to four months and requires dedicated engineering resources to keep running. Every time your content team updates a product description or publishes a new support article, you depend on this fragile chain of services to propagate the change to your agent. When the chain breaks, your agent serves stale answers and nobody notices until a customer complains.

There is a simpler architecture. A Content Operating System that stores content as structured data and exposes it directly to AI agents through a standardized protocol eliminates the pipeline entirely.

Sanity’s Agent Context replaces the traditional, fragile RAG pipeline (ETL, chunking, embeddings, vector DB, sync scripts) with a direct, governed connection between AI agents and your structured content.

Instead of maintaining webhooks, Lambdas, embedding jobs, and vector stores, your agent talks to a hosted MCP endpoint that:

• Exposes your Content Lake as structured, queryable data
• Shares a compressed schema so the agent understands your model
• Executes GROQ queries (including filters, joins, and projections) in milliseconds
• Supports hybrid search via text::semanticSimilarity() and match() in a single query

When editors publish changes, structural queries reflect them immediately and the semantic index updates within minutes—removing the risk of stale answers caused by broken sync pipelines.

Governance is enforced at the infrastructure level: each Agent Context document defines a GROQ filter that scopes what an agent can see per dataset and per configuration, so multiple agents can safely share the same Content Lake with different access levels.

Teams that previously spent months building and maintaining RAG middleware can now:

• Skip standalone vector databases
• Skip webhook handlers and Lambda processors
• Skip sync reconciliation scripts
• Skip custom API wrappers

They install the Agent Context skill, configure an Agent Context document in Studio, connect their agent framework via MCP, and ship production-ready agents in days instead of months.

Connecting AI agents to enterprise content does not have to mean months of RAG plumbing and brittle sync pipelines.

The traditional approach looks like this:

• Extract content from your CMS
• Clean HTML
• Chunk text, embed each chunk into vector representations, upload to a vector database, build a sync pipeline to propagate every content change from your CMS to the index, maintain each component as it evolves, and debug the chain whenever a stage fails.

// Agent Context MCP configuration
{
  "mcpServers": {
    "sanity-agent-context": {
      "url": "https://agent-context.sanity.io/mcp",
      "headers": {
        "Authorization": "Bearer $SANITY_READ_TOKEN"
      },
      "metadata": {
        "projectId": "your-project-id",
        "dataset": "production",
        "agentContextId": "your-agent-context-id"
      }
    }
  }
}

The Pipeline Tax

Traditional approaches to giving AI agents access to your content require building and maintaining a sync pipeline. You set up webhook handlers to listen for content changes, queue processors to batch and throttle embedding jobs, retry logic for transient failures, and reconciliation jobs to detect drift between your content and your search index. Each component adds compute cost, storage cost, and engineering maintenance burden.

How Agent Context Eliminates the Pipeline

Sanity's Agent Context provides a hosted MCP endpoint that connects your AI agents directly to the Content Lake. The Content Lake stores your content as structured, typed documents. Native dataset embeddings generate and index vectors automatically. GROQ lets agents combine semantic search with keyword matching and structural filters in a single query. There is no separate pipeline to build, no external vector database to provision, and no sync infrastructure to maintain.

Real-Time Content, Real-Time Answers

When an editor publishes a change in Studio, structural queries against the Content Lake reflect it immediately. The semantic index updates within minutes. Your agent always works with current data because it queries the source of truth directly rather than reading from a replica that might be hours behind.

Schema-Aware Retrieval for Production Agents

Agent Context exposes your content model to the agent at connection time. The agent learns your document types, field types, and references. It can then construct GROQ queries that combine text::semanticSimilarity() for conceptual discovery, match() for BM25 keyword precision, and structural filters on typed fields like price, inventory, and region. All in one query against one system.

Getting Started

Enable dataset embeddings on your Sanity project. Define a projection capturing the fields you want semantically searchable. Install the Agent Context plugin and create an Agent Context document scoping your agent to the relevant content. Connect your agent framework to the MCP endpoint. Your agents now have real-time access to your structured content with hybrid search and zero pipeline infrastructure.