AI Orchestrators vs Gateco: Workflow Control Is Not Retrieval Control

A question we hear from platform teams: we already run our agents through an orchestrator inside our own cloud, and it uses that vendor's native graph authorization. Do we still need Gateco? Usually the answer is yes, and the reason is a layer boundary. An orchestrator controls workflow execution. Gateco controls what knowledge an AI is allowed to retrieve. Those are not the same control, and the gap between them is exactly where retrieval-augmented systems leak.

Workflow control and retrieval control are different layers

Orchestrators are good at what they do: routing requests across agents, tools, and steps, managing state, retries, and hand-offs. Native cloud authorization sits alongside them and answers a resource question: can this principal call this service or read this object? That is real authorization, and you should keep it. But it governs the workflow, not the content an agent pulls into its context window. Even when the orchestrator runs in the customer's cloud and uses that vendor's graph authorization, it still governs the workflow layer. Gateco governs the retrieval layer, the exact point where RAG and agents are most likely to leak data.

Cloud authorization protects resources. Gateco protects retrieval results.

IAM and graph authorization answer one question: can this principal access this object or service? That keeps the cloud account safe. Gateco answers a narrower, retrieval-time question: can this user retrieve this specific chunk, document, tenant record, or vector result, right now? A principal can be fully authorized to call the search service and still receive chunks they were never supposed to read. The service call was allowed. The content was not gated.

RAG leakage happens after search, not before

A vector search ranks by semantic similarity, not by permission. It returns the chunks closest to the query, and some of those can be exactly the sensitive ones the user should not see. When the orchestrator's authorization fired, the search had not run yet, so there was nothing chunk-shaped to check. Gateco sits at retrieval time, after the search and before the model, and denies or filters unauthorized chunks before the AI ever sees them. We covered this access surface in depth in the RAG authorization gap.

Graph authorization is not built for embeddings

Embeddings flatten content into vectors. The cloud graph may know the permissions on the original document, but the vector store returns chunks detached from that source. A chunk has no inherent link back to the access control on the file it came from, and similarity search does not carry permissions along for the ride. Metadata filters help, but they break in the ways we described in why metadata filters are not enough. Gateco binds permissions to the things that matter at retrieval time: chunks, principals, tenants, and the retrieval request itself.

One authorization layer across every vector store

Native graph authorization is tied to one vendor's stack. The moment a team adds Pinecone for semantic search, pgvector for an internal tool, Neon or Supabase for a new product, or Qdrant, Vertex AI, Azure AI Search, and OpenSearch for everything else, the per-vendor authorization story fragments. Gateco gives one policy layer across all of them, 12 connectors today, instead of rebuilding and re-proving policies per vendor. We covered the AWS, Azure, and GCP case in multi-cloud RAG governance.

Built for AI security evidence, not just allow and deny

The value is not only the decision. Security teams need evidence: a full audit trail of every retrieval, the policy decision and its reason, tenant isolation, and deny-by-default behavior on error. Gateco logs every retrieval with principal, resource, policy, decision, and timestamp, fails closed when a policy cannot be evaluated, and gives security teams traces they can review. An orchestrator's logs tell you which steps ran. They do not tell you what knowledge each agent was allowed to see.

Orchestrators manage agents. Gateco constrains them.

An orchestrator routes tools, agents, and workflows, and it should. But routing is not restraint. As soon as an agent can call a retrieval tool, it can over-retrieve: pull context that is relevant to the query but off-limits to the user it is acting for. Gateco is the constraint. It binds every retrieval to a resolved principal and a deny-by-default policy, so an agent cannot widen its own access just because the workflow let it ask.

Use both: the workflow layer and the retrieval layer

This is not orchestrator versus Gateco. The clean architecture uses both. The orchestrator runs the workflow, and the cloud's native authorization keeps the account safe. Gateco sits at the retrieval boundary, in front of whichever vector stores the agents query, and enforces who is allowed to know what. They operate at different layers with different enforcement points, so there is no overlap to untangle, only a gap to close.

Native cloud authorization tells the system what a user can access. Gateco makes sure the AI only retrieves what that user is allowed to know. The security page documents the enforcement model, the latency budget, and the audit spec. The for security teams page maps it to SOC 2, ISO 27001, NIST AI RMF, and EU AI Act controls.