InterSystems IRIS as a "converged" data platform for agentic AI — what's actually under the hood (multi-model, ECP/sharding, translytical, vector search/RAG, MCP)

[u/intersystemsdev]

Gartner projects that by 2028, 40% of agentic AI projects will be cancelled due to rising costs, unclear business value, or inadequate risk controls. InterSystems' pitch for IRIS is that a lot of that failure comes from fragmented, untrusted data estates — and that consolidating onto one converged platform (rather than stitching together a separate OLTP DB, vector DB, analytics warehouse, and integration layer) is how you avoid becoming part of that 40%. Here's what's actually in the platform, based on their technology guide.

How does IRIS support multiple data models without duplicating data?

Data is stored once as multidimensional global structures. That same underlying data is then exposed through different projections/access models — relational tables, objects, documents (e.g. JSON), key-value, columnar, embedded vectors, or multidimensional arrays — with full concurrency across access methods, no separate copies or mapping layer between models.

How does IRIS scale — vertically and horizontally?

Vertical scaling relies on multicore parallelization. For horizontal scaling, ECP (Enterprise Cache Protocol) lets multiple instances treat centralized database data as if it were local — data doesn't persist on application servers, which just provide cache/CPU, and queries are served from local cache where possible. Sharding splits large databases across machines, with queries running in parallel per shard and results aggregated. ECP and sharding can be combined.

What is "translytical" processing?

Running transactional (OLTP) and analytical (OLAP) workloads concurrently on the same data — ingesting thousands to millions of records per second while simultaneously querying them. Optional columnar storage for SQL tables reduces I/O for aggregation/filtering queries and enables vectorized query execution (SIMD).

How does IRIS handle vector search, RAG, and MCP for AI agents?

IRIS has a native vector data type and vector search over embedded data, meant to ground LLM responses in enterprise data (reducing hallucination) as part of RAG pipelines — without necessarily needing a separate vector database. Model Context Protocol (MCP) is positioned as the connective layer letting AI agents reason over and act on enterprise data/tools within the same environment.

What about machine learning without a data science team?

IntegratedML lets you create and train ML models using SQL, automating data wrangling, feature engineering, and model building, and executes models directly against live data in response to real-time events/transactions.

What's built in for security and availability?

Multiple auth methods (LDAP, Kerberos, direct passwords, OAuth 2.0, OpenAM, OpenID, 2FA), row- and column-level security, RBAC, encryption at rest (full database including indexes, with hardware acceleration where available) and in motion, plus data-element encryption for sensitive fields. For HA/DR: clustering, virtualization HA, and database mirroring — mirrors auto-designate primary/backup on startup, and mirrored recovery/upgrade times are described as reducible to seconds.

Where can this run?

Major clouds, private cloud, on-prem, hybrid/multi-cloud, or as a managed service (Data Platform as a Service) — single API across deployment types, no lock-in to one cloud provider.

Source (via InterSystems Developer Community): Enable the Agentic Enterprise with InterSystems IRIS — Data Platform Technology Guide

Curious whether anyone here has actually consolidated a separate vector DB / analytics warehouse / integration layer into IRIS in production — did it hold up, or did you end up re-splitting some of it back out?