Key Takeaways
* The “11 Steps of Agentic AI System Design” is deceptive, suggesting a simple ‘Digital Employee’ built from basic components like a Vector DB and an LLM router.
* eRAG’s Federated Query Engine unifies data by abstracting complex storage (Oracle, PostgreSQL, MS-SQL) into a single view.
* eRAG employs a Multi-Agent Orchestra of specialized agents (Query Optimization, Multi-Language, Self-Improvement Loops)

If you scroll through LinkedIn or X (formerly Twitter), you’ve likely seen the clean, logical diagrams explaining “How to Build Agentic AI.” They usually look something like the popular “Enterprise AI Execution Pipeline”:

AI Execution Engine System Design

On paper, between the User Layer (The input) and the Response Delivery (The output), this workflow is beautiful. It makes sense. It suggests that if you just stitch together a Vector DB, an LLM router, and a few prompts, you will have a functional “Digital Employee.”

Here is the hard truth: If you try to DIY this over enterprise data, specifically multiple RDBMSs, you are walking into a minefield.

At GigaSpaces, we did more than define an architecture. For almost two years, multidisciplinary teams of R&D engineers, data scientists, architects, and DevOps specialists worked together to deliver eRAG as a production-ready, out-of-the-box solution.

Here is what it actually takes to turn those “11 Steps” into a reality that works seamlessly across your data silos.

The “Magic” Behind the Scenes: Implementing eRAG

The most deceptive box in that 11-step diagram is Step 7: MCP / Tool Access Layer. 

It sounds simple: “It connects the agent to SQL databases.” But in the enterprise world, you don’t have one clean database, you have ten. They have different schemas and they use jargon that is often unique to your organization. For example, Table A in your CRM relates to Table B in your Billing System, but there is no foreign key linking them.

To achieve ready-made integration so that a user simply “connects to DBs” we had to engineer a massive infrastructure beneath the surface. 

1. The Federated Query Engine

Users don’t care where the data lives; they just want answers. We built a Federated Query Engine that abstracts the complexity of the underlying storage. Whether the data is in Oracle, PostgreSQL, or MS-SQL, our engine creates a unified view. The user asks a question, and the system knows exactly which DB, or combination of DBs holds the answer.

2. Metadata RAG & Ontology Building

Standard RAG vectorizes text chunks, but that doesn’t work for structured data. We built a sophisticated Ontology Engine using Metadata RAG. We ingest the database schemas, but we don’t stop there. We map the taxonomy and the jargon specific to your business into vector stores. This ensures that when a user asks about “ARR,” the AI understands exactly which column in the specific finance table represents “Annual Recurring Revenue,” regardless of how it was named by a DBA five years ago.

3. Graph RAG for “Foreign” Relationships

Tables in the same database usually have relationships, but what about relationships between different data sources? We utilize Graph RAG to map local and foreign relationships. We create a semantic graph that links a customer in your Support DB to their transactions in your Order DB. This allows the AI to ‘hop’ across silos to answer complex, multi-hop questions that a standard SQL query generator would fail to answer.

4. The Multi-Agent Orchestra

We didn’t stop with a single “Planner” (Step 4). Instead, we implemented a swarm of specialized agents to handle the cognitive load:

• Query Optimization Agents: They don’t just write SQL, they rewrite it for performance to ensure you don’t crash your production DB.
• Multi-Language Agents: They ensure the system works natively across global teams.
• Self-Improvement Loops: These Agents critique their own performance, learn from failed queries or user feedback to dynamically update the ontology.

Why DIY is a Trap

Looking at the reference architecture, it seems feasible to build a POC in a weekend, and you might be able to do this. But moving from a POC that queries one CSV file to a production system that queries a federated network of RDBMSs is a different beast.

We invested thousands of engineering hours tackling edge cases:

• What happens when the schema changes?
• How do you handle hallucinated table names?
• How do you secure row-level access control within the prompt context?

The Infrastructure Layer

We built eRAG to be the infrastructure layer that solves these problems out of the box. We handled the 11 steps, plus the hundreds of sub-steps between them, so you can focus on the application layer.

Image: The Dependency Architecture between components, it’s not straightforward!

The Next Frontier: Our Massive MCP Upgrade

We are doubling down on Step 7. We are excited to announce a major focus on our upcoming Model Context Protocol (MCP) support.

MCP is the standard that will finally allow AI agents to connect to data sources universally. By baking deep MCP support into eRAG, we are making our “Ready-Made Integration” even more powerful.

Naive Diagram of MCP Integration

This means:

1. Standardized Connectivity: Agents can plug into GigaSpaces eRAG via MCP and instantly gain access to the federated, cleaned, and graph-mapped data we maintain.
2. Safety & Context: The MCP layer will respect the rigorous guardrails and PII filtering we’ve already built.

The Bottom Line

Moving to an agentic workflow means shifting from Prompt Engineering to System Engineering. As the reference model suggests, you aren’t just writing text; you are managing state, orchestrating microservices, and building feedback loops.

You could spend two years and hire 50 engineers to build that foundation yourself, or you could just connect to GigaSpaces eRAG and get fast, accurate insights into your business by querying your organizational data. 

Would you like to see how eRAG handles your specific data schema?

Click here.