Data Modeling for AI Agents pt.1: the Systems of Record Layer

Modern AI systems increasingly rely on multi-agent architectures, where specialized agents (planning, search, action, review, and response) collaborate to achieve complex goals. To make that collaboration reliable and efficient, we have found that data must flow cleanly across three key layers:

1. Systems of Record (SoR): The source of truth for your business and operational data.
2. Context & Knowledge Management System (CKMS): The intelligence layer that turns data into usable context for agents.
3. Orchestration Layer: The process brain that coordinates how agents use that context to act.

Each layer needs intentional data modeling. Done right, it eliminates duplication, reduces operational cost, and keeps your agents aligned with the real circumstances of your business, avoiding drift into disconnected silos.

👉 This series expands on the framework introduced in our main article, Data Modeling for Multi-Agent Architectures diving deeper into the first layer and how to design it effectively.

Here are the links to the rest of the series:

• Data Modeling for AI Agents pt.1: the Systems of Record Layer: this article
• Data Modeling for AI Agents pt.2: the Context and Knowledge Layer
• Data Modeling for AI Agents pt.3: the Orchestration Layer

Multi-agent systems live and die by data consistency. When each agent has a slightly different view of “truth” on e.g. who the customer is, what their latest action was, or whether an order shipped, chaos follows.

The Systems of Record (SoR) layer prevents this by serving as the single, authoritative source for all structured operational data.

Your agents, no matter how advanced, are only as smart as the data they read from.

1. Why SoRs Matter for AI Systems

The role of Systems of Record goes beyond databases, because they set foundational boundaries that define what the rest of the AI stack can rely on.

In a multi-agent setup, this means that:

• Agents retrieve verified entities (e.g., customer, order, lead)
• Other layers (like the CKMS or orchestration layer) subscribe to changes, rather than editing data directly
• Every update has provenance since you know which system, agent, or user made it

A solid SoR design stops issues like data duplication, circular dependencies, or high embedding costs downstream.

2. Modeling for Stability and Agent Access

The goal is to make schemas predictable.

Good SoR models have:

• Stable IDs (UUIDs, not auto-incrementing integers)
• Timestamps and versioning (for time-travel queries and context validation)
• Entity normalization (clear separation between users, products, interactions, etc.)
• Ownership tags (which agent or service “owns” each field)

This structure helps both humans and agents know which data can be trusted, and which is derived or temporary.

3. Connecting the SoR to the Rest of the Stack

The SoR feeds into the CKMS (Context & Knowledge Management System), which in turn feeds the agents.

There are three common sync patterns:

4. Example Setup

A pragmatic early-stage stack might look like this:

• Postgres or MySQL as your SoR
• Kafka, Supabase Functions, or Airbyte to stream data changes
• Weaviate, Pinecone or PGVector as the CKMS layer
• Temporal/Prefect as the orchestration layer

Each SoR entity (like customer, order, or campaign) syncs to the CKMS with minimal metadata, embeddings, and timestamps, letting agents search and reason across a stable, queryable data graph.

5. Common Pitfalls

1. Agents writing directly to operational DBs → creates race conditions and silent corruption
2. No consistent IDs between systems → embeddings and context drift apart
3. Embedding everything → unnecessary compute cost and latency
4. No lineage tracking → impossible to debug why an agent made a certain decision

6. Key Takeaway

A well-modeled SoR turns your AI stack from a sandbox into a scalable system.

Agents stop guessing and start executing against data that’s consistent, queryable, and versioned, which we believe to be the foundation of every scalable multi-agent architecture.