Building Agentic AI on Distributed SQL: Why Galaxy's Semantic Foundation Replaces Legacy Data Platforms

Agentic AI systems demand something traditional data platforms were never designed to provide: the combination of transactional correctness and semantic context. Unlike stateless inference APIs that simply respond to prompts, autonomous agents plan, reason, act, observe outcomes, and adapt over time. They need durable state they can trust and shared business meaning they can reason over. Most enterprise data infrastructure offers one or the other, rarely both.

Why Traditional Data Platforms Fail Agentic AI Workloads

The Shift from Stateless Inference to Autonomous Operations

Traditional applications use short-lived, predictable database transactions. A user clicks a button, the system updates a record, the transaction completes. Agentic AI breaks this model entirely with stateful operations that span minutes or hours.

Agents maintain context across multiple interactions, coordinate with other agents, and execute multi-step workflows where each action depends on observing the results of the previous one. They don't just read data and return an answer. They write state, trigger downstream processes, and make decisions that other agents must respond to.

This fundamental shift means databases designed for request-response patterns struggle under agentic workloads. The isolation guarantees, coordination mechanisms, and scalability assumptions all need rethinking.

Multi-Step Coordination and State Management Challenges

Agents execute dependent read-write sequences that must maintain consistency. Agentic workloads require precise orchestration where an agent might read customer status, evaluate eligibility, update multiple records, trigger notifications, and record its decision—all within a single logical operation that must either fully succeed or fully fail.

When multiple agents operate on overlapping data, coordination becomes critical. One agent updating a customer's subscription tier while another processes a refund creates race conditions that corrupt business logic. Without proper isolation, agents act on stale data and make decisions based on partial state.

Multi-agent systems require precise orchestration to ensure reliable inter-agent communication and conflict resolution mechanisms. The database layer must prevent contradictory actions while maintaining performance.

Legacy ETL Pipelines and Data Silos Block Real-Time Decision Loops

Autonomous agents need current information to make sound decisions. Legacy ETL pipelines delay insights with batch-oriented processing, making it nearly impossible for agents to respond quickly to changing conditions. When your customer churn model runs nightly and your retention agent operates continuously, the agent is always working with yesterday's understanding.

Worse, legacy systems often lack robust built-in governance and monitoring capabilities, increasing compliance risks as agents act on unvalidated data. The procedural logic embedded in decades-old ETL jobs doesn't translate cleanly to modern architectures, and real business logic often lives in hidden corners like Excel macros and manual data patches.

Data silos compound the problem. The average enterprise uses nearly 900 applications, and only about one-third are integrated. Agents attempting to coordinate across fragmented systems face conflicting definitions, duplicate entities, and no shared understanding of what terms like "active customer" actually mean.

Distributed SQL as the Transactional Foundation for Agent State

ACID Guarantees Prevent Partial Updates and State Corruption

ACID transactions ensure the highest possible data reliability, preventing data from falling into an inconsistent state because operations only partially complete. For agentic AI, this matters profoundly. When an agent executes a multi-step workflow—reading account status, calculating eligibility, updating records, and triggering downstream processes—the entire sequence must succeed or fail atomically.

CockroachDB provides strict serializable isolation by default, ensuring agents never act on stale or partially updated state. This level of consistency means agents can trust the data they observe and coordinate safely with other agents operating on overlapping entities. Without it, you're building autonomous systems on quicksand.

The durability component of ACID guarantees that once an agent commits a transaction, its changes persist even through system failures. This matters for long-running agentic systems where decisions made hours ago must remain intact and auditable.

Horizontal Scalability Without Schema Redesign

Modern distributed SQL databases support fully distributed ACID transactions across multiple rows, shards, and nodes at any scale and across zones, regions, and countries. This solves a critical challenge: as your agent fleet grows, you need to add capacity without redesigning schemas or compromising consistency.

Traditional SQL databases forced you to choose between strong consistency and horizontal scale. Distributed SQL databases combine seamless scalability with strong ACID guarantees, solving issues that traditional SQL databases face with horizontal scaling. You can add or remove capacity dynamically while maintaining the transactional correctness agents depend on.

This architectural approach uses consensus protocols like Raft or Paxos to ensure consistency across nodes, creating a foundation where agents can operate globally without sacrificing correctness for performance.

What Distributed SQL Alone Cannot Provide

Durable persistence with strong consistency is necessary but insufficient for production agentic AI. A distributed SQL database can tell you that customer ID 12345 has a status field set to "active" and a subscription tier of "enterprise." It cannot tell you what "active" means in your business context, how it relates to billing cycles, or why certain state transitions are valid while others violate business rules.

AI agents need to reason over disparate data sets and collaborate across siloed application stacks, but databases provide structure without semantics. An LLM cannot inherently understand enterprise-specific concepts without explicit definitions. The database stores facts; it doesn't encode meaning or relationships that span systems.

This gap between transactional storage and semantic understanding is where most agentic AI implementations struggle. You have reliable state but no shared vocabulary for agents to interpret it.

The Missing Layer: Semantic Infrastructure for Shared Understanding

Why Agents Need Business Context Beyond Raw Tables

Without a semantic layer, AI models lack the ability to inherently understand enterprise-specific concepts like "Active Customer," "Gross Margin," or "Net Revenue," leading to hallucinations and inaccurate responses. Raw tables contain columns and values. They don't explain that "customer_status = 1" means something different from "subscription_active = true" or how these fields relate to payment history and support interactions.

Agents operating without business context make brittle assumptions. They might treat a customer with a paused subscription the same as one who churned, or calculate revenue incorrectly because they don't understand the relationship between invoice line items and recognized revenue. The database enforces referential integrity; it doesn't encode business logic or domain knowledge.

This becomes critical in multi-agent systems where specialized agents must coordinate. If one agent updates customer tier while another processes refunds, they need a shared understanding of what those operations mean and how they interact.

Semantic Layer vs. Data Catalog: Translating Structure into Meaning

Data catalogs inventory and describe data assets via metadata for discovery, while semantic layers translate raw data into consistent business terms that both people and AI can interpret. A catalog tells you where to find customer data and what columns exist. A semantic layer defines what "customer lifetime value" means, how it's calculated, and which systems contribute to it.

A semantic layer sits between your data warehouse and tools that access it, defining what terms like "revenue" mean and how concepts relate. It creates a unified business view that standardizes definitions across organizations, ensuring consistency in reporting and analysis while managing data access and security.

For agentic AI, this distinction matters enormously. Agents don't just need to find data—they need to understand it. A catalog helps with discovery; a semantic layer provides the interpretation framework that makes autonomous reasoning possible.

Knowledge Graphs as Contextual Grounding for Multi-Agent Reasoning

Knowledge graphs serve as a contextual grounding layer, ensuring agents reference correct entities and relationships during reasoning processes, greatly reducing errors. They model entities, their attributes, and relationships in a way that mirrors how people reason by connecting concepts.

When agents generate information that other agents must consume, the knowledge graph records this as a structured relationship between entities, preserving meaning, provenance, and context. This creates an auditable record of agent decisions and the reasoning chains that produced them.

Knowledge graphs enable agents to reason about new concepts without extensive training by leveraging existing knowledge relationships through zero-shot or few-shot learning. An agent encountering a new product category can reason about it based on its relationships to known categories, pricing tiers, and customer segments.

Galaxy's Semantic Foundation: Architecture for Agentic AI

Unified Entity Model with Explicit Lifecycles and Dependencies

Galaxy models businesses as interconnected systems with explicit lifecycles, dependencies, and meaning rather than cataloging metadata or moving data into another repository. This approach treats entities like customers, products, and subscriptions as first-class objects with defined states, transitions, and relationships.

Where traditional data platforms flatten business logic into tables and views, Galaxy makes structure explicit. A customer entity doesn't just have fields—it has a lifecycle from prospect to active to churned, with dependencies on subscription entities, payment methods, and support interactions. These relationships are modeled as semantic connections that agents can traverse and reason over.

This unified entity model provides the shared vocabulary that multi-agent systems require. When a billing agent updates subscription status, a retention agent can understand the implications because both operate over the same semantic representation of what a subscription is and how it relates to customer health.

Context Graphs Alongside Distributed SQL Systems

Galaxy provides machine-readable business meaning that complements transactional storage layers rather than replacing them. Your distributed SQL database maintains ACID guarantees and handles state management. Galaxy sits alongside it, offering a semantic layer that explains what the data means and how entities relate across systems.

This architecture preserves the strengths of both approaches. Distributed SQL handles state management with strong consistency. Galaxy provides the context graph that enables agents to interpret that state correctly and coordinate through shared understanding.

The integration is bidirectional. Agents write state changes to the transactional database and update the knowledge graph to reflect new relationships or entity transitions. When reasoning about next actions, they query the graph to understand context and dependencies before executing database transactions.

Entity Resolution and Single Source of Truth Across Silos

Entity resolution consolidates multiple labels for individuals, products, or other data classes into a single resolved entity, enabling agents to work with a unified view rather than duplicate or conflicting records. Galaxy performs this resolution across your existing systems without requiring data migration.

Without entity resolution, agents would be forced to interpret fragmented data across systems, leading to incorrect reasoning and decision-making based on incomplete information. When your CRM calls someone "John Smith," your billing system knows them as "J. Smith," and your support platform uses "John R. Smith," agents need a way to understand these refer to the same person.

Galaxy creates a single source of truth by resolving entities and modeling their relationships explicitly. A single source of truth breaks down silos by ensuring all departments work with the same reliable data, resulting in better communication and collaboration across the organization—and across agents operating on behalf of different functions.

Core Requirements for Production-Grade Agentic Infrastructure

Consistent State Management Across Concurrent Agent Operations

Multi-agent systems must implement conflict detection and resolution mechanisms such as priority queues, locks, or optimistic concurrency to prevent contradictory actions. When multiple agents attempt to modify the same entity simultaneously, the infrastructure must handle conflicts gracefully without corrupting state.

Shared context is critical in multi-agent systems where multiple specialized agents operate concurrently and need to align their understanding of the environment. This goes beyond database-level locking to include semantic understanding of what operations are compatible and which require serialization.

The combination of distributed SQL's transactional guarantees and Galaxy's semantic layer provides both mechanisms. ACID transactions prevent data corruption at the storage level. The knowledge graph ensures agents understand the business implications of concurrent operations and can coordinate through explicit dependency modeling.

Event Tracking and Traceable Decision Memory

Agents make autonomous decisions that impact business outcomes. You need to know what they did, why they did it, and what information they based decisions on. Event-driven handoffs enable agents to communicate primarily through domain events rather than direct calls, creating a coordination layer that's flexible and auditable.

Galaxy records agent actions as structured relationships in the knowledge graph, preserving provenance and audit trails. When an agent updates a customer's subscription tier, that action becomes a timestamped event linked to the agent, the customer entity, the previous state, and the reasoning context that informed the decision.

This traceable decision memory serves multiple purposes. It enables debugging when agents make unexpected choices. It provides compliance and audit trails for regulated industries. It creates training data for improving agent behavior over time.

Interoperability Without Vendor Lock-In or Proprietary Translation

Legacy ETL tools create vendor lock-in by generating proprietary code with limited interoperability and portability. Agentic infrastructure must avoid this trap. Agents need to work with your existing systems, not force you to replace them with vendor-specific alternatives.

Galaxy connects directly to existing data sources through standard protocols and APIs. It doesn't require migrating data into a proprietary repository or rewriting integrations in custom formats. The semantic layer it provides is expressed through open standards that agents can consume regardless of their implementation framework.

This interoperability extends to the agent layer itself. Whether you're building agents with LangChain, AutoGen, or custom frameworks, they can query Galaxy's knowledge graph and reason over the same unified entity model. The infrastructure serves as a common foundation rather than dictating your agent architecture.

Practical Patterns: Connecting AI Workflows to Semantic Infrastructure

Graph-RAG for Factual Context-Rich Generation

Graph-RAG is a key architectural pattern that uses knowledge graphs to achieve factual, context-rich generation by leveraging structured knowledge. Instead of retrieving raw documents or database records, agents query the knowledge graph to understand entity relationships and business context before generating responses.

This approach dramatically reduces hallucinations. When an agent needs to explain why a customer's renewal failed, it traverses the graph to find relationships between the customer entity, their subscription, recent payment attempts, and support interactions. The generated explanation is grounded in actual business relationships rather than statistical patterns in training data.

Galaxy's context graphs provide the structured knowledge that makes Graph-RAG practical for enterprise use cases. Agents can perform semantic queries that return not just facts but the relationships and dependencies that explain why those facts matter.

Sequential Orchestration with Shared Entity Context

Sequential orchestration chains AI agents in predefined linear order where each agent processes output from the previous agent. Without shared context, this becomes a game of telephone where meaning degrades at each handoff.

Galaxy enables sequential orchestration through unified entity models rather than raw data handoffs. A lead qualification agent passes not just a customer record but a resolved entity with its full relationship graph to the sales routing agent. The routing agent understands the customer's history, current state, and dependencies without re-querying multiple systems.

Orchestrated multi-agent approaches achieve 100% actionable recommendations compared to only 1.7% for uncoordinated single-agent systems, with 80× improvement in action specificity. Shared semantic context is what makes this coordination effective.

Event-Driven Coordination via Domain Events and Semantic Relationships

Event-driven architectures enable loose coupling between agents while maintaining coordination. When a billing agent processes a failed payment, it emits a domain event that other agents can respond to. The challenge is ensuring those events carry sufficient context for downstream agents to act appropriately.

Galaxy models domain events as semantic relationships in the knowledge graph. A payment failure event isn't just a notification—it's a structured entity linked to the customer, the subscription, the payment method, and the billing cycle. Agents subscribing to these events receive rich context that enables intelligent responses.

This pattern supports flexible coordination where agents can be added or modified without rewriting integration logic. The semantic layer provides stable abstractions that agents reason over, even as the underlying systems and event schemas evolve.

Moving from Prototypes to Production: Combining Transactional Correctness with Shared Context

Production agentic AI requires pairing distributed SQL's reliability with Galaxy's semantic layer. The database ensures agents operate on consistent, durable state with ACID guarantees. Galaxy provides the business meaning and relationship modeling that enables agents to interpret that state correctly and coordinate through shared understanding.

This combination addresses the full stack of requirements: state management with strong consistency, semantic context that reduces hallucinations, entity resolution across silos, traceable decision memory, and interoperability without vendor lock-in. Neither component alone is sufficient.

Organizations building agentic systems often start with either a database-centric approach that lacks semantic grounding or a knowledge graph without transactional guarantees. Both paths hit walls in production. Agents need the operational resilience of distributed SQL and the interpretive framework of a semantic layer working together as integrated infrastructure.

Galaxy's architecture recognizes this reality. It doesn't replace your transactional systems—it complements them by making business meaning explicit and machine-readable. The result is infrastructure where autonomous agents can operate with both correctness and understanding, turning the promise of agentic AI into reliable production systems.