Beyond Legacy Databases: How DCB Future-Proofs AI Systems with Event Sourcing

Traditional distributed architectures are frequently constrained by their own rigidity. When working with legacy data architectures, developers often find themselves with the same problem over and over: data that was persisted in previous versions of the application is incompatible with future versions of the application.

This rigidity leads to "analysis paralysis". If the architecture team spends too much time thinking of every conceivable use of the system, then the project will take years to make a pilot or a simple Proof of Concept (POC).

This is a pure form of "architectural drag”, since it’s a direct tax on your TCO, developer velocity, and system performance. As our customers have told us, system complexity is a major pain point. One customer noted their previous architecture was "way too complex," and another reported that without snapshots, their "repository times out," and even with them, it still "takes more than 60 seconds to load."

The convergence of Axoniq’s platform innovations—specifically the Dynamic Consistency Boundary (DCB)—with artificial intelligence capabilities fundamentally resolves this rigidity. It transforms applications from static, transactional silos into adaptable, resilient, and intelligent systems.

This article examines how legacy data architectures, when enhanced by DCB, fundamentally transforms traditional request-response based AI systems and the agents they rely on.

1. The Solution: Dynamic Consistency Boundary (DCB)

Dynamic Consistency Boundaries (DCB), introduced in Axon Framework 5.0 and supported by Axon Server 2025.2, is a game-changer that brings true architectural flexibility to event-sourced applications. It is not just an update; it's a new architectural primitive.

The core value of DCB is dynamic scoping:

• Temporary Consistency Scope: DCB allows developers to define the transactional consistency scope dynamically, on a per-operation basis. It establishes a "temporary consistency bubble" that encapsulates only the necessary data at the moment it is needed.

• Makes Snapshots Obsolete: By eliminating the core problem with traditional databases, DCB removes the root cause that prevents data architectures to adapt and change when new requirements present themselves. The entire category of snapshotting workarounds is no longer necessary.

• Optimized Performance: This dynamic nature ensures data correctness without the performance degradation of locking oversized scopes.

• Adaptable Architecture: This approach allows the architecture to adapt as the team's understanding of the domain matures. Teams can begin with "good-enough boundaries" and refine them subsequently, aligning perfectly with agile development methodologies.

2. Event Tagging: The Future-Proof Data Foundation

DCB is enabled by event tagging, a feature that expands upon the traditional event sourcing pattern of rigidly tying an event to a single aggregate ID.

This provides the rich, flexible context necessary for intelligent agents:

• Event Tagging: This allows each event to carry multiple semantic labels (e.g., customer ID, order ID, product category).

• Instant Queryability: If a system is initially modeled around "customers" but the business later shifts to an "account-based" model, traditional methods would demand a resource-intensive re-architecture. With tagging, new labels can be added, making the entire event history instantly queryable from multiple perspectives.

• Future-Proofing the Agent’s Model: This capability allows the underlying domain model of an AI agent to adapt its consistency boundaries as the business evolves—even enabling an "aggregate-less approach"—without necessitating complex migrations or losing historical data.

3. Transformation 1: Structured, AI-Driven Development

In traditional request-response AI development, code generation can be an opaque "black box" prone to hallucinations.

The Axoniq platform transforms this into a structured, specification-driven process via the AI Dev Agent:

• Architecture-Aware Generation: The AI Dev Agent is taught to reason about the prompt and understands event modeling, user journeys, and CQRS (Commands, Queries, Events). It formalizes requirements by classifying steps as commands (blue), events (orange), queries (views on the data), and external system actions (purple).

• DCB-Aligned Code: The agent generates code broken down into components, which represent the consistency boundaries of DCB. This ensures the resulting code is manageable, comprehensible, and aligned with architectural best practices.

• Human-in-the-Loop: The AI breaks specifications into detailed user journeys. A human developer must approve all of these business processes and can cooperate with the AI to refine terminology or flow (e.g., changing "users" to "zooies" or "bikes" to "steps"). This process serves as an accessible entry point for all developers, allowing them to gain the benefits of an event-sourcing architecture without requiring deep, specialized expertise.

4. Transformation 2: High-Performance Contextual Analytics

For the Axoniq Insights Agent, the event-driven architecture ensures that data extraction is no longer a performance drain on the operational system:

• Performance Isolation: Analytics are executed on a specialized, read-only Analytics Node that runs alongside the primary cluster. This node tails the primary cluster and supports only read operations, ensuring that analytical queries do not impact the performance of the production cluster.

• Democratized Data Access: The Insights Agent allows users to pose natural language queries, which are then converted to SQL/CQL and executed against the event store. This demonstrates that knowledge of the business domain has become more important than mastering a complex data model or crafting SQL.

• Contextual Intelligence: Users can define a business ontology (e.g., defining a "Very Important Bike" or VIB) to provide the agent with domain understanding it may not initially possess. This deep contextualization—powered by the rich, tagged data foundation DCB enables—leads to more accurate and relevant analytical results.

5. Added Efficiency: Reactive Processing and Cost Reduction

Beyond DCB, Axon Framework 5.0 introduces architectural changes that offer demonstrable cost and performance benefits crucial for modern, agent-driven systems:

• Reactive Efficiency: Axon Framework 5.0 supports truly reactive event processing via an async-native API. This allows for non-blocking operations, eliminating performance bottlenecks under high-load conditions.

• Resource Optimization: This architecture enables applications to handle substantially more concurrent operations utilizing the same computing resources. This efficiency translates directly into significant cost reduction on cloud infrastructure and an improved ROI on existing hardware investments.

Dynamic Consistency Boundaries, Building the Future on an Adaptive Foundation

DCB and its supporting features represent a foundational evolution in software architecture. They solve the long-standing challenge of consistency versus flexibility, ensuring that early design decisions are not immutable.

By replacing the need for rigid aggregates and costly snapshots, DCB provides the tools and flexibility to create more resilient, maintainable, and high-performance distributed applications.

We invite developers to explore the potential of DCB and experiment with the "aggregate-less" approach. We are eager to hear your experiences on the Axoniq Discuss server as we guide the future of event-sourced architecture together.