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You can use AI to extend a Sekiban DCB template into a small library-management application with book registration, borrowing, and returns. A published example uses PostgreSQL for persistence and a Blazor interface, then examines concurrent operations for consistency. Its report describes a short-time build, but gives no measured development time or benchmark; treat “fast” as an experience, not a guarantee. The author’s tutorial is a useful starting point for trying an event-sourced application with Sekiban DCB and AI.
What the example builds
The sample is a small library system rather than a production-ready catalog or circulation platform. It covers three core actions: registering a book, borrowing it, and returning it. The author reports building APIs and UI with AI, storing data in PostgreSQL, and presenting a basic Blazor interface. The tutorial also examines whether concurrent operations preserve consistency.
The author, kairi, describes using Codex with GPT-6 Astra. This is an account of one project, not a controlled comparison of development speed or evidence that AI-generated code is correct without review.
Start from the right Sekiban template
The tutorial reports using the .NET 10 SDK and Linux containers in Docker Desktop. Its commands create a project from the decider template:
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dotnet new install Sekiban.Dcb.Templates
dotnet new sekiban-dcb-decider -n BookManagementTemplate names matter. The project README’s current quick start instead shows the Orleans template. These are distinct paths, not interchangeable command variants. Check the Sekiban repository README for current setup instructions and select the template that matches your intended architecture.
The generated decider sample contains Student, ClassRoom, and Enrollment implementations. Rather than asking an AI to invent the application architecture from scratch, use these as local examples of how the project organizes domain behavior.
Read the sample before asking AI to extend it
Trace a feature end to end before prompting. The relevant pattern is not just a command or a UI page: it is how user input becomes a command, how a Decider evaluates that command, which events are emitted, how state is derived, and how queries and API endpoints expose results.
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- Inspect the existing UI flow and API boundary.
- Follow commands through their Deciders and emitted events.
- See how state is reconstructed and queried.
- Note naming, validation, error handling, and test conventions.
Then give the AI explicit requirements for the library actions and the repository conventions it must follow. Ask it to adapt the existing implementation style rather than introduce a parallel design. This makes generated work easier to review and helps preserve consistency across the UI, APIs, domain logic, and persistence.
Specify business rules and concurrent cases
For a library, the central challenge is not the form for adding a book; it is defining what must remain true when actions overlap. State the rules explicitly in the prompt and tests. For example, decide what should happen if two borrowers attempt to borrow the same book before either sees the other’s update, or if a return and a new borrowing attempt occur concurrently.
Describe expected outcomes, not just happy paths. Include which operation should succeed, which should be rejected or retried, and what the user should see. The tutorial says it checked concurrent-operation consistency, but does not supply a general benchmark or a universal guarantee for every store configuration.
The author writes that “Letting Sekiban handle conflict detection and persistence also allowed us to focus on implementing the business rules.” That is a report of the author’s experience. AI assistance and framework conflict handling do not replace review of domain rules, provider behavior, or generated code.
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How DCB makes consistency decisions
Dynamic Consistency Boundaries (DCB) frames a command’s consistency scope around the events relevant to its decision, rather than requiring every decision to be confined to one fixed aggregate stream. An event store can read sequenced events matching a query and append new events with an optional append condition. Queries can filter by event type and/or tags; tags can carry domain-specific identifiers, such as product:p123.
The decision is based on the events used to build an in-memory model and the last event position the client observed. Before accepting an append, the store checks whether new events matching the decision’s query have appeared in the meantime. If the append condition conflicts with intervening matching events, the append fails rather than silently accepting a decision based on stale information. The DCB specification also requires atomic persistence of one or more events.
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This model can let unrelated writes proceed while still checking cross-entity invariants represented in a command’s query. It is an architectural consistency approach, not proof that every storage provider has identical operational behavior. See the DCB specification and the DCB explanatory site for the underlying model.
Choose storage for its actual consistency behavior
The library example uses PostgreSQL. Sekiban’s documentation also lists Cosmos DB on Azure and DynamoDB on AWS as event-store options, and Azure Blob Storage and Amazon S3 for snapshots. It documents cloud components for Orleans clustering and streams as well. These are documented options, not evidence that each provider is interchangeable for every workload.
Before choosing a provider, compare the needs of your application against its documented behavior:
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- Used Book in Good Condition
- Whether event and tag visibility meets the consistency requirements of your commands.
- Query and indexing needs for the event types and tags that decisions use.
- Deployment, clustering, and operational expertise available to your team.
- Provider-specific recovery behavior and the consistency contract.
Sekiban’s repository specifically directs readers to review its storage consistency contract before selecting Cosmos DB for a workload requiring atomic event/tag visibility. Do not infer provider-level guarantees solely from a common framework API; consult the current repository documentation for the selected provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check project status and adapt the workflow
The Sekiban repository characterizes DCB as tag-based event sourcing with consistency scope defined per command, recommends Sekiban DCB for new projects, and lists Sekiban.Pure and Sekiban.Core as maintenance mode. These status statements can change, so confirm them in the current project README before starting a new application. The DCB implementation directory lists Sekiban.Dcb among C# implementations.
- Install the template and create a project using the template path appropriate to your architecture.
- Trace the generated sample’s UI, API, command, Decider, event, state, and query flow.
- Write down library rules and concurrency scenarios before prompting for feature work.
- Ask AI to adapt the established patterns for registration, borrowing, and returns.
- Review generated code and verify both ordinary and overlapping operations against the chosen provider’s consistency contract.
The practical lesson is to use AI as an implementation partner constrained by an existing codebase and clear domain rules. Sekiban DCB supplies a model for expressing which events matter to a decision; the application still needs explicit invariants, verification, and provider-aware deployment choices.
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