I’m taking a little bit of time today to start writing up a “strategery” document on the JasperFx / Critter Stack approach to AI in the near future. One of the arguments I’m wanting to lean into quite heavily to call Wolverine AI-friendly is how it compresses code in its approach to “Vertical Slice Architecture” and how its focus on the “A-Frame Architecture” to testability has also made Wolverine AI friendly as well.

Formal layered architecture approaches like the Clean Architecture or other Hexagonal Architecture flavors have dominated the .NET landscape as the accepted “best practices” approach for years. It was a well intentioned strategy to improve maintainability through enforcing some level of loose coupling. Today though, there’s a new factor that’s annoyingly dominating seemingly all software development discourse: the AI coding agent.

An agent doesn’t get tired, but it does have a finite context window, and it pays — in tokens, in latency, and in accuracy — for every irrelevant file it has to load to understand one feature. The structure of your codebase is now, effectively, part of the prompt. And it turns out that the architecture that’s easiest for an agent to reason about is a vertical slice approach, which is conveniently enough, what I’d prefer to do anyway and something that Wolverine is very good at.

I want to make the case that the Critter Stack — and Wolverine specifically — is the best foundation and approach in .NET for vertical slice architecture in the age of coding agents, precisely because of how aggressively it compresses the code you have to write and read for each feature as well as the structural consistency you’ll get by following our recommended idioms.

Why layered architectures fight the agent

Picture the canonical “clean” layered solution: controllers in one project, application services in another, a pile of IRequest/IRequestHandler types, repository interfaces and their implementations, DTOs, mapping profiles, and a domain project underneath it all. To change one behavior — say, how a shipment gets created — an agent has to go find the controller, the request, the handler, the validator, the repository interface, the repository implementation, and probably a mapping profile or two. They live in six or seven directories, scattered among dozens of unrelated features that share those same folders.

Every one of those files has to be pulled into the agent’s context before it can safely make a change. Most of what it loads is irrelevant to the task. The signal-to-noise ratio in the context window collapses, and that’s exactly the condition under which agents start guessing — inventing abstractions you didn’t ask for, “fixing” error cases that can’t happen, and drifting away from the intent of the change. The architecture that was supposed to manage complexity ends up manufacturing context pollution.

This isn’t a hypothetical. It’s become the dominant theme in writing about AI-ready codebases over the last year, usually under the banner of locality of reference: keep everything a feature needs in one place, and the agent loads only what’s relevant. The conclusion practitioners keep arriving at is that feature-organized, vertical-slice code is simply easier — and cheaper — for an agent to work in than layer-organized code.

Vertical slices, and the ceremony problem

Vertical slice architecture, as Jimmy Bogard originally framed it, organizes code around features instead of technical layers. A slice owns its whole pathway: take the input, do the work, produce the output, all in one place. It’s no accident that the .NET community tends to conflate VSA with MediatR — they share an originator, and MediatR’s request-per-handler model nudges you naturally toward one self-contained unit per use case.

For the record, FubuMVC (Wolverine’s predecessor) also promoted what we now call “vertical slices,” but as Jimmy told me, does it matter if nobody used the tool? (ouch).

But here’s the thing, the “just use MediatR” approach still carries a surprising amount of ceremony. For one feature you typically write a request record, the IRequest<T> marker, a handler class implementing IRequestHandler<,>, constructor injection for every dependency, an explicit SaveChangesAsync, a separate call through IMediator/IPublisher to raise follow-on events, a Program.cs registration, and a pipeline behavior to wire up validation. The slice is co-located, which is good. But it’s not small. There’s a lot of structural code surrounding the two or three lines that actually express the business decision — and the agent has to read all of it.

If fewer files and tighter context are what make vertical slices good for AI, then the natural question is: how few artifacts can a slice actually be reduced to?

Wolverine takes the slice to its logical conclusion

This is where Wolverine earns its place in the conversation. Wolverine is, in effect, vertical slice architecture compressed about as far as the language allows. Consider a typical “create a shipment” slice the MediatR way:

public record CreateShipment(Guid OrderId, string Carrier) : IRequest<ShipmentCreated>;
public class CreateShipmentValidator : AbstractValidator<CreateShipment>
{
    public CreateShipmentValidator() => RuleFor(x => x.Carrier).NotEmpty();
}
public class CreateShipmentHandler : IRequestHandler<CreateShipment, ShipmentCreated>
{
    private readonly IDocumentSession _session;
    private readonly IPublisher _publisher;
    public CreateShipmentHandler(IDocumentSession session, IPublisher publisher)
    {
        _session = session;
        _publisher = publisher;
    }
    public async Task<ShipmentCreated> Handle(CreateShipment request, CancellationToken ct)
    {
        var shipment = new Shipment(request.OrderId, request.Carrier);
        _session.Store(shipment);
        await _session.SaveChangesAsync(ct);
        var @event = new ShipmentCreated(shipment.Id, request.OrderId);
        await _publisher.Publish(@event, ct);
        return @event;
    }
}

Plus the controller that calls it, plus the MediatR and validation-pipeline registration in Program.cs.

Here’s the same slice in Wolverine:

public record CreateShipment(Guid OrderId, string Carrier);
public class CreateShipmentValidator : AbstractValidator<CreateShipment>
{
    public CreateShipmentValidator() => RuleFor(x => x.Carrier).NotEmpty();
}
public static class CreateShipmentHandler
{
    [Transactional]
    public static ShipmentCreated Handle(CreateShipment command, IDocumentSession session)
    {
        var shipment = new Shipment(command.OrderId, command.Carrier);
        session.Store(shipment);
        // The returned event is published by Wolverine as a cascading message.
        // No IMediator. No manual SaveChangesAsync. No pipeline wiring.
        return new ShipmentCreated(shipment.Id, command.OrderId);
    }
}

Look at what disappeared. No marker interfaces — Wolverine discovers handlers by convention, so the type carries no IRequest/IRequestHandler noise. No constructor and no fields — dependencies arrive by method injection, declared right where they’re used. No manual transaction management — [Transactional] (or a global auto-transaction policy) lets Wolverine and Marten manage the unit of work and use the document session as a transactional outbox. No separate publish call — returning a value is publishing it, as a cascading message. The validator is still a plain FluentValidation validator, but it’s discovered and run by Wolverine’s middleware; there’s no pipeline behavior to hand-wire.

And also, the Wolverine version also integrates a transactional outbox capability for durable execution. Now that there is so much interest in modular monoliths right now as well, I think it’s going to be important for folks to consider asynchronous workflows between modules within the same system. Conveniently enough, Wolverine has very strong support for that through its in process queueing, transactional outbox for durability, and built in Open Telemetry tracing for visibility into the asynchronous workflows. MediatR and all the tools it has inspired typically do not have any of that.

What’s left is almost entirely the business decision — and that’s the whole point!

It compresses even further on the HTTP edge. With Wolverine.Http, the endpoint is the handler — there’s no controller calling a mediator calling a handler:

public static class CreateShipmentEndpoint
{
    [WolverinePost("/shipments")]
    [Transactional]
    public static ShipmentCreated Post(CreateShipment command, IDocumentSession session)
    {
        var shipment = new Shipment(command.OrderId, command.Carrier);
        session.Store(shipment);
        return new ShipmentCreated(shipment.Id, command.OrderId);
    }
}

And if you’re doing event sourcing with Marten, the aggregate handler workflow collapses the load-decide-append-save dance into a single method that receives the current aggregate state and returns the resulting events:

public static class ShipOrderHandler
{
    [AggregateHandler]
    public static OrderShipped Handle(ShipOrder command, Order order)
    {
        if (order.HasShipped)
            throw new InvalidOperationException("Order has already shipped");
        return new OrderShipped(command.OrderId, DateTimeOffset.UtcNow);
    }
}

Wolverine fetches and rehydrates the Order from its event stream, hands it to you, appends whatever events you return, and commits — transactionally — without you writing any of that plumbing. The slice is the decision and nothing else.

Mediator tools were valuable when you were using them within ASP.Net Core MVC architectures where MVC controllers organized around domain entities (think InvoiceController) had a tendency to get very bloated. MediatR absolutely provided value in those days for teams to mitigate and control the accidental complexity from that type of MVC controller approach. In my opinion though, using a “mediator” should be completely unnecessary with Wolverine.

Wolverine can of course be used as just a “mediator” too, but I tend to recommend against that in most cases.

Why compression is the feature for AI

Tightly co-located slices are good for agents; small slices are better. Three reasons it compounds:

The whole slice fits in context. When a feature is one record, one validator, and one short static handler, the agent can load the entire unit of work and still have headroom for the task. It never has to reconstruct a flow from fragments strewn across layers, which is precisely the situation where agents hallucinate.

There’s far less surface to get wrong. Boilerplate isn’t free for an agent — it’s more code to generate correctly, more interfaces to implement consistently, more registration to remember. Every artifact Wolverine removes is an artifact the agent can’t fumble. Returning a cascading message can’t drift the way a hand-written IPublisher.Publish call can be forgotten or mis-ordered.

It’s cheaper to operate. This one is easy to overlook. Fewer tokens loaded per task is a direct, recurring cost reduction every single time an agent touches the code — whether that’s a developer’s Copilot session, a Claude Code run, or an automated diagnostic agent. In a world where you may be paying per token to run agents against your system, the most compressed codebase is also the cheapest one to keep an agent working in.

And by the way, our new curated AI Skills for the Critter Stack will help you settle into idiomatic vertical slice usage with Wolverine and Marten that will fit well into AI usage. And it so happens that you can purchase access to those AI Skills from the JasperFx website🙂

Compression alone isn’t the whole story

I want to be honest about where “just write tiny handlers” stops being sufficient, because the failure mode is real. When every part is small and stateless, the burden of knowing how the parts wire together — what conventions discover a handler, what a cascading return actually does, what middleware runs around your method — doesn’t vanish. It shifts somewhere. If it shifts into the agent’s context as guesswork, you’ve traded one problem for another.

The answer is conventions plus documented context. Wolverine’s behavior is convention-driven, which means it’s learnable and, more importantly, encodable. This is exactly why we ship AI skill files for the Critter Stack: they give the agent the macrostructure that the compressed slice deliberately leaves implicit — handler discovery rules, the cascading-message model, when and how the transactional middleware applies, the idiomatic shape we actually want. The skills are the constitution; the slices are the code. Together they give an agent a codebase that is both minimal to read and unambiguous to extend. Compressed code without the conventions documented is just terse code. Compressed code with the conventions encoded is an architecture an agent can work in confidently.

Two practical notes in the same honest spirit. Wolverine generates the glue code around your handlers, and that generated code is a benefit for AI — the agent writes the small handler; the framework produces the plumbing it would otherwise have to read and reproduce — but your conventions should tell the agent plainly that generated code is not to be hand-edited, and explain the model so it doesn’t fight the generator. And the classic VSA critique about duplication at scale still applies: resist the urge to grow a sprawling shared “services” layer the moment two slices rhyme. Wolverine’s middleware and compound-handler patterns are the right place to absorb genuinely shared concerns without rebuilding the layered architecture you just escaped.

It’s still in flight, but we’ve put work into our forthcoming CritterWatch tool to give you visualizations of how messages flow between systems or even between handlers within the same system. I’ll be recording a video on that some time next week.

The takeaway

The industry is converging on vertical slices as the AI-friendly way to organize code, and it’s converging there for sound reasons: locality, focus, and a clean context window. Wolverine is the most thorough expression of that idea in .NET. It strips a slice down to the business decision, removes the ceremony that an agent would otherwise have to read and reproduce, and — paired with skill files that encode the conventions — gives a coding agent a codebase that is small, coherent, and cheap to reason about.

If your architecture is now part of the prompt, the move is to make that prompt as short and as clear as it can be. That’s been the Wolverine philosophy from the start. It just happens to be exactly what the agents want too.

This is in lieu of the “official” release post I’ll make early next week after the Memorial Day holiday when I have enough rest and energy to do something much better by hand. Until then, here’s a peek at the new releases we’ll be announcing early next week — but with some published updates to our AI Skills tomorrow morning that will help you do the migrations!

The whole stack just shipped to NuGet — a coordinated major release across every Critter Stack project, built on a shared JasperFx 2.0 foundation and targeting .NET 9 + .NET 10. Headline themes: AOT-friendly, faster cold-start, and Marten ↔ Polecat dedupe onto shared infrastructure.

(Quick notes for now — a full writeup written by an actual human being with benchmarks lands next week.)

JasperFx 2.0 (foundation)

The shared base for the whole stack — also ships JasperFx.Events 2.0 + JasperFx.RuntimeCompiler 5.0.

• AOT-compatible core; runtime Roslyn split into JasperFx.RuntimeCompiler so Static-mode apps drop it and the trimmer removes Roslyn.
• Source generators for projection dispatch (FEC-free), CLI command discovery, and options descriptions — less runtime reflection.
• Cold-start wins (delegate caching, deterministic/byte-stable codegen) + consolidated event-store & async-daemon abstractions.

📖 Docs: https://shared-libs.jasperfx.net · 🏷️ Release: https://github.com/JasperFx/jasperfx/releases/tag/2.0.0 · 🧭 Migration: https://shared-libs.jasperfx.net/migration-guide.html

Weasel 9.0 (database layer)

• Database-primitive foundation on JasperFx 2.0; the consolidation home for IStorageOperation, OperationRole, BulkInsertMode, and the SQL Server advisory lock (dedupe pillar).
• Postgres / SQL Server / Sqlite / MySql / Oracle / EF Core providers, all 9.0.0.

📖 Docs: https://weasel.jasperfx.net · 🏷️ Release: https://github.com/JasperFx/weasel/releases/tag/v9.0.0 · 🧭 Migration: https://weasel.jasperfx.net/migration-guide.html

Marten 9.0

• No more runtime code generation — Roslyn is gone. Closed-shape document/event storage + Marten.SourceGenerator compiled queries. No codegen write step for Marten; AOT-publishable in Static mode.
• Best-perf defaults flipped on: Quick append w/ server timestamps, advanced async tracking, bigint events, lightweight sessions, System.Text.Json. One-line revert with opts.RestoreV8Defaults().
• IRevisioned.Version stays int (V8-compatible); new ILongVersioned for long. DCB gains optional HSTORE tag storage + identity-less boundary aggregates.

📖 Docs: https://martendb.io · 🏷️ Release: https://github.com/JasperFx/marten/releases/tag/V9.0.0 · 🧭 Migration: https://martendb.io/migration-guide.html

Polecat 4.0

• SQL Server / EF Core-rooted event sourcing on the shared foundation — source-generator-first and AOT-clean end to end.
• Adopted the lifted JasperFx.Events async-daemon abstractions; folded SqlServerAppLock into Weasel.SqlServer.AdvisoryLock; SingleTenant lock-ids now align with Marten.
• Ships in lockstep with Marten 9.

📖 Docs: https://polecat.jasperfx.net · 🏷️ Release: https://github.com/JasperFx/polecat/releases/tag/V4.0.0 · 🧭 Migration: https://polecat.jasperfx.net/migration-guide.html

Wolverine 6.0

• ⚠️ Runtime codegen decoupled from core. Apps in the default Dynamic mode must add WolverineFx.RuntimeCompilation (dev/test), or pre-generate via codegen write + Static mode (prod). This is the #1 upgrade gotcha — see the migration guide.
• ⚠️ ServiceLocationPolicy.NotAllowed is the new default — restructure registrations or call opts.RestoreV5Defaults() to revert.
• AOT-clean; Tier-1 cold-start static handler registry. Newtonsoft extracted to WolverineFx.Newtonsoft; IForwardsTo<T> discovery is now explicit.

📖 Docs: https://wolverinefx.net · 🏷️ Release: https://github.com/JasperFx/wolverine/releases/tag/V6.0.0 · 🧭 Migration: https://wolverinefx.net/guide/migration.html

Upgrading? Bump the whole stack in lockstep — JasperFx 2.0 / Weasel 9.0 / Marten 9.0 / Polecat 4.0 / Wolverine 6.0. Each migration guide above has an at-a-glance table of breaking changes. The big perf/benchmark writeup is coming next week. 🚀

I normally write this out in January, but I’m feeling like now is a good time to get this out as some of it is in flight. So with plenty of feedback from the other Critter Stack Core team members and a lot of experience seeing where JasperFx Software clients have hit friction in the past couple years, here’s my current thinking about where the Critter Stack development goes for 2026.

As I’m sure you can guess, every time I’ve written this yearly post, it’s been absurdly off the mark of what actually gets done through the year.

Critter Watch

For the love of all that’s good in this world, JasperFx Software needs to get an MVP out the door that’s usable for early adopters who are already clamoring for it. The “Critter Watch” tool, in a nutshell, should be able to tell you everything you need to know about how or why a Critter Stack application is unhealthy and then also give you the tools you need to heal your systems when anything does go wrong.

The MVP is still shaping up as:

• A visualization and explanation of the configuration of your Critter Stack application
• Performance metrics integration from both Marten and Wolverine
• Event Store monitoring and management of projections and subscriptions
• Wolverine node visualization and monitoring
• Dead Letter Queue querying and management
• Alerting – but I don’t have a huge amount of detail yet. I’m paying close attention to the issues JasperFx clients see in production applications though, and using that to inform what information Critter Watch will surface through its user interface and push notifications

This work is heavily in flight, and will hopefully accelerate over the holidays and January as JasperFx Software clients tend to be much quieter. I will be publishing a separate vision document soon for users to review.

The Entire “Critter Stack”

• We’re standing up the new docs.jasperfx.net (Babu is already working on this) to hold documentation on supporting libraries and more tutorials and sample projects that cross Marten & Wolverine. This will finally add some documentation for Weasel (database utilities and migration support), our command line support, the stateful resource model, the code generation model, and everything to do with DevOps recipes.
• Play the “Cold Start Optimization” epic across both Marten and Wolverine (and possibly Lamar). I don’t think that true AOT support is feasible, but maybe we can get a lot closer. Have an optimized start mode of some sort that eliminates all or at least most of:
  • Reflection usage in bootstrapping
  • Reflection usage at runtime, which today is really just occasional calls to object.GetType()
  • Assembly scanning of any kind, which we know can be very expensive for some systems with very large dependency trees.
• Increased and improved integration with EF Core across the stack

Marten

The biggest set of complaints I’m hearing lately is all around views between multiple entity types or projections involving multiple stream types or multiple entity types. I also got some feedback from multiple past clients about the limitation of Marten as a data source underneath UI grids, which isn’t particularly a new bit of feedback. In general, there also appears to be a massive opportunity to improve Marten’s usability for many users by having more robust support in the box for projecting event data to flat, denormalized tables.

I think I’d like to prioritize a series of work in 2026 to alleviate the complicated view problem:

• The “Composite Projections” Epic where you might use the build products of upstream projections to create multi-stream projection views. This is also an opportunity to ratchet up even more scalability and throughput in the daemon. I’ve gotten positive feedback from a couple JasperFx clients about this. It’s also a big opportunity to increase the throughput and scalability of the Async Daemon by making fewer database requests
• Revisit GroupJoin in the LINQ support even though that’s going to be absolutely miserable to build. GroupJoin() might end up being a much easier usage that all our Include() functionality. 
• A first class model to project Marten event data with EF Core. In this proposed model, you’d use an EF Core DbContext to do all the actual writes to a database. 

Other than that, some other ideas that have kicked around for awhile are:

• Improve the documentation and sample projects, especially around the usage of projections
• Take a better look at the full text search features in Marten
• Finally support the PostGIS extension in Marten. I think that could be something flashy and quick to build, but I’d strongly prefer to do this in the context of an actual client use case.
• Continue to improve our story around multi-stream operations. I’m not enthusiastic about “Dynamic Boundary Consistency” (DCB) in regards to Marten though, so I’m not sure what this actually means yet. This might end up centering much more on the integration with Wolverine’s “aggregate handler workflow” which is already perfectly happy to support strong consistency models even with operations that touch more than one event stream.

Wolverine

Wolverine is by far and away the busiest part of the Critter Stack in terms of active development right now, but I think that slows down soon. To be honest, most work at this point is us reacting tactically to JasperFx client or user needs. In terms of general, strategic themes, I think that 2026 will involve:

• In conjunction with “CritterWatch”, improving Wolverine’s management story around dead letter queueing
• I would love to expand Wolverine’s database support beyond “just” SQL Server and PostgreSQL
• Improving the Kafka integration. That’s not our most widely used messaging broker, but that seems to be the leading source of enhancement requests right now

New Critters?

We’ve done a lot of preliminary work to potentially build new Critter Stack event store alternatives based on different database engines. I’ve always believed that SQL Server would be the logical next database engine, but we’ve gotten fewer and fewer requests for this as PostgreSQL has become a much more popular database choice in the .NET ecosystem.

I’m not sure this will be a high priority in 2026, but you never know…

To continue a consistent theme about how Wolverine is becoming the antidote to high ceremony Clean/Onion Architecture approaches, Wolverine 4.8 added some significant improvements to its declarative persistence support (partially after seeing how a recent JasperFx Software client was encountering a little bit of repetitive code).

A pattern I try to encourage — and many Wolverine users do like — is to make the main method of a message handler or an HTTP endpoint be the “happy path” after validation and even data lookups so that that method can be a pure method that’s mostly concerned with business or workflow logic. Wolverine can do this for you through its “compound handler” support that gets you to a low ceremony flavor of Railway Programming.

With all that out of the way, I saw a client frequently writing code something like this endpoint that would need to process a command that referenced one or more entities or event streams in their system:

public record ApproveIncident(Guid Id);

public class ApproveIncidentEndpoint
{
    // Try to load the referenced incident
    public static async Task<(Incident, ProblemDetails)> LoadAsync(
        
        // Say this is the request body, which we can *also* use in
        // LoadAsync()
        ApproveIncident command, 
        
        // Pulling in Marten
        IDocumentSession session,
        CancellationToken cancellationToken)
    {
        var incident = await session.LoadAsync<Incident>(command.Id, cancellationToken);
        if (incident == null)
        {
            return (null, new ProblemDetails { Detail = $"Incident {command.Id} cannot be found", Status = 400 });
        }

        return (incident, WolverineContinue.NoProblems);
    }

    [WolverinePost("/api/incidents/approve")]
    public SomeResponse Post(ApproveIncident command, Incident incident)
    {
        // actually do stuff knowing that the Incident is valid
    }
}

I’d ask you to mostly pay attention to the LoadAsync() method, and imagine copy & pasting dozens of times in a system. And sure, you could go back to returning IResult as a continuation from the HTTP endpoint method above, but that moves clutter back into your HTTP method and would add more manual work to mark up the method with attributes for OpenAPI metadata. Or we could improve the OpenAPI metadata generation by returning something like Task<Results<Ok<SomeResponse>, ProblemHttpResult>>, but c’mon, that’s an absolute eye sore that detracts from the readability of the code.

Instead, let’s use the newly enhanced version of Wolverine’s [Entity] attribute to simplify the code above and still get OpenAPI metadata generation that reflects both the 200 SomeResponse happy path and 400 ProblemDetails with the correct content type. That would look like this:

    [WolverinePost("/api/incidents/approve")]
    public static SomeResponse Post(
        // The request body. Wolverine doesn't require [FromBody], but it wouldn't hurt
        ApproveIncident command, 
        
        [Entity(OnMissing = OnMissing.ProblemDetailsWith400, MissingMessage = "Incident {0} cannot be found")]
        Incident incident)
    {
        // actually do stuff knowing that the Incident is valid
        return new SomeResponse();
    }

Behaviorally, at runtime that endpoint will try to load the Incident entity from whatever persistence tooling is configured for the application (Marten in the tests) using the “Id” property of the ApproveIncident object deserialized from the HTTP request body. If the data cannot be found, the HTTP requests ends with a 400 status code and a ProblemDetails response with the configured message up above. If the Incident can be found, it’s happily passed along to the main endpoint.

Not that every endpoint or message handler is really this simple, but plenty of times you would be changing a property on the incident and persisting it. We can *still* be mostly a pure function with the existing persistence helpers in Wolverine like so:

    [WolverinePost("/api/incidents/approve")]
    public static (SomeResponse, IStorageAction<Incident>) Post(
        // The request body. Wolverine doesn't require [FromBody], but it wouldn't hurt
        ApproveIncident command, 
        
        [Entity(OnMissing = OnMissing.ProblemDetailsWith400, MissingMessage = "Incident {0} cannot be found")]
        Incident incident)
    {
        incident.Approved = true;
        
        // actually do stuff knowing that the Incident is valid
        return (new SomeResponse(), Storage.Update(incident));
    }

Here’s some things I’d like you to know about that [Entity] attribute up above and how that is going to work out in real usage:

• There is some default conventional magic going on to “decide” how to get the identity value for the entity being loaded (“IncidentId” or “Id” on the command type or request body type, then the same value in routing values for HTTP endpoints or declared query string values). This can be explicitly configured on the attribute something like [Entity(nameof(ApproveIncident.Id)]
• Every attribute type that I’m mentioning in this post that can be applied to method parameters supports the same identity logic as I explained in the previous bullet
• Before Wolverine 4.8, the “on missing” behavior was to simply set a 404 status code in HTTP or log that required data was missing in message handlers and quit. Wolverine 4.8 adds the ability to control the “on missing” behavior
• This new “on missing” behavior is available on the older [Document] attribute in Wolverine.Http.Marten, and [Document] is now a direct subclass of [Entity] that can be used with either message handlers or HTTP endpoints now
• The existing [AggregateHandler] and [Aggregate] attributes that are part of the Wolverine + Marten “aggregate handler workflow” (the “C” in CQRS) now support this “on missing” behavior, but it’s “opt in,” meaning that you would have to use [Aggregate(Required = true)] to get the gating logic. We had to make that required test opt in to avoid breaking existing behavior when folks upgraded.
• The lighter weight [ReadAggregate] in the Marten integration also standardizes on this “OnMissing” behavior
• Because of the confusion I was seeing from some users between [Aggregate]which is meant for writing events and is a little heavier runtime wise than [ReadAggregate], there’s a new [WriteAggregate] attribute with identical behavior to [Aggregate] and now available for message handlers as well. I think that [Aggregate] might get deprecated soon-ish to sidestep the potential confusion
• [Entity] attribute usage is 100% supported for EF Core and RavenDb as well as Marten. Wolverine is even smart enough to select the correct DbContext type for the declared entity
• If you coded with any of that [Entity] or Storage stuff and switched persistence tooling, your code should not have to change at all
• There’s no runtime Reflection going on here. The usage of [Entity] is impacting Wolverine’s code generation around your message handler or HTTP endpoint methods.

The options so far for “OnMissing” behavior is this:

public enum OnMissing
{
    /// <summary>
    /// Default behavior. In a message handler, the execution will just stop after logging that the data was missing. In an HTTP
    /// endpoint the request will stop w/ an empty body and 404 status code
    /// </summary>
    Simple404,
    
    /// <summary>
    /// In a message handler, the execution will log that the required data is missing and stop execution. In an HTTP
    /// endpoint the request will stop w/ a 400 response and a ProblemDetails body describing the missing data
    /// </summary>
    ProblemDetailsWith400,
    
    /// <summary>
    /// In a message handler, the execution will log that the required data is missing and stop execution. In an HTTP
    /// endpoint the request will stop w/ a 404 status code response and a ProblemDetails body describing the missing data
    /// </summary>
    ProblemDetailsWith404,
    
    /// <summary>
    /// Throws a RequiredDataMissingException using the MissingMessage
    /// </summary>
    ThrowException
}

The Future

This new improvement to the declarative data access is meant to be part of a bigger effort to address some bigger use cases. Not every command or query is going to involve just one single entity lookup or one single Marten event stream, so what do you do when there are multiple declarations for data lookups?

I’m not sure what everyone else’s experience is, but a leading cause of performance problems in the systems I’ve helped with over the past decade has been too much chattiness between the application servers and the database. The next step with the declarative data access is to have at least the Marten integration opt into using Marten’s batch querying mechanism to improve performance by batching up requests in fewer network round trips any time there are multiple data lookups in a single HTTP endpoint or message handler.

The step after that is to also enroll our Marten integration for command handlers so that you can craft message handlers or HTTP endpoints that work against 2 or more event streams with strong consistency and transactional support while also leveraging the Marten batch querying for all the efficiency we can wring out of the tooling. I mostly want to see this behavior because I’ve seen clients who could actually use what I was just describing as a way to make their systems more efficient and remove some repetitive code.

I’ll also admit that I think this capability to have an alternative “aggregate handler workflow” that allows you to work efficiently with more than one event stream and/or projected aggregate at one time would put the Critter Stack ahead of the commercial tools pursuing “Dynamic Consistency Boundaries” with what I’ll be arguing is an easier to use alternative.

It’s already possible to work transactionally with multiple event streams at one time with strong consistency and both optimistic and exclusive version protections, but there’s opportunity for performance optimization here.

Summary

Pride goeth before destruction, and an haughty spirit before a fall.

Proverbs 16:18 in the King James version

With the quote above out of the way, let’s jump into some cocky salesmanship! My hope and vision for the Critter Stack is that it becomes the most effective tooling for building typical server side software systems. My personal vision and philosophy for making software development more productive and effective over time is to ruthlessly reduce repetitive code and eliminate code ceremony wherever possible. Our community’s take is that we can achieve improved results compared to more typical Clean/Onion/Hexagonal Architecture codebases by compressing and compacting code down without ever sacrificing performance, resiliency, or testability.

The declarative persistence helpers in this article are, I believe, a nice example of the evolving “Critter Stack Way.”