Event-driven AI - building composable AI systems

Event-driven architecture transforms AI systems from rigid monoliths into flexible, composable services that can be mixed and matched like Lego blocks. Instead of building one massive AI application that does everything poorly, you build small, focused services that do one thing brilliantly and communicate through events.

Here’s the kicker: research comparing architectures found event-driven systems respond 19% faster with 34% fewer errors than traditional API-driven approaches. Yet most companies still build AI monoliths that become impossible to maintain after six months.

The composability advantage

Building AI systems at Tallyfy taught me something counterintuitive: the more you try to integrate everything tightly, the less integrated your system actually becomes. Sounds backwards? Let me explain.

Monolithic AI systems start simple. One codebase, one deployment, one database. Beautiful. Then reality hits. Your recommendation engine needs updating but it’s tangled with your fraud detection. Your NLP service crashes and takes down image processing with it. Six months later, nobody wants to touch the code because changing anything might break everything.

I stumbled across this piece about composable architecture that perfectly captures what we discovered: when you divide AI models into smaller, reusable components, each piece can evolve independently. No more coordinated deployments at 2 AM hoping nothing breaks.

Events create natural boundaries between services. Your fraud detection publishes a “TransactionAnalyzed” event. Your recommendation engine publishes “PreferencesUpdated.” Services don’t know or care who’s listening - they just announce what happened. This decoupling means you can swap out your recommendation algorithm without touching fraud detection. You can scale them independently. You can even run different versions simultaneously for A/B testing.

The magic happens when you realize events are contracts, not integrations. A contract says “when X happens, I’ll publish event Y with data Z.” That’s it. No shared databases. No synchronized deployments. No 3 AM calls because someone updated a shared library.

Think about it: when was the last time you could completely replace a core AI component in production without a massive migration project? With event-driven architecture, it becomes routine. Old service publishes events, new service starts consuming them, gradually shift traffic, deprecate old service. Done.

Why monoliths always win

Let’s be honest - monoliths win at the start. Always.

They’re simpler to build, easier to debug, and faster to deploy initially. One codebase means one set of tests, one deployment pipeline, one monitoring setup. For your first AI proof of concept, a monolith makes perfect sense.

But there’s a tipping point I’ve seen happen at exactly the same stage in every company: when you have more than three AI capabilities in production serving different use cases. That’s when the monolith starts creaking.

Netflix discovered this the hard way. They started with a monolithic recommendation system. Worked great until they needed real-time personalization, content analysis, and viewing predictions all running at different scales with different latency requirements. Their solution? Event-driven microservices powered by Kafka, processing billions of events daily.

The real problem with AI monoliths isn’t technical - it’s human. When your fraud detection team needs to coordinate with your recommendation team for every deployment, velocity drops to zero. When a bug in image processing delays updates to NLP, innovation stops. When nobody understands the whole system anymore, fear replaces experimentation.

Event-driven architecture flips this dynamic. Each team owns their events and their service. The fraud team can deploy hourly if they want. The recommendation team can experiment with new models without asking permission. As long as they honor their event contracts, everything keeps working.

But - and this is crucial - don’t decompose too early. I’ve watched teams split their AI system into twenty microservices before they had twenty users. That’s not architecture, that’s procrastination disguised as engineering.

Implementation patterns that actually work

After years of watching event-driven AI implementations succeed and fail, clear patterns emerge. Not the theoretical kind you read in architecture blogs, but the battle-tested patterns that survive production.

CQRS changes everything for AI

Microsoft’s documentation on CQRS explains the concept, but here’s what it means for AI systems: separate your model inference from your model training data updates.

Your inference service optimizes for speed - cached models, pre-computed embeddings, minimal latency. Your training data service optimizes for completeness - event sourcing, data versioning, full audit trails. They read from different stores, update at different rates, scale independently. One handles thousands of predictions per second, the other processes training updates in batches.

We implemented this at Tallyfy for our workflow predictions. Inference runs on optimized read replicas with sub-100ms response times. Training data accumulates in an event store, processed hourly. Same predictions, completely different operational requirements, perfectly separated.

Saga patterns for multi-step AI workflows

Found this explanation of saga patterns that finally made sense: when your AI workflow spans multiple services, you need coordination without coupling.

Imagine a document processing pipeline: OCR extracts text, NLP analyzes sentiment, classification assigns categories, summary generates abstracts. In a monolith, these run sequentially in one process. One failure kills everything.

With saga choreography, each service listens for events and publishes their results. OCR completes, publishes “DocumentTextExtracted.” NLP picks it up, analyzes, publishes “SentimentAnalyzed.” Classification and summary can run in parallel once they see the event they need.

The beauty? If classification fails, the rest keeps working. If you need to reprocess with a better model, just replay the events. If you want to add translation, just start listening to the right events. No code changes to existing services.

Dead letter queues save your sanity

IBM’s dead letter queue pattern isn’t exciting until you need it. Here’s when you’ll thank yourself for implementing it: when your sentiment analysis service starts choking on emojis at 3 AM.

Every event that fails processing goes to a dead letter queue instead of disappearing into the void. Malformed data, service timeouts, model inference errors - they all get captured for debugging. More importantly, you can reprocess them once you fix the issue.

At one point, our text classification started failing on specific Unicode characters. Without dead letter queues, we’d have lost that data forever. Instead, we fixed the model, reprocessed the queue, and recovered three days of classifications. Customer never knew anything went wrong.

The tools question everyone asks

“Should we use Kafka, Pulsar, or RabbitMQ?”

Wrong question. The right question: “What are we optimizing for?”

Confluent’s benchmarks show Kafka crushing everything else on throughput - 15x faster than RabbitMQ, 2x faster than Pulsar. But raw speed isn’t everything.

Kafka wins when you need maximum throughput and have the expertise to manage it. Uber uses it to handle millions of ride events per second. But Kafka requires ZooKeeper (though they’re removing it), careful partition management, and deep operational knowledge.

Pulsar shines for geo-replication and multi-tenancy. The architecture comparison reveals Pulsar’s storage-compute separation makes it better for dynamic workloads. But it’s even more complex than Kafka - you’re managing Pulsar brokers, BookKeeper, ZooKeeper, and RocksDB.

RabbitMQ just works. No distributed dependencies, lower operational overhead, perfect for smaller scale. If you’re processing thousands of events per second instead of millions, RabbitMQ might be all you need. We started with RabbitMQ at Tallyfy and didn’t need to migrate to Kafka for two years.

Don’t choose based on what Netflix uses. Choose based on what you can operate reliably at 3 AM when production is down.

When not to use events

Event-driven architecture isn’t always the answer. I know that’s not what you expect in a post promoting event-driven AI, but false promises help nobody.

Skip events for synchronous requirements

If your AI inference needs guaranteed sub-10ms response times, events add unnecessary overhead. Direct API calls beat message queues for synchronous, request-response patterns where the caller needs immediate results.

Avoid events for simple CRUD operations

Updating model parameters? Storing user preferences? Basic CRUD doesn’t need events. You’re adding complexity without gaining benefits. Save events for state changes that multiple services care about.

Don’t use events for binary data

Passing large images or videos through event streams is asking for trouble. Most streaming platforms struggle with large messages. Store the data in object storage, pass references through events. Your message broker will thank you.

Skip events if you can’t handle eventual consistency

The CQRS documentation warns about this clearly: read and write models update asynchronously. If your AI system needs immediate consistency across all services, events make this harder, not easier.

Here’s my rule: if you find yourself building complex transaction coordinators to maintain consistency across events, you’re using the wrong pattern. Some problems need ACID transactions. Banking fraud detection during payment processing? Probably needs synchronous validation, not eventual consistency.

The trap many teams fall into: using events everywhere because it’s “best practice.” Best practices without context aren’t best anything - they’re cargo cult architecture.

Event-driven works brilliantly when you need scalability, flexibility, and resilience. It fails miserably when you need simplicity, immediate consistency, or synchronous processing. Choose wisely.

The sustainable path lies in pragmatism: start with a modular monolith, introduce events at natural boundaries, decompose services when teams can’t coordinate anymore. Most importantly, measure everything - response times, error rates, deployment frequency. Let data drive your architecture decisions, not conference talks.

Building composable AI systems with events isn’t about following architectural patterns blindly. It’s about creating systems that can evolve with your business without requiring complete rewrites every eighteen months. Get that right, and your AI architecture becomes an asset, not a liability.