Building your agentic stack: A roadmap to real integration

You know that feeling when you're building something and the ground keeps shifting beneath your feet? That's exactly what it's like constructing an agentic AI stack right now. The GPUs evolve, the frameworks update, the models improve; everything's in constant flux. But here's what I've learned: some things remain constant, and those are the foundations you need to focus on.

I recently shared my journey building an agentic stack for StartUp Play, an OTT platform aggregator service. Let me walk you through what worked, what didn't, and what you absolutely need to know if you're venturing into this space.

The enterprise evolution that got us here

Think about where we've come from. We started with monolithic architectures, and hey, Prime Video still uses them for monitoring, so they're not dead yet. Then came the progression: servers, microservices, event-driven architectures, and finally serverless with Lambda functions.

Now? We're in the AI-native era. And that means adding reasoning capabilities, large language models, RAG systems, and agent AI into our existing enterprise stacks. The biggest challenge isn't the technology itself, but the integration. How do you weave agentic capabilities into systems that are already running, already serving customers, already generating revenue?

The layers that matter (and why you can't skip any)

Let me paint you a picture of what a modern agentic stack actually looks like. Yes, it's complex. No, you can't skip layers and hope for the best.

Starting from the top, you've got your API layers: the interface between your agents and the world. Below that sits the orchestration layer, whether that's Kubernetes, microservices, or something like LangGraph for workflows. Then come your language models (large or small, depending on your use case), followed by the memory and context layer—this is where embeddings live, where knowledge graphs provide semantic understanding.

The action layer is where things get interesting. Your agents need tools and APIs to actually do things in the real world. And underneath it all? Data and governance. Because without proper data handling and security, you're building a house of cards.

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The microservices mandate

Here's something crucial: your microservices must be stateless. I can't stress this enough. Store your state information in Kafka, Redis, Cassandra, or MongoDB -anywhere but in the service itself. This isn't just about following best practices; it's about building something that can scale when you need it to.

And speaking of scale, let me touch on something we achieved: a system supporting one million transactions per second. Yes, you read that right. It's possible, but only if you architect for it from day one.

Your APIs need clear lifecycle management. Are they experimental? Stable? Deprecated? This matters more than you think, especially when you're iterating rapidly.

Database writes should be append-only. For reads, leverage caches aggressively. And your data pipeline? It needs schema validation, ETL processes, incremental loads, and backfill capabilities. These aren't nice-to-haves; they're essential.