Building your agentic stack: A roadmap to real integration

You know that feeling if you’re constructing one thing and the bottom retains shifting beneath your toes? That’s precisely what it is like setting up an agentic AI stack proper now. The GPUs evolve, the frameworks replace, the fashions enhance; all the things’s in fixed flux. But this is what I’ve discovered: some issues stay fixed, and people are the foundations you want to give attention to.

I just lately shared my journey constructing an agentic stack for StartUp Play, an OTT platform aggregator service. Let me stroll you thru what labored, what did not, and what you completely want to know for those who’re venturing into this house.

The enterprise evolution that received us right here

Think about the place we have come from. We began with monolithic architectures, and hey, Prime Video nonetheless makes use of them for monitoring, so they don’t seem to be useless but. Then got here the development: servers, microservices, event-driven architectures, and at last serverless with Lambda features.

Now? We’re within the AI-native period. And meaning including reasoning capabilities, giant language fashions, RAG methods, and agent AI into our current enterprise stacks. The largest problem is not the know-how itself, however the integration. How do you weave agentic capabilities into methods which might be already operating, already serving prospects, already producing income?

The layers that matter (and why you may’t skip any)

Let me paint you an image of what a contemporary agentic stack really seems like. Yes, it is advanced. No, you may’t skip layers and hope for the very best.

Starting from the highest, you have received your API layers: the interface between your brokers and the world. Below that sits the orchestration layer, whether or not that is Kubernetes, microservices, or one thing like LangGraph for workflows. Then come your language fashions (giant or small, relying on your use case), adopted by the reminiscence and context layer—that is the place embeddings reside, the place information graphs present semantic understanding.

The motion layer is the place issues get fascinating. Your brokers want instruments and APIs to really do issues within the real world. And beneath all of it? Data and governance. Because with out correct information dealing with and safety, you are constructing a home of playing cards.

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The microservices mandate

Here’s one thing essential: your microservices have to be stateless. I can not stress this sufficient. Store your state data in Kafka, Redis, Cassandra, or MongoDB -anywhere however within the service itself. This is not nearly following finest practices; it is about constructing one thing that may scale if you want it to.

And talking of scale, let me contact on one thing we achieved: a system supporting a million transactions per second. Yes, you learn that proper. It’s doable, however provided that you architect for it from day one.

Your APIs want clear lifecycle administration. Are they experimental? Stable? Deprecated? This issues greater than you suppose, particularly if you’re iterating quickly.

Database writes needs to be append-only. For reads, leverage caches aggressively. And your information pipeline? It wants schema validation, ETL processes, incremental masses, and backfill capabilities. These aren’t nice-to-haves; they’re important.

The 5 paths ahead

Through trial and error, I’ve recognized 5 distinct approaches to constructing your agentic stack. Let me break them down:

Path one is for groups with current enterprise methods. You’ve received microservices, they’re stateless, and also you’re offloading state to Redis or Kafka. The magnificence right here? Token effectivity. You’re not calling fashions unnecessarily. Maybe you have received a Lambda operate operating for quarter-hour, calling LLMs or small language fashions as wanted. It’s quick to market since you’re constructing on what you have already got.

Path two seems related however with one key distinction: internet hosting. In path one, you host the fashions your self. Path two leverages public cloud suppliers: Google, Azure, AWS. The trade-off? Less management for extra comfort.

Path three introduces MCP (Model Context Protocol) as a separate part. This standardizes your tooling, querying, and useful resource entry. It’s about creating consistency in a world of fixed change.

Path 4 focuses on workflows. Tools like LangGraph allow you to outline states and transitions, calling completely different fashions or brokers based mostly on the place you might be within the course of. It’s highly effective for advanced, multi-step operations.

Path 5 (and that is bleeding-edge stuff)entails agent sandboxes. Think of it like Android apps operating in sandboxes over Linux. Everything’s managed: your information, your file system, your execution setting. This actually emerged final week with bulletins from Enterprise Agent Cloud and Kubernetes North America 2025. I’m optimistic about this method. Imagine agent shops the place builders deploy brokers like cellular apps. We’re not there but, nevertheless it’s coming.

Use instances that taught me all the things

Let me share what we constructed for our OTT aggregator platform. Instead of subscribing to a number of streaming providers, customers subscribe to our aggregator and entry all of them by means of one interface. We constructed fashions for metadata enrichment, suggestions, search, video monitoring, high quality of expertise monitoring, and content material publishing.

Here’s the essential lesson: we constructed this framework three years in the past. The fashions have modified. The frameworks have developed. But the applying information, the interface patterns, the consumer insights we captured? Those are nonetheless gold. The information you accumulate right this moment will outlive any particular mannequin or framework you select.

Our multimodal advice system taught us the worth of flexibility. We use a proxy and a load balancer to route calls between domestically hosted fashions and distant ones. This means we will swap fashions with out disrupting the service. That sort of architectural resolution pays dividends over time.

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Data: the fixed in a world of variables

Let me be crystal clear about this: information dealing with will make or break your agentic system. You want to take into consideration information at three ranges:

1. Session information: What stays inside a single consumer session?
2. Cross-session information: What persists throughout a number of interactions?
3. Long-term information: What turns into a part of your institutional information?

Every piece of information getting into your system wants to be captured and orchestrated. Whether it wants rating, deduplication, prioritization, or growing old, you want a plan. This is not attractive work, nevertheless it’s the muse all the things else builds on.

We experimented extensively with vector databases. LightFM and DeepFM fashions have been giving us sluggish query-to-embedding efficiency. After testing a number of choices, we landed on Milvus for its scale capabilities. For information graphs, we went deep on metadata enrichment, fastidiously designing our node and context buildings.

The construct versus purchase resolution matrix

This is the place issues get strategic. You want to establish what will not change and what’s going to add distinctive worth to your group. Here’s my framework:

Build these parts:

• Your orchestration layer (in case you have microservices, maintain them stateless and add an website positioning layer for distribution)
• Memory structure (Redis or Hazelcast for short-term, Neo4j for information graphs)
• Context routing (that is your secret sauce, maintain it in-house)
• Data pipeline (transformation, schema mapping, deduplication – all important and particular to your use case)
• Governance and security guidelines (domain-specific and essential for compliance)
• Cost optimization and mannequin routing (you want visibility into what’s costing you cash)

Buy or undertake these:

• Large and small language fashions (the open-source ecosystem is wealthy right here)
• Edge inference capabilities (Akamai’s edge inference is game-changing for scale)
• Vector databases (Milvus has confirmed itself)
• MCP frameworks (LangGraph or CrewAI are strong selections)
• DevOps and MLOps platforms (until you may have very particular wants)
• Experimental platforms (Weights & Biases, Comet, or MLflow for mannequin versioning)

The edge inference revolution

Here’s one thing that does not get sufficient consideration: inference would not want to occur in your centralized infrastructure. Edge inference is important for scale. When you are pushing towards that million TPS mark, centralizing all inference turns into your bottleneck. Akamai and CloudFlare are doing unbelievable issues right here. Consider it critically.

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Your integration touchpoints technique

This is about future-proofing. Your stack wants a number of integration touchpoints: API-driven, modular, replaceable. You will change fashions. You will undertake new platforms. If you are tightly coupled to any single part, you are setting your self up for ache.

The takeaways that matter

After constructing and rebuilding these methods, this is what I do know for sure:

First, your enterprise system’s founding pillars nonetheless matter. Scale, reliability, safety; these do not go away since you added AI. They turn out to be extra important.

Second, intelligence have to be woven into your enterprise material, not bolted on. Your agentic structure wants to motive, adapt, collaborate, and, crucially, work with your current methods.

Third, establish your enterprise case now. Not in three months. Not after extra analysis. Now. Use prompts and brokers to construct one thing right this moment. But acknowledge that is simply stage one.

Fourth, construct your workspace by establishing agentic guidelines and buildings particular to your area. This is not about following another person’s playbook; it is about creating your personal.

Fifth, create strong utility workflows that deal with reminiscence, context, and information graphs. This turns into your wealth of knowledge, one thing solely you may create for your particular area.

Sixth, fine-tune relentlessly. Generic language fashions will not lower it. Whether you utilize LoRA, QLoRA, or different strategies, you want fashions that perceive your particular context.

Seventh, spend money on higher inference strategies. Edge-based inference is not non-obligatory if you need to scale. Think Meta-scale, not MVP-scale.

Finally, personal your area. The utility layer, the information, the consumer behaviors, these are yours. They’ll outlast any particular know-how alternative you make right this moment.

The backside line

Building an agentic stack appears like setting up a constructing throughout an earthquake. Everything’s shifting, evolving, bettering. But some issues stay fixed: the necessity for strong structure, the worth of your information, and the significance of constructing for change slightly than stability.

Your utility layer and the information it generates will likely be with you lengthy after right this moment’s sizzling framework is out of date. Build your stack to seize and leverage that worth. Make it versatile sufficient to evolve however steady sufficient to depend on.

The fashions will change. The frameworks will evolve. But the issues you are fixing and the worth you are creating? Those are yours to personal. Build accordingly.

Prathap Chowdry, SVP, Head Product Engineering at Tata Play, gave this presentation at our Agentic AI Summit in London, December 2025.