AI-first architecture Why AI agents break in production

It usually starts with optimism. The product works. The backend is stable. Deployments are clean. Then an idea comes up that says “Let’s put some AI in here.” So, a model is connected to the app. A chatbot appears. The demo runs well. In a recent conversation with Shashank Singla, Founder of HCode Technologies & Co-founder & CTO of Playtunes, this familiar pattern surfaced again, along with a hard truth: AI cannot be treated as a feature layer. It must be treated as infrastructure.

Then production comes.

Latency grows; the cloud bills arrive; the AI agent behaves differently on Monday than it did on Tuesday. Logs do not help provide clarity. What started as a feature has turned into what now feels like a rearchitect. This is when teams are forced to learn the harsh reality, an AI-first architecture is a redesign not an upgrade.

AI First Architecture: No Deal Without the Data

The way we used to build AI into our products was like slapping a new layer on top of a pre-existing structure build the product first, then add some intelligence later. That approach doesn't get you very far when things hit the ground in real production environments.

In production, AI agents need context. And context depends on the underlying data architecture. You can't just ignore where you're storing data and hope for the best anymore. It's not just a matter of backend plumbing, it's a strategic decision that will have a significant impact on how your system performs.

So instead of trying to squeeze everything that your system needs into one big model, what often happens is that production systems start routing tasks to different specialized models. So heavy reasoning stuff needs to go one way, structured extraction goes another. And then of course there's real time ingestion and policy enforcement which becomes a fundamental part of your system design.

And then there's the cost factor - which often turns out to be an architectural problem. If you design your workflows poorly, you end up with AI models getting stuck in reasoning loops, burning hundreds of dollars on a single unresolved task. That's not just a model problem, that's a system design flaw.

That is why the approach of AI first architecture forces teams to think about throughput, limits and budget from the very start.

From Single- Agent Demos to What Happens in Real Life

Single-agent demos can look incredibly cool and magical. But when you scale up to a real multi-agent system in production, suddenly it looks just like a distributed systems eng problem. In a real deployment, you might have one agent trying to do some research, another trying to synthesise stuff, a third personalising and a fourth actually executing actions. And as the context grows the outputs begin to drift, latency builds up and then errors start to cascade.

The normal response from the engineers is to just tighten up the state management. So you're no longer just shoveling massive amounts of data at the model like it's a magic prompt factory. No, instead you are now keeping all the state nice and tidy and only passing limited and relevant context to each step.

And there is also another new pattern emerging called Model Context Protocol (MCP) - again the basic idea is that rather than just pushing massive amounts of data into the model, these agents query a database directly. Which of course has the great side effect of reducing hallucinations and lowering your token consumption.

The thing is we are moving pretty fast with the AI agent orchestration and it is starting to look more like classic distributed system design than it does like a chat bot scripting problem.

Automation scales pretty darn fast but thats not the end of the story

AI automation in production is seriously capable. I mean, you can get systems that make thousands of calls an hour, classify intent with ease, pick apart responses, and trigger workflows without needing human intervention.

That is pretty impressive stuff. Not however entirely risk-free. Thats where human oversight becomes a must -have, human in the loop design takes centre stage. Most interactions get automated, but that high-impact stuff gets reviewed by us humans first. AI takes care of the volume, and us humans handle the bits that can go wrong.

For any enterprise looking to roll this out on a larger scale, it is accountability that will shape the architecture, far more so than how big the model is.

Security used to be all about the 'perimeter' but those days are behind us

AI is not just passively reading data anymore, its actively taking action.

That shift in play has moved security into a whole new area of policy engineering. We need to start embedding constraints straight into the prompts or into the APIs themselves. Simple rules can have a lot of weight - dont send an email without getting approval, dont spent more than you've been told to, restrict access to only those who need it.

Some orgs take it a step further by hosting their own open-source models in-house instead of outsourcing to some third party API. That does add to the operational mess, but at least the sensitive data stays within your own controlled environments. All of that gets monitored according to the same principles as site reliability engineering. We get shadow deployments testing out new models in secret against real traffic before we roll it all out. We measure for drift before anyone else needs to.

When AI starts to feel like a real teammate

In one experiment, we had an AI agent right there in the messaging interface, in real time updating a live website in production - and all based on just a few chat instructions from us

The experience of using it felt a heck of a lot less like staring at software and more like giving a colleague some direction and watching it get done That's kinda what it's all about - the shift to where AI is no longer just some extra layer behind the scenes but actually participating in the operation

And that's a pretty clear message for all the engineering teams out there: when you're going AI-first, you need to start thinking at the system level. Treating AI like just another bit of software (never mind the hype around AI agents) is a quick way to create instability - whereas looking at it as the foundation you build everything else on changes just about everything about how you design, deploy and keep things running in production

Now I know all the hype around AI agents can get pretty loud, but the real work of getting the infrastructure right is a lot quieter

But the fact is, in production, the infrastructure not the hype is what ends up calling the shots

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