Serious Talk: Did Agentic AI just made your Data Platform Mission Critical?

So, microservices were supposed to fix everything, remember? That didn't exactly pan out. But you know what? They did teach us something useful about letting different parts of a system do their own thing without some central controller breathing down their neck. Fast forward to now, and we're watching AI agents start to reshape energy trading. It's giving me that same feeling, except this time, we're not just talking about code modules chatting with each other. We're talking about AI making decisions that actually move markets. Which is cool. And also kind of terrifying if you think about it too much.

Here's the thing nobody saw coming: data platforms just became the most critical infrastructure most companies weren't planning to overhaul. And if you're a domain architect in this space, you're probably already feeling the pressure to figure out how these pieces fit together.

When It All Clicks (Or Doesn't)

Imagine an AI agent adjusting hedging strategies on the fly. It's pulling weather forecasts, grid congestion data, real-time market signals - the whole works. Pretty slick, right? Until you realize it just exposed every crack in your data architecture. Most data platforms were built for humans, people who click around dashboards, wait for queries to finish, maybe check Slack while things load. Nobody designed them for AI agents that need to rip through thousands of decisions before anyone's finished their morning coffee, explain themselves to regulators who really don't want to hear "because the algorithm thought it was a good idea," and trust where data came from like their life depends on it (which, legally speaking, it kind of does).

That infrastructure that's been humming along fine for BI and analytics? It's about to find out what stress-testing really means. The challenge isn't just about scale or speed rather it's about trust, traceability, and the ability to coordinate multiple autonomous agents without the whole thing turning into chaos.

Source-Aligned Data Products: Why This Actually Matters

Okay, so here's the interesting bit. We've been building data lakes and warehouses the same way forever i.e. dump everything in one place and figure out the details later. Worked well enough. But AI agents don't work like that. They need data they can find, data they can trust, data that kind of knows what it is. They can't go fishing around in some data swamp hoping to stumble on the right answer.

Enter source-aligned data products. And yeah, I know sounds like consultant-speak ☺️. But stick with me. Think about what's actually happening in energy trading ➡️ market data pouring in from different exchanges, each doing things slightly differently as weather forecasts from multiple providers who never seem to agree on anything or grid data from ISOs and RTOs or your trading positions and risk numbers or regulations that seem to change every other week. Every one of these is its own world with its own rules, its own quality standards and its own weird quirks.

When you build source-aligned data products, you're basically saying ➡️ "Hey, this thing right here? This is the source of truth for market prices. You need market prices? Come here. Don't go wandering around." It's about clarity. About ownership. About not having five different versions of the truth floating around. From an architecture perspective, this means treating each data product as a bounded context with clear interfaces, explicit contracts, and dedicated ownership—principles that should sound familiar if you've been doing domain-driven design.

How This Actually Looks in Practice

There's a pattern emerging across the industry, and it maps pretty cleanly to how we think about distributed systems:

Source System → Data Product → Contract/API → Agent Mesh → Decision 

Looks simple on a slide but gets messy fast in real life. Let's break it down.

Data Products as Bounded Contexts. Each data product needs to know what it is (metadata, lineage, quality metrics), have versions (because breaking an AI agent's expectations at 3 AM is nobody's idea of a good time), be observable (log everything, track everything), and have an owner (an actual person who understands the domain). So like, your "Real-Time Market Prices" product? The market data team owns it. They define what it looks like, they make sure it's good quality, they run the pipeline, they provide the API. An AI agent needs prices? It goes there. Done. No confusion about where the data came from or whether it's legit.

This isn't just organizational more architectural. Each data product becomes a service boundary with its own lifecycle, its own SLA, its own evolution path. You're essentially applying microservices principles to data, which means all those lessons about API design, versioning, and backward compatibility suddenly matter a lot more than they used to.

The Semantic Layer as Translation Infrastructure. Here's a fun problem: one system calls it "LMP", another calls it "locational_marginal_price", another just says "price" and crosses its fingers. Now picture AI agents trying to work together across all of that. Yeah, that goes sideways fast. You need something that maps concepts to actual data products, handles conversions (mixing up MW and MWh is an expensive mistake), keeps a glossary that agents can actually use, and tells you how fresh the data is and whether it's any good.

Usually this means schema registries, ontologies (I know, I know), and solid API contracts. Not sexy stuff. But it keeps agents from doing spectacularly dumb things. From an architecture standpoint, this is your anti-corruption layer, it protects each domain from the implementation details and quirks of others. It's also where you encode business rules about data quality, freshness thresholds, and acceptable variance between sources.

Agent Orchestration as Distributed Coordination. This is where it gets spicy. You've got an agent forecasting renewable generation, an agent watching risk exposure, an agent hunting for trading opportunities, and an agent making sure everyone stays on the right side of regulations. They all need to coordinate without getting in each other's way.

What's working ➡️

• event streams where agents broadcast what they're doing and what they're seeing
• shared state in a fast store where agents can check what's happening right now
• rules about who wins when agents disagree (compliance usually trumps everything)
• and emergency brakes because when AI goes wrong, it goes wrong at speed.

Most places are using Kafka for the event stuff, something like Redis for shared state, and some kind of policy engine to keep order. The exact tools vary, but the pattern's pretty consistent. Architecturally, you're building a choreographed system rather than an orchestrated one as agents react to events and shared state rather than being told what to do by a central controller. This gives you resilience and scalability, but it also means you need really solid observability to understand what's actually happening.

Architectural Patterns That Are Actually Working

The Data Product Mesh Pattern. Forget the giant monolithic data platform. Think federated data products instead, each with someone who owns it (with actual contact info), service levels that someone watches, a contract that doesn't change on a whim, and monitoring so you know when things go sideways. Agents talk to these products through clean APIs. No backdoor database access. No shortcuts. Turns out constraints like this actually make things more solid.

From a domain architecture perspective, this is about applying Conway's Law deliberately. You're aligning your data architecture with your organizational structure and your domain boundaries. Each data product maps to a domain capability, owned by the team that understands that domain best. This isn't just about technology 👉 it's about governance, accountability, and evolution. When something breaks or needs to change, there's a clear owner who can make decisions without coordinating with seventeen other teams.

The Explainability and Lineage Pattern. Every decision an agent makes needs to leave breadcrumbs: what data did it use, what were the numbers, how did it think through the decision, what else did it consider. This isn't just regulatory box-checking (though regulators do like it). It's for when things get weird. When an agent does something nobody expected, you need to rewind and see exactly what it was thinking. Without that trail, you're just guessing.

Architecturally, this means building lineage tracking into the foundation, not bolting it on later. Every data product needs to expose its lineage as part of its contract. Every agent needs to record its decision process as a first-class concern, not an afterthought. You're essentially building an audit log for autonomous decision-making, which means thinking about storage, retention, queryability, and performance from day one. The good news is that modern event streaming platforms and time-series databases make this a lot more tractable than it used to be.

The Progressive Exposure Pattern. You don't just throw an AI agent into production trading. That's asking for trouble. The pattern that's emerging 👉 sandbox where the agent plays with fake data and can't break anything; shadow mode where the agent sees real data and makes decisions but nothing actually happens; limited production where the agent makes real calls but with training wheels on; and full production where the agent earns more freedom as it proves itself.

Each stage needs different data access and different monitoring. Your platform has to juggle all of them at once. From an architecture standpoint, this means building environment isolation into your data products from the start. A data product needs to be able to serve production agents, shadow agents, and sandbox agents simultaneously, with different access patterns and different quality guarantees for each. This is where things like feature flags, traffic shadowing, and canary deployments become relevant for data architecture, not just application deployment.

The Hard Stuff We Need to Talk About

Data Quality as a Safety Mechanism. Humans usually notice when data looks weird. AI agents? They'll confidently make terrible decisions at scale. This changes the whole game: quality checks running in real-time on every data product, automatic shutoffs when quality tanks, backup data sources for critical stuff, quality scores that agents can actually understand and use. You can't just measure quality anymore. You have to tell the agents about it in a way they can factor into their confidence.

From an architecture perspective, this means treating data quality as a runtime concern, not a batch concern. Your data products need to expose quality metrics as part of their API and not just "here's the data" but "here's the data, and here's how confident you should be in it." This requires instrumentation, monitoring, and probably some kind of circuit breaker pattern so agents can automatically fall back to alternative sources or reduce their risk exposure when quality degrades. It's defensive programming applied to data.

Lineage as a Non-Functional Requirement. Data lineage used to be that thing on the roadmap that kept sliding to next quarter. Not anymore. When an agent makes a call that loses money or breaks a rule, you need to trace every single piece of data that went into it. People are tracking where each data product's data came from, which products influenced which decisions, and which agents worked together on what. It's a ton of metadata. But storage is cheap. Fines and losses aren't.

Architecturally, this means lineage can't be an afterthought or a separate system that tries to infer relationships. It needs to be built into the data flow itself. Every transformation, every aggregation, every join needs to record its inputs and outputs. Every agent decision needs to reference the data products it consulted. You're essentially building a provenance graph that spans your entire data platform, which has implications for how you design APIs, how you structure events, and how you think about data product contracts.

The Training and Production Dichotomy. Agents need data to learn. But you can't just let them mess around with real money in real markets. So organizations are getting creative: replay environments where agents can "trade" against historical markets, synthetic data for rare events (waiting for the next crisis to test your agent is not a strategy), and borrowing learning from related domains. Your platform needs to handle training mode and production mode and everything in between.

From a domain architecture perspective, this means your data products need to support temporal queries and point-in-time reconstruction. An agent training on historical data needs to see the world exactly as it was at that moment and not with today's corrections or adjustments applied retroactively. This is harder than it sounds, especially for data products that aggregate or derive from multiple sources. You're essentially building a time-travel capability into your data platform, which means thinking carefully about versioning, immutability, and storage costs.

What's Becoming Obvious

Start with Data Products, Not Agents. It's tempting to jump straight to building cool AI agents. But the places that are pulling this off? They're doing it backwards. Data products first. Clean ownership, clear contracts, solid quality. Then the agents. Turns out agents come together way faster when they've got good data products to work with. From an architecture perspective, this is about building the right abstractions first. Agents are consumers of data products, if the products are well-designed with clear boundaries and contracts, the agents can evolve independently. If the products are a mess, every agent becomes a custom integration nightmare.

Observability as a First-Class Architectural Concern. AI agents will surprise you. They will do things nobody thought of. If you can't see what they're doing and why, you're in trouble. Logging, tracing, monitoring, this is not optional. This is the difference between "huh, that's interesting" and "update your LinkedIn profile."

For domain architects, this means designing observability into every layer ➡️

• data products need to expose metrics about usage, quality, and performance
• agents need to expose their decision-making process
• the orchestration layer needs to expose interaction patterns and coordination failures.

You're building a distributed system with autonomous components, which means you need distributed tracing, correlation IDs, and probably some kind of service mesh for visibility.

Governance as Architecture, Not Policy. Build the off switch early. Stuff will break. Agents will make bad calls. Markets will do weird things. You need to be able to shut agents down fast, undo what you can, and figure out what happened. Building this after you need it is way too late. Get compliance involved early as regulatory stuff around autonomous AI in trading is complicated and still evolving. Companies that bring compliance into the architecture conversations from the beginning are saving themselves a lot of pain later.

From an architecture standpoint, this means building control planes and data planes separately. The control plane handles agent lifecycle, permissions, circuit breakers, and kill switches. The data plane handles the actual data flow and decision-making. This separation lets you make governance decisions quickly without disrupting the entire system. It also means thinking about idempotency, compensating transactions, and rollback strategies from the start as these concepts borrowed from distributed transactions also applied to autonomous agent behavior.

The Real Talk

Look, this isn't easy. We're building something new here ➡️ systems where AI agents aren't just using data, they're active participants in the whole ecosystem. The patterns are still taking shape. Everyone's figuring it out as they go. But here's what's clear ➡️ energy trading is getting too fast, too complicated, too data-heavy for humans to handle alone. The companies that figure out how to build data platforms that AI agents can actually trust and work with? They're going to have a real edge.

Agentic AI isn't coming to energy trading. It's already here. The question is whether the data platforms are ready. And honestly? Most aren't. Not yet. Which also means there's a lot of work to do. And a lot of opportunity for the people who get it right. As architects, we are in a unique position to shape how these systems evolve 👉 to apply the lessons we have learned from distributed systems, microservices, event-driven architectures, and domain-driven design to this new challenge. The fundamentals haven't changed. But the stakes just got a lot higher.

What are you seeing where you are? Is your data platform keeping up with the AI ambitions? Would be interesting to hear what others are running into.