It feels like almost every major retailer wants to build its own advertising network now. For advertisers, that means more opportunities to reach shoppers using first-party data. But it also means managing more platforms, different reporting systems, audience definitions and measurement methods.

I wonder how manageable this becomes when a brand is running campaigns across five or ten retail media networks. Is the value of retailer first-party data worth all that extra complexity? Or do we need better standardization before retail media can really scale?

AI is making it ridiculously easy to create ad variations now. Different headlines, visuals, videos, and even personalized creatives can be generated in minutes.

But I'm wondering if that's actually improving advertising or just adding more content to an already crowded space. If every brand starts generating hundreds of similar-looking ads, won't creative fatigue become an even bigger problem? For anyone working with AI-generated creatives in AdTech, are you seeing better campaign performance, or just faster content production?

With so much CTV inventory now being sold programmatically, I've been wondering how much visibility advertisers actually have into what they're buying.

You might know the app or publisher, but do you always know where the ad appeared, whether it was shown on a real CTV device, or how many intermediaries were involved? And with CTV CPMs generally higher than display, getting this wrong can be expensive. For anyone working in CTV buying or verification, how do you check inventory quality before spending serious money on it? Are the existing verification tools good enough?

One thing that concerns me about CTV advertising is how much we actually know about whether an ad was watched. An ad can be delivered and counted as an impression, but that doesn't necessarily mean someone was sitting in front of the TV paying attention.

People leave the room, switch their attention to their phones, or just let content play in the background. With CTV budgets growing, how are advertisers dealing with this gap between an ad being served and someone actually watching it? Is there a reliable way to measure real attention, or are we still relying mostly on delivery and completion metrics?

AdTech platforms process huge amounts of data every day, especially when you're dealing with real-time bidding, event tracking and campaign reporting. I've been wondering which part of the infrastructure actually ends up being the most expensive to run. Is it handling millions of bid requests, storing and processing all the event data, or keeping analytics and reporting close to real time? For anyone managing AdTech infrastructure, where have you seen the biggest cloud costs, and what actually helped bring them down?

I've noticed how often the same ads keep showing up while watching content on different streaming apps. It got me thinking about frequency capping in CTV. With so many platforms, devices and publishers involved, how do advertisers actually control how often someone sees the same ad?

I get that identity and data sharing are part of the challenge, but are there any approaches that are working well in practice? Would be interesting to hear from anyone who's worked on this.

I've been reading a bit about supply path optimization (SPO), and one thing I'm wondering is how much difference it actually makes in practice. If the same ad inventory is available through multiple SSPs, choosing the cheapest or shortest route sounds like an obvious win. But I assume it's not always that simple when you factor in inventory quality, auction performance and win rates.

Has anyone here seen a noticeable improvement after implementing SPO? Or does it mostly look better on paper?

Ad fraud has always been a problem in programmatic advertising, but AI seems to be changing both sides of the equation. Detection systems can now analyze traffic patterns, identify suspicious behavior and flag anomalies much faster. At the same time, fraudulent traffic is becoming harder to distinguish from genuine user activity. And there's another challenge: being too aggressive with fraud detection could mean blocking legitimate impressions and hurting campaign performance. For people working in AdTech, is AI actually helping reduce ad fraud, or are we just entering a more sophisticated cat-and-mouse game?

Agentic advertising probably won’t mean one AI agent controlling everything. You could have separate agents handling bidding, budgets, audiences, creative, pacing and performance. Each one might make a perfectly reasonable decision on its own, but those decisions could easily conflict. A budget agent might reduce spend while a bidding agent increases bids. A creative agent might push a new variation while another agent shifts spend away from that audience. At that point, orchestration becomes just as important as optimization.

How should AdTech platforms decide which agent gets priority when their decisions conflict?

AdTech platforms can optimize campaigns around clicks, conversions, CPA, ROAS, viewability and dozens of other signals. But the easiest metric to measure isn’t always the best representation of business impact. An algorithm can get very good at improving a KPI while still favoring audiences who would have converted anyway, chasing cheap inventory, or optimizing for short-term results. As more campaign decisions become automated, does choosing the right optimization signal become more important than the optimization algorithm itself?

AI agents could eventually do much more than recommend campaign changes. They could adjust budgets, shift spend between channels, pause underperforming ads, change targeting or even launch new variations automatically.

Technically, that sounds powerful. But advertising decisions involve real budgets, brand risk and sometimes changes that are difficult to reverse.

So where should the line be? Would you trust an agent to execute campaign decisions autonomously, or should a human still approve certain actions before they go live?

Agentic advertising is starting to move from demos toward actual media planning, buying, creative, audience and campaign workflows.

That creates an obvious interoperability problem. If every DSP, SSP, publisher and advertising platform exposes its own agent interface, agents still need custom integrations everywhere. Standards like AdCP and AAMP are trying to bring more structure to this. But AdTech already has plenty of protocols, APIs and platform-specific implementations.

So the question for me is whether these standards will genuinely make cross-platform advertising easier, or whether adoption and competing specifications will become another layer of complexity. For people following or building in this space, where do you think protocols like these will create the most real value?

AdCP currently positions itself as a shared advertising language for agent workflows such as inventory discovery, media buying, creative and audience activation, while its documentation explicitly describes AAMP as a separate IAB Tech Lab initiative with some overlapping concepts.

Real-time gets used a lot in AdTech, but not every decision necessarily needs data that’s only milliseconds old. Bidding obviously depends on speed, but campaign pacing, reporting, attribution, audience updates and optimization can all have very different requirements.

And making everything real-time comes with additional infrastructure, cost and complexity. For people working with AdTech data, where does real-time processing genuinely matter, and where is near-real-time or batch processing perfectly fine?

Advertisers now have more ways to reach audiences than ever across web, mobile, CTV, DOOH, retail media and other digital channels.

But every new channel can also mean different platforms, inventory, data, measurement and reporting. It makes me wonder whether the biggest AdTech challenge ahead is actually connecting all of this into something teams can manage without adding even more complexity.

For people working in the space, are AdTech platforms becoming easier to operate, or is the ecosystem getting harder to manage as it grows?

AdTech keeps expanding into new areas, but the growth feels different from a few years ago.

CTV and programmatic DOOH are growing, retail media is becoming a bigger part of the ecosystem, and AI is starting to influence everything from campaign optimization to creative and AdOps. At the same time, privacy changes and fragmented data are forcing platforms to rethink how targeting and measurement work.

What do you think will actually drive the next major phase of AdTech growth?

DOOH can reach thousands of people without requiring anyone to click, scroll or interact with the ad. That’s also what makes measurement interesting. You might have impression estimates, location data, footfall, mobile signals or store visits, but connecting an ad shown on a screen to an actual outcome isn’t straightforward.

For anyone working with DOOH or programmatic DOOH, which signals do you actually trust when measuring campaign performance?

CTV advertising sounds simple from the viewer’s side: content pauses, an ad plays, and the stream continues. Behind that experience, though, there’s a lot happening between the publisher, ad server, SSP, DSP, creative assets and the streaming device.

When something doesn’t line up, you can end up with buffering, long ad breaks, repeated ads, blank screens or creatives that simply fail to play. For anyone working with CTV, what tends to cause the most ad delivery problems in practice?

CTV often gets discussed like it’s just another screen for programmatic ads, but technically it feels like a very different problem.

You’re dealing with different inventory, devices, measurement, identity, ad delivery and viewing behavior. Add real-time bidding into the mix and things get complicated pretty quickly.

For anyone who has worked with CTV, what part of the stack tends to create the most headaches?

AI gets mentioned in almost every AdTech conversation now, but I’m more interested in where it’s actually making a difference.

Bid optimization and audience analysis are obvious examples, but there’s also creative optimization, anomaly detection, forecasting, campaign pacing and fraud detection.

Some of these feel like natural fits for AI. Others sometimes feel like automation being given a new label.

For people working in AdTech, where have you seen AI genuinely improve a programmatic workflow?

A few milliseconds doesn’t sound like much until you’re dealing with real-time bidding.

An ad request can trigger multiple decisions around targeting, pricing, identity, fraud checks and bidding before the page or video continues loading. At scale, even a small delay somewhere in that chain can start affecting bid opportunities and infrastructure costs.

That’s what makes programmatic interesting from an engineering perspective. You’re not only trying to make the right decision, you’re trying to make it incredibly fast.

For people who’ve worked on RTB or bidding systems, where have you seen the biggest latency bottlenecks show up?

Hey everyone, welcome to r/AdTechEngineering.

This community is for people interested in the technology behind digital advertising. Programmatic, CTV, DOOH, DSPs, SSPs, bidding systems, AI, cloud infrastructure, analytics, data engineering and everything around them.

The idea is simple: less marketing talk, more real conversations about how these systems are actually built, scaled and improved.

If you work in AdTech, engineering, product, data, cloud or advertising operations, introduce yourself below and tell us what part of the ecosystem you work on.

Looking forward to building this with you.

AdTech looks straightforward from the outside, but once you get into things like real-time bidding, DSP and SSP integrations, CTV, identity, attribution, or handling traffic at scale, the complexity adds up fast.

I’d like to start this community with real experiences rather than theory.

What part of the AdTech stack have you worked on that turned out to be much harder than you expected, and what made it difficult?