By Satish Thiagarajan, founder and CEO of Brysa
Agentic AI is coming, and it’s not just another incremental step in the tech’s development; it’s the first genuine move towards true autonomy. In MarTech, that means the potential for autonomous advertising. The moment where AI stops merely predicting what might work and actually begins acting on its own to execute and orchestrate campaigns in real-time. It’s what AI was always meant to be. But how can you prepare your business to make the most of it?
What’s the difference between existing AI and agentic?
Most media companies are already using some form of AI, whether for forecasting impressions and building audience segments or generating bid recommendations. And the recommendations it supplies are useful – but that’s where it ends. It gives you an idea of what might work, but then it’s down to your team to optimise and execute. Agentic AI does something different. It doesn’t wait for human action or even decision-making; entirely autonomous, agentic AI identifies solutions, launches them, optimises them, reallocates budgets, and pauses to assess, without any manual intervention. So, while the current iteration of AI might tell you that action needs to be taken, agentic AI will inform you when the change has been made, how it was made, and why.
So, is your MarTech stack ready to make the most of agentic AI? The answer is “probably not.”
Why your Mar-Tech stack won’t cope with agentic AI
Most agencies have a Mar-Tech stack that was designed to store data and report on campaigns. And that’s fine, because up until this point, that’s all it needed to do. But with agentic AI, you need a stack that is capable of acting in real time, across channels, buying platforms, inventory, pricing, targeting, bidding, and optimisation. And there are very few agencies with tech stacks ready to support that level of autonomy. Why? Because of a lack of integration in three core areas.
Data siloing
Agentic AI needs to be able to access data in a unified context. But most agencies still function around multiple data silos. Audience data is separate from campaign outcomes and audience cohorts. Pricing signals and inventory utilisation are on their own individual systems. If agentic AI can’t access the whole picture, it can’t make the right decisions.
Point-to-point integrations
When you onboard new tools, they require their own custom connector and API plumbing, creating brittle links that can break at the slightest change. Autonomous agents can’t operate reliably on a stack that collapses every time a field updates or a platform shifts.
Basic workflows
Right now, most automation is built around basic conditional workflows. Agentic AI requires something more sophisticated, with multi-step reasoning, continuous optimisation, and closed-loop execution.
How to make your business agentic AI-ready
Unify your data architecture
Agentic AI needs a single, consistent, connected view of everything connected to decision-making within your business. That means your audiences, content, channels, performance outcomes, pricing signals, and historical context. Without that, they can’t make confident decisions. Most media companies aren’t ready for that. Data is scattered across BI dashboards, CRM exports, CMS logs, campaign tools, legacy platforms, and publisher systems. And when your data architecture is that fragmented, agentic AI just can’t work.
Focus on integration and automation
Agentic AI needs to be able to communicate and act across all areas of your ecosystem, from campaign platforms and inventory tools to content systems, billing, CRM, attribution engines, activation layers, and audience stores. And that demands a composable, event-driven integration layer that independently supports multi-system operations.
In addition to that, you need to build an autonomous workflow capable of multi-step actions and continuous optimisation. Without that, agentic AI can’t deliver its full potential.
Introduce governance
While agentic AI is all about introducing true automation, it still needs boundaries. You don’t want every decision to be made for you, especially those relating to budget, prioritisation, and content use. So, you need to put guardrails in place to define what agentic AI can and can’t do. The goal is to ensure that all autonomous decisions fit within your brand integrity, legal frameworks, audience expectations, and business strategy.
Before agentic AI becomes a technical consideration for your business, you need to have these pillars in place, creating a strong foundation for modernisation.
The tools to support agentic AI adoption
One of the first steps agencies can take towards agentic AI adoption is the creation of a technological command centre, and this can be achieved through modern marketing cloud systems, like Salesforce and Microsoft Dynamics 365. It provides an operating layer that can support multiple functions.
- Unified data and activation through the centralisation of audience, content, and performance data, providing agentic AI with instant access to the signals it needs.
- Faster creation, deployment, and adaptations, with built-in tools helping to generate segments, journeys, creative variations, and workflows.
- Personalisation and real-time engagement through the ability to tailor content, offers, and placements across channels, based on unified profiles and live events.
- Governance and cross-department alignment through shared records and built-in governance and permissions.
Instead of layering AI on a fractured stack, platforms like Salesforce and Microsoft provide a cohesive foundation where agentic systems can access context, execute decisions, and optimise autonomously.
Agentic AI isn’t just an add-on that can be plugged in to any Mar-Tech stack. It needs a functional, integrated system to build upon. But if you take the necessary steps to prepare that, it could just be the change your agency needs to thrive.
