Agencies are being sold AI from every direction. Most of it is a chat window bolted onto someone else's product. We take a different angle: we are adtech engineers who have spent 11 years building DSPs, SSP integrations and data pipelines, and we now build the agentic layer on top of that infrastructure. That combination matters, because agency automation fails at the plumbing, not at the prompt.
AI strategy for media agencies: decide before buying tools
The first decision is not which vendor to pick. It is which of your workflows are automatable now, which need a custom build, and which should wait for the protocol landscape to settle. We run that analysis with your operations leads: we map the brief-to-reporting pipeline, score each workflow for data readiness and failure cost, and produce a buy-build-integrate roadmap.
Part of that roadmap is a standards timeline. AdCP and AAMP are both moving fast, and we track both through working group participation and IAB events. That means your strategy accounts for what the protocols will support in six months, not just what a vendor demo shows today. If you want the deeper technical picture, see our Buyer Agent and Seller Agent pages.
Agency workflow automation across brief, taxonomy, buying and reporting
Agency operations decompose into four workflows, and each one automates differently.
Brief handling
Agents that parse incoming briefs, extract objectives, budgets and constraints, and produce structured campaign inputs your team validates instead of retypes.
Taxonomy building
Agents that apply your naming conventions and channel structures as executable tools, so campaign setup is consistent across teams and clients.
Media buying
Buyer-agent workflows that discover inventory and negotiate deals over AdCP and AAMP as publishers expose seller agents.
Reporting
Covered out of the box by Arctus, our whitelabel reporting agent that queries your existing warehouse directly.
Reporting automation is where we recommend most agencies start. Arctus integrates with the warehouse you already run, so there is no data migration, and account teams get grounded answers instead of dashboard archaeology. The other three workflows are custom agent builds, and we cover the full development cycle for those: tools, evals against golden datasets, memory, and Langfuse monitoring. Details on that practice are on our AI agents for agencies and brands and AI agent development pages.
Build your own buyer agent, compatible with AdCP and AAMP
Publishers are beginning to expose seller agents, and agentic media buying rewards the agencies that transact with them programmatically over the ones emailing IOs. We build buyer agents that integrate with those seller agents and stay compatible with both AdCP and AAMP.
Dual-protocol compatibility is a deliberate architecture decision, not a checkbox. We isolate the negotiation logic from the protocol adapters, so when one standard adds a capability or the market consolidates around one of them, you swap an adapter instead of rewriting the agent. You are not betting your buying infrastructure on one protocol winning.
Why an adtech engineering firm and not a consultancy
Consultancies deliver decks. We deliver running systems, and we have the receipts.
We engineered Bedrock Platform's DSP end-to-end and run production agents there, not demos. For a brand-creator collaboration platform we took over as the sole development team, modernised the stack, and the client was acquired for $80 million within a year. For an ad-insights engine we shipped a working AWS data pipeline MVP in the first iteration cycle, linking campaign metrics to creative attributes for advertiser dashboards.
That is the difference: when your agentic roadmap hits a warehouse schema problem or a bidder integration, the people who wrote the strategy are the same engineers who fix it.
