Spotify builds a multi-agent architecture for AI-powered advertising media planning
Spotify's ads business had the same core decisions re-implemented per buying channel and surface, causing behavior to drift over time with no unified intent layer to understand advertiser goals and orchestrate Ads APIs consistently. Advertisers also had to manually configure all campaign dimensions without optimization guidance from historical performance data.
The standard playbook of a new service with a state machine and REST endpoints did not fit because workflows are combinatorial and decisions needed to appear consistently everywhere. The previous manual campaign configuration had complex UI flows, no optimization guidance, slow iteration, and no access to historical performance data.
The Ads AI multi-agent system reduced media plan creation time from 15-30 minutes to 5-10 seconds and cut required user inputs from 20+ form fields to 1-3 natural language messages, with every recommendation backed by historical performance data from thousands of campaigns.
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Frequently asked questions
What did this team achieve with this AI workflow?
The Ads AI multi-agent system reduced media plan creation time from 15-30 minutes to 5-10 seconds and cut required user inputs from 20+ form fields to 1-3 natural language messages, with every recommendation backed by…
What tools did this team use?
Vertex AI, Gemini 2.5 Pro, gRPC, PostgreSQL, Google Cloud, Apollo.
What results were reported?
Media plan creation time (agentic): 5-10 seconds; Media plan creation time (manual baseline): 15-30 minutes; Required user inputs (agentic): 1-3 natural language messages; Required user inputs (manual baseline): 20+ (source-reported, not independently verified).
What failed first in this deployment?
The standard playbook of a new service with a state machine and REST endpoints did not fit because workflows are combinatorial and decisions needed to appear consistently everywhere.
How is this marketing ops AI workflow structured?
Advertiser submits natural language brief → RouterAgent classifies message → Parallel resolution agents extract parameters → MediaPlannerAgent generates recommendations → Optimized media plan delivered.