Custom AI Agents for Google Ads Campaign Automation

Marketing agencies often start with Google Ads' built-in 'Automated Rules'. These rules are a good first step, but they are rigid. An `if-then` rule can pause a low-performing keyword, but it cannot first check inventory levels in Shopify or consult a client's specific margin data from a database. The logic is one-dimensional and cannot combine signals from multiple systems.

In practice, this means an agency managing 50 e-commerce clients must handle complex scenarios manually. Consider a client running a 4-hour flash sale. The team needs to increase bids by 30% on specific product campaigns, but only for products with more than 10 units in stock. Doing this with native Google Ads tools is impossible. This forces the team into a painful, error-prone workflow: export data to a CSV, manually cross-reference inventory in another system, calculate new bids, and pray the bulk upload sheet is formatted correctly. This process takes an hour for a task that should take seconds.

Third-party platforms like WordStream or Optmyzr offer more sophisticated recommendations, but they are still closed systems. They cannot incorporate your agency's unique operational logic or a specific client's business data. You are limited to the metrics and actions their platform supports. The structural problem is that these tools are platforms, not frameworks. They are not architected to integrate with your specific business data or execute the custom, multi-step logic that your agency's strategy depends on.

We started by mapping the marketing agency's exact workflow for campaign management. The goal was to understand every manual step, every data source, and the specific logic used for bid adjustments. This audit produced a clear specification document that defined the rules for the automation agent, from how to handle budget pacing to which external client data to pull for performance analysis. This initial discovery phase took 3 days.

We built the core system using Python and the official `google-ads-python` library. The agent runs as an AWS Lambda function on a 15-minute schedule, fetching performance data, checking it against the rules stored in a Supabase database, and pushing changes back to the Google Ads API. This serverless architecture keeps hosting costs under $20/month while providing reliable, on-demand processing. The entire processing for 200 campaigns completes in under 60 seconds.

The delivered system is a set of automated workflows that the agency controls. They received a simple dashboard built on Vercel to monitor the agent's actions and override settings for specific clients. The agency owns the full Python source code, a runbook for maintenance, and the AWS and Supabase accounts. The system removed over 10 hours per week of manual tasks, freeing up the team to focus on strategy, not spreadsheet manipulation.