[Webinar] Building the modern Google Ads Automation stack

Last month, I was invited to join Ruben Runneboom for Taskforce Talks.

Ruben and I talked about automated rules, scripts, AI, agents, and how all these things have their place in modern-day PPC. Here’s a link to a recording of the event: https://www.youtube.com/watch?v=Z5f5ioR43m4

Top 10 talking points from the webinar (AI-generated):

  • Agents aren’t new — Nils wrote his thesis on multi-agent systems in 2000; the concept is the same, only the underlying technology has changed.
  • The automation spectrum — There’s a range from simple automated rules → scripts → AI-augmented workflows → fully autonomous agents. Every problem needs the right tool for the job.
  • Simple rules still win for simple tasks — Pausing a campaign at $100 spend doesn’t need an agent; adding AI to deterministic logic introduces unnecessary risk of hallucinations and errors.
  • Scripts as guardrails for agents — Scripts act as structured tools that feed clean data to LLMs, preventing context drift and hallucinated numbers. Agents and scripts are complementary, not competing.
  • SOPs are your competitive moat — Generic AI knows the internet; your edge is the agency-specific knowledge, client context, and best practices you feed into the agent.
  • Brand campaign strategy tip — Split exact and phrase match brand keywords into separate campaigns and avoid Target Impression Share bidding, as competitor keywords inflate CPCs on long-tail branded phrases.
  • Start with read-only access — Connect agents with read-only API/MCP access first, export suggestions as CSVs, review them manually, then gradually expand permissions as trust is established.
  • Privacy, security, safety, and liability — Four distinct risks to consider before connecting any AI to a client account. Never expose developer tokens or refresh tokens; agents are vulnerable to prompt injection.
  • Build agent memory and logging — Log every suggested change, the reasoning behind it, and how to undo it. This enables the agent to learn from outcomes and refine its tactics over time.
  • Know where you sit on the adoption curve — Innovators should experiment freely; early adopters should turn what works into systems; the early majority should operationalize those systems. Agent technology is still in the innovator phase; LLM-assisted ad copy is already early-majority ready.

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– Nils

Author: Nils Rooijmans

Google Ads Performance Architect with a passion for PPC Automation & AI, in particular via Google Ads Scripts.