OpenAI’s new tools unlock the next phase of automation

Automation has shaped PPC for decades, and the landscape keeps shifting.

I’ve seen that evolution firsthand, from helping build the first AdWords Editor to developing early Google Ads scripts and writing about automation layering.

Now we’re entering another major transition. 

As AI changes how we search and get answers, it’s also transforming how automation itself gets built. 

And this time, the momentum isn’t coming from ad platforms like Google – it’s coming from AI companies like OpenAI.

Until recently, AI mostly helped with human language tasks like writing ad copy, summaries, or reports. 

But the latest generation of LLMs can increasingly generate computer language too, including the software and workflows that streamline how we work. 

At OpenAI’s DevDay in San Francisco, the company introduced AgentKit, a new way to build AI that can take action.

It marks the start of a phase where the automation mindset that powered PPC optimization can extend far beyond campaigns and into entire workflows.

Imagine if AI could handle your everyday busywork

Picture this:

• A client sends a CSV with weekly results, and before you’ve even opened the email, the file is saved to the right folder and added to your dashboard.
• A client asks for a meeting – AI checks your calendar, drafts an agenda, and schedules it.
• You start writing new ad copy with AI, and the system automatically pulls your brand guidelines and checks for tone and compliance.

This is all possible today, and you don’t need an engineering degree to make it happen. 

If you can define how your work is broken down into distinct tasks, you can create an agent that does those steps for you.

Dig deeper: 4 ways to connect your ads data to generative AI for smarter PPC

What agents really are

An AI agent is a smart helper that can figure out what needs to happen and then take action using connected tools.

Software has historically been built around deterministic steps. If X, do Y, else do Z. It’s predictable, but inflexible. 

And it requires humans to define every possible scenario that should be covered, which makes writing a helpful program time-consuming and difficult.

But just like an LLM is flexible in how it answers your questions, it can use that flexibility to automatically figure out a reasonable next step to complete a task. 

Instead of replying with text, agents can reason through steps, call APIs, and perform tasks.

I’ve explained early versions of this before: 

• You ask ChatGPT for restaurant ideas while planning a trip.
• It suggests a few places.
• It then uses an app like Resy to book the reservation.

That’s what an agent does: it can understand your intent and take a real-world step.

This concept builds on earlier OpenAI features, such as GPT Actions and function calling, which gave models controlled access to outside data. 

Agents are the next evolution – they…