What to know and build today

AI in paid search isn’t a future prediction; it’s already reshaping how campaigns are managed. 

AI agent tools are now capable of transforming your PPC workflows. 

The question isn’t if AI will change PPC management. 

It’s which tools you should start using now, and what you can build today to make tomorrow’s work faster, smarter, and easier.

Scripts vs. automation vs. agents: Understanding the evolution

Before diving into specific tools, it’s crucial to understand where AI agents fit in the spectrum of PPC management solutions. 

The progression from scripts to agents represents a fundamental shift in how we approach campaign optimization.

Scripts: The foundation

Google Ads scripts and similar tools are predefined code paths that execute specific actions based on fixed conditions. 

They’re powerful for repetitive tasks but limited in scope. They can:

• Execute predetermined logic (e.g., if-then statements).
• Require explicit programming for every scenario.
• Struggle to adapt to unexpected situations.
• Excel at rule-based optimizations, like pausing low-performing keywords.

A classic example of script usage is a reporting script that exports daily metrics into a Google Sheet. 

Possible extensions are focus reports on Performance Max, Demand Gen, or quality score. 

As Google and Microsoft cover hundreds of metrics, there are almost no limits on reporting script ideas.

Another key use case for scripts is automation through action scripts. 

For example, you might set up a script to pause campaigns if ROAS falls below 5, or pause keywords with a quality score under 3. 

These scripts go beyond reporting; they actually perform tasks, taking some manual work off your plate. 

However, they remain limited: their actions are predefined and can only handle specific, recurring scenarios. 

While useful, ad scripts offer minimal flexibility for more advanced or adaptive automation.

Automated rules

Rules let you make changes in your account automatically, based on settings and conditions you choose. 

You can change your ad status, budget, bids, and more. 

For example, you can set a rule to increase your keyword bid whenever your ad drops off Page 1 of the SERPs.

Here’s what automated rules can do:

• Execute simple if/then logic (if cost per click rises above $5, then pause keyword).
• Monitor specific metrics and trigger actions when thresholds are hit.
• Send email alerts when certain conditions happen.
• Save time on repetitive manual tasks.

But automated rules have clear limits. They:

• Only work with the conditions you set up in advance.
• Can’t adapt when unexpected situations happen.
• Need manual updates when your strategy changes.
• Work within one platform at a time.

Automated rules are similar to action scripts, but they are easier to set up and still offer a lot of value for early automation.

Advanced automation is not possible due to built-in limits.

AI agents: The strategic evolution

AI agents represent a new level of automation. 

They’re systems where LLMs dynamically direct their own processes, maintaining control over how they accomplish tasks. 

In PPC terms, agents can:

• Reason through complex scenarios using natural language understanding.
• Plan multi-step strategies across different campaign elements simultaneously.
• Adapt approaches based on changing performance patterns and market conditions.
• Execute coordinated actions across platforms while maintaining strategic oversight.

Think of the progression this way.

Scripts and rules follow recipes, but agents can become the chef. 

They understand your business goals and can craft strategies to achieve them. 

Even better, there are few limitations on agents, which means that you can automate almost everything.

A simple AI agent could be a keyword research agent.

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

How AI agents scale: From single tasks to full-funnel automation

Not all AI agents are created equal. 

Some handle one task really well, while others coordinate multiple systems across your PPC stack. 

Here’s how to think about the different levels – and how to choose the right starting point.

Level 1: Single-task agents

Let’s start with a simple example. 

You build an agent that works with Google Keyword Planner and Google Trends. It can pull keyword data and export everything to Google Sheets.

This looks basic, but it’s already solving real problems. 

• Which metrics matter most? 
• Do you want all keywords or just the profitable ones? 
• Should it filter by search volume or competition level?

Depending on your goals, this could be a weekend project or something much bigger. 

You might add SEO insights or connect to tools like Semrush or Ahrefs.

Level 2: Multi-agent teams

The next step is building a lead agent that manages other specialized agents. Think of it like a marketing manager with a team of specialists.

Your lead agent might have access to a keyword research agent and a campaign builder agent. 

Tell it “create a search campaign for our new product,” and it coordinates both agents to deliver a complete campaign.

One lead agent handles the strategy. The sub-agents handle the execution.

Level 3: Complete marketing operations

Now imagine this approach across all of Google Ads:

Each area has its own specialized agents, all coordinated by your lead marketing agent.

This is where things get exciting. 

• Need to expand internationally? Your lead agent talks to your market research agent and competitor analysis agent for insights.
• Starting a new ecommerce account? It builds search campaigns and shopping campaigns automatically.
• Working on brand awareness? The creative agent makes ads while the demand gen agent sets up campaigns.

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

Current reality: 3 practical paths forward

The possibilities are endless, but there’s a catch: building everything takes serious time and technical skill. 

You need a starting point that gives you quick wins while you learn what works.

That’s why most successful agent implementations start small and grow over time.

Claude MCP for Google Ads: Conversational campaign management

Anthropic’s Model Context Protocol (MCP) for Google Ads represents a breakthrough in how we can interact with campaign data. 

Rather than navigating through multiple dashboards and reports, marketers can now query their Google Ads data conversationally and receive intelligent analysis.

What MCP enables:

• Natural language queries like “Show me which keywords are driving the highest ROAS but have impression share below 80%.”
• Automated analysis of performance patterns across campaigns.
• Intelligent recommendations based on account-specific data and industry benchmarks.
• Seamless data extraction for deeper analysis in other tools.

Building applications or agents around MCP is a first step for much faster analysis and reporting.

Google ADK: Building custom PPC intelligence

Google’s ADK takes customization to the next level, allowing marketers to build specialized agents tailored to their specific business logic and KPIs.

Key ADK capabilities:

• Direct integration with Google Ads API for real-time data access.
• Custom decision-making logic based on your business rules.
• Multi-platform coordination (Google Ads, Analytics, Search Console).
• Automated reporting and stakeholder communication.

ADK is the first big step into automated PPC management. 

As shown before, ADK allows users to quickly build sub-agent structures that are managed by a lead agent. 

Tutorials, templates, and lots of documentation make it easy to start with basic ideas and develop complex systems over time. 

Open-source agent frameworks: Build your own intelligence

For teams with development resources, open-source frameworks like CrewAI, LangChain, and AutoGen offer complete control over agent behavior and integration capabilities.

What these frameworks provide:

• CrewAI: Specialized for multi-agent teams where different agents handle specific PPC tasks (one for keyword research, another for bid management, another for creative testing).
• LangChain: Advanced toolkit for building custom agents with memory, planning, and tool integration.
• AutoGen: Focus on multi-agent conversations and collaborative problem-solving.

If Google ADK is something you want to leave out, there are plenty of other agent frameworks to start with.

When to consider this path:

• You have dedicated development resources.
• Your business logic is too complex for off-the-shelf solutions.
• You need agents that work with proprietary tools or data sources.
• You want complete control over agent behavior and decision making.

The technical reality: What you need to know

Implementation and costs 

Both Claude MCP and Google ADK require technical setup, but the barriers are lower than many marketers expect. 

• Claude MCP involves API access and basic configuration, something most marketing teams can handle with minimal support. 
• Google ADK is more complex and typically requires developer involvement during initial setup, but it offers significantly more customization.

API costs are usually manageable, but the real investment lies in strategic planning and the ongoing refinement of agent behavior. 

It’s also important to note that the success of AI agents depends heavily on data quality and volume – accounts with limited history or poor conversion tracking may see weaker results.

Strategic implementation: Lessons from the field 

The best approach is to start small and think big.

Begin with a use case where manual work is time-consuming but the logic is well-defined, like keyword bid management or budget reallocation. 

Clearly define your success metrics up front; while AI agents can optimize for almost any goal, they need priorities before they’re deployed, not after.

And while agents can execute complex decisions, they still require human oversight. 

Regular reviews, strategic guidance, and override mechanisms are essential for sustainable performance.

The bottom line for PPC marketers

AI agents in PPC aren’t futuristic concepts – they’re active tools creating real competitive advantages today.

The technology is here. The APIs are available. 

The real question is: Will you lead or lag behind?

For those ready to dive in, start with Claude MCP to get comfortable with conversational campaign management. 

As your needs grow, explore Google ADK for more advanced, custom agent development.

A smart starting point? 

Rebuild the automated rules or scripts you’re already using, then expand into routine tasks like keyword research, trend monitoring, or competitive analysis. 

Whatever takes up time and follows repeatable logic is ripe for automation.

Dig deeper: Top AI tools and tactics you should be using in PPC