How to make a Google Search Ads AI agent
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Search is structurally different from other ad channels because the user's intent is explicit in the query. The signal is exceptionally rich, but only if you are reading it at the right level of granularity. Most agents and most human reviewers work at the keyword level. The real signal is at the query level.
Why query-level data matters
A keyword like "project management software" can match to dozens of different actual search queries. Some are high-intent buyers. Some are students doing research. Some are looking for free tools. Their conversion rates vary by an order of magnitude, but at the keyword level they are averaged together and the variance disappears.
Your agent should read search_term_view from the Google Ads API, not just keyword_view. This gives you performance broken out by the actual query the user typed. Build logic to flag any query that has accumulated spend above a threshold with zero conversions. These are negative keyword candidates the agent can surface automatically on every run.
Cross-channel attribution
Your agent should not evaluate Search campaigns in isolation. A Search campaign's attributed revenue looks different depending on your attribution model: first touch, last touch, or multi-touch. A user who clicked a Meta ad two weeks ago and then clicked a Search ad yesterday could be credited entirely to Search (last touch) or split (multi-touch).
The attribution model you choose is less important than applying it consistently across all channels. Every incoming visitor is parsed and identified by traffic source, channel, and campaign. Your model assigns credit to each touchpoint, deduplicated across both paid and organic channels. The key is having campaign-level attribution on all revenue movement metrics: new customers, expansion, churn, contraction, reactivation. A tool like Roadway handles this out of the box, or you build it internally.
This consistency is what allows your agent to compare Search CAC to Meta CAC to LinkedIn CAC in a way that is actually meaningful. Do not rely on any individual platform's self-reported numbers.
Goal, funnel, and guardrail configuration
For Search specifically, the funnel typically looks like: impression, click, landing page conversion, downstream conversion event. Map each step to a measurable metric. Identify what "normal" looks like for each. The agent uses this to localize problems: a drop in downstream conversions when click volume is steady points to a landing page or product issue, not a Search campaign issue.
Guardrails for Search: minimum conversion volume before the agent recommends match type or bid strategy changes (automated bidding strategies need sufficient data, and below roughly 30 conversions per month per campaign they are unstable), maximum CPL ceiling, impression share floor for branded terms.
Memory for Search
Search accounts accumulate history that matters: which keyword clusters have been tested, what negative keyword lists have been built and why, what bid strategy changes were made and what happened, what quality score trends look like over time. Pass this into every agent run. A Search agent with account history avoids re-treading decisions that were already made and understands that a QS drop in a specific campaign in October is a pattern, not a surprise.
Tools and skills
Your agent needs two types of inputs: tools (API integrations that let it read and write data) and skills (markdown files that give it context and decision-making frameworks).
Tools (APIs):
- Google Ads API -
search_term_viewfor query-level data (this is the critical one for Search),keyword_view,campaign,ad_group_criterion,campaign_budget. Write access requiresStandard Accessdeveloper token level. Mutate operations for keyword management, bid changes, budget modifications, and negative keyword additions - Attribution / data warehouse - cross-channel attribution data joined to revenue, so the agent evaluates Search performance against the same model used for every other channel
- CRM - downstream conversion data, deal stages, revenue by acquisition source
Skills (markdown files):
- Query analysis rules - waste detection thresholds (how much spend with zero conversions before flagging), query-to-keyword relevance scoring, when to add negatives vs. adjust match types
- Keyword expansion criteria - what makes a search term worth promoting to a keyword, minimum performance thresholds, match type selection logic
- Bid strategy playbook - when to use manual CPC vs. Target CPA vs. Target ROAS, minimum conversion volume per campaign before switching to automated, re-learning period rules
- Negative keyword taxonomy - existing negative lists, categorization logic, shared negative list management rules
- Quality Score playbook - how to interpret QS changes, expected relevance by campaign type, when a QS drop warrants action vs. monitoring
- Account structure - campaign naming conventions, ad group organization, how the account maps to business segments
The three levels
Monitoring. Query-level waste detection, keyword performance against attribution targets, Quality Score trends (QS drops typically precede CPC increases), bidding strategy stability, and funnel metric health. Runs on a schedule and outputs ranked findings.
Planning. Given the monitoring output, the agent reasons about keyword expansion opportunities from high-performing search terms, match type changes based on query relevance data, bid strategy adjustments based on conversion volume and stability, and budget reallocation between campaigns based on comparative efficiency. All of this is grounded in your cross-channel attribution data and account history.
Action. Negative keyword additions (campaign_criterion or shared negative list), keyword bid adjustments (ad_group_criterion CPC bid mutate), budget modifications (campaign_budget mutate), new keyword additions from search terms, keyword pause/enable (ad_group_criterion status). Write operations require Standard Accessdeveloper token and appropriate OAuth scopes. Same manifest-and-approval model: agent proposes with reasoning, you approve what makes sense, it executes.
How to set it up in Roadway
- Create a new Coworker
- Filter for the channel
- Choose your goal metric (this is what your agent will optimize for)
- Choose the funnel metrics that lead to your goal metric
- Choose your guardrail metrics and define their limits
- Choose your refresh schedule
- Publish
Work with AI Coworker to plan and execute campaigns. Reach out to us if you need any help - happy building: contact@roadwayai.com
FAQ
Why should an AI agent use query-level data instead of keyword-level data?
A single keyword can match dozens of different search queries with wildly different intent. "Project management software" might match someone looking to buy, someone writing a school paper, and someone looking for a free tool. At the keyword level, their conversion rates average together and you lose the signal. Query-level data from search_term_view shows you exactly what people typed, so the agent can flag high-spend zero-conversion queries as negative keyword candidates and identify high-performing queries worth promoting to keywords.
How does cross-channel attribution change Search campaign decisions?
Without cross-channel attribution, Search gets credit for every last-click conversion, even when the user was first exposed through Meta or LinkedIn. This makes Search look more efficient than it actually is and other channels look worse. With a unified attribution model, the agent sees Search's real contribution and can make honest budget comparisons across channels. This often shifts how much budget Search should get relative to other channels.
What is the minimum conversion volume needed for an AI agent to optimize Search?
Below roughly 30 conversions per month per campaign, there is not enough data for reliable pattern detection. Google's automated bidding strategies also become unstable below this threshold. If a campaign is below this volume, the agent should flag it and recommend either consolidating campaigns to pool conversion signal or using manual bidding until volume grows.
How does an AI agent handle negative keyword management?
On every run, the agent pulls query-level data and flags any search term that has accumulated spend above a defined threshold with zero conversions. These are surfaced as negative keyword candidates. The agent also categorizes them (irrelevant intent, wrong audience, competitor terms) and recommends whether to add them at the campaign level or to a shared negative list. Over time, this becomes one of the highest-ROI functions the agent performs because it directly eliminates waste.
Can an AI agent manage both branded and non-branded Search campaigns?
Yes, but they need different configurations. Branded campaigns typically have high conversion rates, low CPCs, and serve a defensive purpose (protecting your brand terms from competitors). Non-branded campaigns are where the acquisition growth happens and where the agent's optimization has the most impact. The agent should have separate guardrails for each: impression share floor for branded, CPL ceiling and ROAS targets for non-branded.