How we monitor ad performance
The ad-performance agent we run on Kortix — connected to Google Ads and Meta Ads, with alerts in Slack. Every day it checks budget pacing, CPA and ROAS drift, and underperforming ads and keywords, then posts ranked findings and optimization recommendations — it never changes a budget, pauses a campaign, or edits an ad itself.
Paid ad spend drifts quietly. A campaign paces ahead of budget three days into the month. A cost-per-acquisition creeps up for a week before anyone notices the trend line. A keyword that used to convert keeps taking budget from ones that still do. None of it is a single dramatic failure — it's a slow leak across two ad platforms that nobody has time to check line by line every morning.
We run an ad-performance agent on Kortix that reads campaign spend and performance from Google Ads and Meta Ads every day and posts ranked findings and optimization recommendations to Slack. It never touches a budget, a campaign, or an ad — it only ever recommends.
The problem
Budget pacing, CPA/ROAS drift, and underperforming ads or keywords are each easy to catch on their own — if someone is looking. In practice they live split across two platforms with different dashboards, and checking both every day for every active campaign doesn't survive contact with a real workload. By the time a weekly review catches a campaign that's overspent its monthly budget by day ten, or a keyword whose CPA has doubled, the money is already spent.
The usual fixes don't hold up. Each platform's native alerting fires on its own thresholds and only sees its own account. A weekly spreadsheet review is thorough but a week is a long time for a pacing problem or a CPA spike to compound. And automated bid/budget tools that act on their own remove the one thing a marketing team actually wants to keep: the decision.
What we built
On Kortix, a daily cron spawns a fresh agent session. It reads campaign spend and performance from Google Ads and Meta Ads, checks budget pacing against each campaign's monthly target, computes CPA and ROAS drift against trailing performance, flags underperforming ads and keywords and any spend or click-through anomalies, and drafts prioritized optimization recommendations — pause a specific ad, shift budget toward a better performer, add a negative keyword — before posting the ranked list to Slack. It writes nothing back to either ad platform.
How it works
Run on a daily cron
A cron trigger fires the agent once a day. Each firing spawns a fresh session in its own sandbox. One day maps to one run on one disposable machine, so pacing and drift are recomputed from the current state of both platforms every time — nothing carries over.
Give the agent the optimization playbook
What counts as off-pace, how much CPA or ROAS drift is worth flagging, what makes an ad or a keyword "underperforming," and how a good recommendation is worded live as a skill that travels with the agent. When we tighten a threshold or add a new anomaly pattern, it's a change to the skill, not a one-off instruction.
Connect the ad platforms read-only
Through scoped connectors, brokered server-side so no raw token reaches the model, the agent reads:
- Campaign spend and performance from Google Ads — daily spend, clicks, conversions, and cost per acquisition per campaign, ad, and keyword.
- Campaign spend and performance from Meta Ads — the same, across Facebook and Instagram placements.
- Posts to Slack — the ranked findings and recommendations, once a day.
It has no write access to either platform. It cannot change a budget, pause a campaign, or edit an ad.
Set the guardrails
The agent's only actions are reading two ad platforms and posting to Slack. It recommends pausing an ad, shifting budget toward a better performer, or adding a negative keyword — it never pauses, edits, or reallocates spend itself. That decision, and the click that executes it, stays with the marketing team. Credentials are encrypted in the Secrets Manager and injected at runtime, never shown to the model or written to logs.
Post the ranked findings
Each day's message lists what needs attention: campaigns off their budget pace, ads or keywords with CPA/ROAS drifting the wrong way, underperformers worth pausing, and anything that looks anomalous — each with the numbers behind it and a specific recommended action. The marketing team reads it and decides what to actually change.
The pattern
A daily cron spawns a fresh session with read-only connectors into Google Ads and Meta Ads. The optimization rules live as a skill. The agent reads spend and performance from both platforms and writes nothing but the Slack post — every pause, budget shift, and keyword change is a recommendation, never an action.
Guardrails
The agent reads live spend data across two ad platforms, so its access is scoped and one-directional:
- Isolation. Every run happens in its own microVM sandbox. The session can reach only Google Ads and Meta Ads, and only the Slack post leaves the sandbox.
- Scoped secrets. The Google Ads and Meta Ads credentials are encrypted in the Secrets Manager and injected into the sandbox at runtime, never exposed to the model or the logs.
- Recommend-only. The agent has no write access to either ad platform. It cannot change a budget, pause a campaign, or edit an ad — it can only report and suggest.
- Everything is code. The agent's optimization rules, thresholds, and per-platform permissions are files in the repo, versioned and changed through a reviewed change request rather than a dashboard setting.
The outcome
The pacing check, the CPA/ROAS trend, and the underperformer scan that used to require opening two dashboards now arrive as one ranked Slack post every morning, each finding carrying the numbers and a specific recommendation. The agent only reads and recommends; the marketing team decides what to pause, shift, or exclude.
Read more
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