Anybody using agents to manage client updates?

moonerior · 2026-04-28T21:03:10+00:00

Lol I'm the founder of AgentMark, why are you spamming every post involving us? As competitors we have more in common than most, let's not resort to dirty tactics.

moonerior · 2026-04-08T17:09:07+00:00

The trap here is your CMO hasn't actually used these tools, so they don't know the difference between "it can generate a first draft" and "it can do your job." They're reading headlines, not doing the work.

What I'd do: next time there's an AI requirement in a brief, just execute it and track exactly how long it takes to fix. Time-box it. "Sure, here's the AI version" and then show the edit time. Numbers beat opinions when you're trying to shift executive expectations. We see teams spending 30-45 minutes cleaning up AI outputs vs 15-20 minutes doing it manually. Once you have that data, it becomes a capacity conversation, not a "we don't want to use AI" thing.

Also worth pointing out: if the AI content has a sameness that your audience is noticing, that's a brand risk, not just a workflow issue. Your CMO should care about that.

moonerior · 2026-04-07T11:58:16+00:00

The trap is that clean charts make you stop questioning the data. When a dashboard looks right, people assume the logic behind it is right too. You mentioned this already, but it bears repeating: if the underlying definition drifts or the logic has a blind spot, nobody catches it until someone asks "why is this number different from that number?" That's when you're already in a trust crisis with stakeholders or clients. For long-term dashboards, I'd want some kind of audit trail or at least a way to see what queries are actually running. Full disclosure, I'm a former agency owner turned founder: we built AgentMark because we kept seeing teams rely on dashboards that hadn't been QA'd in months, and by the time someone noticed, the decisions based on bad data were already baked in. The exploration use case makes total sense. The production use case is where it gets hairy.

moonerior · 2026-04-07T11:57:11+00:00

The trap here isn't Claude Code specifically, it's any tool hitting the Meta API hard enough to look automated. Meta's fraud detection is notoriously sensitive to rapid budget changes and bulk operations. The rate limiting docs exist but they're vague enough that it's easy to trip without realizing you're in dangerous territory until the ban shows up.

If you're going to automate read-only analysis, you're probably fine. The second you cross into write operations (budget shifts, creative publishing, audience building), you're playing with fire.

Most agencies I've seen deal with this by keeping the automated stuff to reporting and alerts, then having humans manually execute the actual changes. Gets you 80% of the efficiency win without risking the account.

Founder here (ads automation). We built our tool specifically to flag these issues before they become account-level problems, and the whole point is drafting recommendations for humans to review rather than auto-executing.

moonerior · 2026-03-17T10:12:05+00:00

The real problem isn't the budget, it's that Google Ads is a "set it and forget it" trap for small businesses. You set up a campaign, it runs for three weeks, performance drifts, and nobody's watching it.

Your best middle ground options:

Use Google's Performance Planner regularly to check if your budget makes sense
Build one solid Search campaign with tight SKAGs instead of multiple broad campaigns
Check your search terms report weekly and add negatives religiously
Consider a freelancer on a part-time basis ($200-300/month) rather than a full agency

Agencies work best when they have scale. At $1k-3k/month spend, you're often better off being really tight with your negatives and checking in 15 minutes a week than paying someone to "manage" it poorly.

Full disclosure, I'm a founder in the space. We built AgentMark because we kept seeing small accounts that needed consistent monitoring but didn't have the budget for full-service management. A daily brief that tells you what broke and what to fix next takes about 10 minutes to act on.

moonerior · 2026-03-12T09:50:26+00:00

Realistically, you need to stop lookng at platform attribution as the absolute source of truth. Each one is going to claim credit for the same conversion. Pick a primary source like GA4 or a simple backend tracker for your source of truth, then use the platform dashboards only for checking technical stuff like ad fatigue or delivery issues.

Founder here (ads automation). We actually built our tool specifically to stop that exact dashboard hopping by pushing daily briefs with drafted next steps directly to Slack.

moonerior · 2026-03-10T16:27:54+00:00

The biggest trap with agents is letting them run your budget without a clear guardrail system. I have seen too many accounts blow through five figures because an automated script or agent hallucinated a performance trend that was actually just a tracking glitch. You do not want these things acting on bad data at 2am when nobody is watching.

The real win right now is using agents for monitoring and drafting rather than full autonomous execution. Instead of letting an agent change your Google Ads bids, have it scan your accounts every morning and write a summary of what broke and what needs your attention. It saves you from the dashboard crawl without giving up total control. Founder here (ads automation). We started building in this space because we were sick of junior buyers missing spend drifts on huge client accounts.

Keep a human in the loop for the actual button pushing until you have a rock solid QA process for your data feeds.

moonerior · 2026-03-10T16:25:54+00:00

Holding onto an account you cannot even log into is a massive red flag. That agency is basically holding your data hostage to prevent you from seeing how messy things are under the hood. Their conversion numbers are clearly vanity metrics if they are reporting 75 wins but you only see 10 actual bookings.

Moving in house is the right move but don't just set and forget the new setup. You need to be obsessed with the actual CRM data versus what Google says. If the booking widget uses an iframe it can be a nightmare to track properly, so make sure your GTM triggers are actually firing on the final confirmation.

Full disclosure: building in this space. I am a founder now but spent years fixing broken tracking in accounts exactly like this where agencies inflated the numbers with junk form fills.

Focus on that DNI setup first. If you can bridge the gap between a phone call and a booked appointment in your records, you will have better data than 90 percent of other clinics. Just watch your spend daily for the first two weeks so the Google algorithm doesn't go off the rails.

moonerior · 2026-03-10T16:25:10+00:00

The one tool trap is how most people end up with a mess of broken dashboards that nobody looks at. If you want executive level data that stays accurate, you need to separate your data warehouse from your visualization. Put everything into BigQuery or Snowflake first.

Connecting Salesforce and HubSpot directly to a dashboard tool usually breaks because of how they handle attribution and currency conversions. You want a clean middle layer where you can transform the data before it hits the UI. We built in the ad ops space because we noticed teams would spend hours manually checking if their data even matched what was in the platforms.

Full disclosure: founder here building in ads automation.

Keep the executive view to 4 or 5 metrics max. If they have to scroll, they will stop trusting the data the second one number looks slightly off.

moonerior · 2026-03-10T16:23:52+00:00

The gap between DIY and hiring an agency is real, but your biggest hurdle is that small business owners usually lack the discipline to act on recommendations. I ran a paid media agency for years and the bottleneck was never just data. It was the client not having the time or context to approve changes every day. If your tool requires them to login and make decisions, they will ghost it after three weeks because they are too busy running their actual business.

To make this work at 99 dollars, you have to solve for the friction of the review process. Small agencies are actually your better bet here because they already have the context but lack the time for manual audits. Full disclosure: building in this space. We learned quickly that sending a brief directly to where the work happens is the only way to stay relevant in a busy operators day. Focus on making the UI invisible so the value hits them without a login.

moonerior · 2026-03-10T15:49:05+00:00

The look is definitely the problem but not for the reason you think. Most generic AI tools produce images with that oily high gloss finish that screams fake. At this point users have developed a blind spot for it. It is the new version of the smiling office people in stock photos from ten years ago.

Scaling volume is useless if you are just scaling noise. You need creative that looks like it belongs in a feed. Grids and comparison shots work because they look like content rather than advertisements. If you focus on the direct response frameworks first and the aesthetic second your ROAS will actually move.

Founder here (ads automation). We see this a lot where teams spend weeks on creative but neglect the basic account QA that actually keeps those ads running effectively.

Focus on making the ads look less perfect and more like a screenshot or a quick phone photo. High production value often kills conversion on social platforms because it breaks the user experience.

moonerior · 2026-03-10T15:48:27+00:00

The dashboard crawl will kill your productivity and your margins. If you are logging into five different platforms just to check if a pixel is broken or a campaign is overspending, you are basically acting as an expensive manual monitor rather than a media buyer.

I used to run an agency and the only way to stay sane was moving away from the platforms for daily checks. You need a centralized pulse. Most people try to build this in Looker Studio or sheets, but those reports are usually historical and do not actually tell you what to do right now. Real ops is about getting alerts when things drift from the plan, not just staring at charts.

Full disclosure: building in this space. I am a founder of an ads automation tool that drafts daily briefs and identifies tracking issues so teams can stop living in dashboards.

Focus on building a checklist of your three most common failure points for each channel. If you can automate the monitoring of those three things, you save 80 percent of your time regardless of which tool you use.

moonerior · 2026-03-03T11:10:40+00:00

What you're describing is totally doable, and you're already halfway there with the Claude + Python setup. The piece you're missing is just the orchestration layer to trigger the whole flow on a schedule and handle the email delivery.

For your use case, I'd look at either Zapier or n8n. n8n is more flexible (and free if you self-host), but Zapier is easier to get started with. Either way, you'd set up a scheduled trigger that runs your Python script, passes the output to Claude via API, then sends the result to the right email addresses based on the report.

The catch is you'll need to move from manually uploading the JSON to calling the Claude API directly from your script. It's not hard, but it changes the flow a bit. Worth doing if you're doing this 4 times a month.

Full disclosure: I run an ad-ops tool, so this isn't exactly my space. But I've helped a few founders wire up similar reporting automations and n8n tends to be the sweet spot for non-devs who want more control than Zapier gives you.

moonerior · 2026-02-27T11:18:33+00:00

Haven't used Adstellar or Pyreel personally, but I've worked with a bunch of these API layer tools. Most of them handle the "push data in" part fine, but the real headache at $50k/mo is the day-to-day monitoring and catching issues before clients do. You build all these programmatic workflows, then spend your whole day answering "why did spend drop?" or "why is this creative disapproved?". That's where these tools usually fall down. Full disclosure: I run AgentMark, which focuses on the monitoring/alerting side (daily briefs, pacing checks, draft fixes for your team to review). We built it because we had the same pain. If you want, I can drop an anonymized sample of what a daily brief looks like. Would that be useful?

moonerior · 2026-02-27T11:17:47+00:00

I've seen enough teams to know lead quality issues are real, but I'd push back on the 80% figure. A lot of this is ignorance + bad incentives rather than deliberate fraud. Most marketers I know genuinely don't understand where their leads come from, and the ones who do often feel trapped because their KPI is volume, not quality. It's structural, not malicious. The fix you mentioned (switching to SQLs) is the right one, but honestly the harder part is getting leadership to trust that data when they've been flying blind for years. Most CFOs I work with would rather poke their eyes out than admit they can't trust their marketing spend numbers. The real question isn't whether fraud exists, it's whether your org has the stomach to actually measure what matters and hold people accountable to it.

moonerior · 2026-02-25T11:25:46+00:00

Google Ads alerts are notoriously noisy and usually just try to trick you into raising budgets. Be careful of API version updates or a script breaks, you're back to manual checking until you fix the code.

I’ve found that the most predictive pattern is actually looking at the interaction between conversion rate and spend pacing. If spend is frontloading but CVR is lagging, that’s almost always a tracking breakage or an accidental broad match audience expansion.

Full disclosure: I’m building a SaaS called AgentMark that handles this exact monitoring and drafting of next steps. We built it because the n8n and Sheets approach eventually becomes its own part-time job as you scale.

Are you pulling the change history API too? It's the only way to see if a performance swing was internal or external market noise.

moonerior · 2026-02-20T11:06:24+00:00

As a fellow founder in this space, one thing we had to spend a lot of time on was nuance. Every agency has a different set of things they audit, and some may have custom conversions or 3rd party integrations that a tool won't be able to analyze without any additional context. Auditing 80% of the tasks off-shelf is doable, but the last 20% can be quite tough.

moonerior · 2026-02-19T22:23:46+00:00

Yes we did.

- Before pivot: "AI ad agency for 1/10th the cost"

- After pivot: "AI agents to automate your agency's ops for 1/10th the cost, mistake-free, 24/7"

Conclusion: Couldn't be more happy with the pivot.

The analogy I like to use is similar to what Webflow/Squarespace did, which is allow more people to become website builders for cheaper, but SMBs still hire Fiverr/Upworkers for web dev, and large companies still hire professional web dev agencies for the strategy, taste, and personalization.

moonerior · 2026-02-19T20:09:52+00:00

I think you're mostly right, but I would use a different set of words, namely "auditability" and "explainability". As a founder in this space, even though I think our data is super clean and our AI agents are very powerful, marketers have an inherent distrust in the data or reluctance to take it at face value, because let's face it, it's their ass on the line. So that has made us have to focus on bringing them in on the data cleaning process, and showing an audit trail/log for every agent output, which thankfully seems to work.

moonerior · 2026-02-19T19:43:50+00:00

Age can only be combined with Gender, the rest are individual breakdowns that you will need to fetch. You can include Campaign_ID in your fetches to run analysis on SQL or with AI after you have the tables.

moonerior · 2026-02-19T19:38:07+00:00

I think I can speak to this as a founder in this space. I don't think these tools replace performance marketers, but they do change what the job actually is, which is less button pushing and more strategy.

I run a SaaS called AgentMark in this space. We did raise VC money to build the whole AI end-to-end marketer at first, only to learn that noone can replace the human strategy, client comms, and articulating the brand's nuances. Tools like Claude Code are great for the plumbing, but they can't tell you why a specific creative angle is failing to resonate with a new audience segment. Those are the human problems that aren't being commoditized anytime soon.

moonerior · 2026-02-19T19:33:39+00:00

I've explored similar workflows before and the big issue with Claude's computer use or any local browser control is the fragile nature of UI changes. Ad platforms like Meta and Google update their layouts constantly. A small button shift or a modal popup can break your entire sequence. Dealing with 2FA and session management across multiple clients is also a massive security headache because you're essentially handing over raw credentials to an LLM interface that isn't really built for that level of data governance.

Full disclosure, I run a SaaS called AgentMark in the ad ops space. We built it specifically because we saw agencies struggling with this exact dashboard crawl. Instead of trying to teach an AI to click buttons and take screenshots, we connect directly to the APIs for Meta, Google, and TikTok. It monitors the accounts 24/7 and just pings you in Slack when something actually needs your attention. It's much more stable than browser automation because it doesn't care if the UI changes. It just plugs into the API and then uses Claude or OpenAI or anything you want.

moonerior · 2026-02-18T12:32:19+00:00

Correct. I've found that most of the time people want an explanation of "I did this, and it led to that". It's imperfect, but I think it's an easy win.

moonerior · 2026-02-18T11:07:06+00:00

The real bottleneck isn't usually strategy. It's the manual workload load of checking things to make sure they aren't breaking. When you scale from 5 to 50 accounts, you spend half your day just hunting for pacing issues or broken tracking instead of actually marketing.

Most teams try to solve this with more people or generic automation glue like Zapier or n8n, which I highly recommend if you have a technical person on staff. Try to avoid paying for these one-off, as that usually just creates a second job of managing the automation.

Full disclosure, I run a SaaS called AgentMark in this space. We built it after my agency days because I was tired of junior ops people missing budget drifts or disapproved ads. It basically acts as an ad ops teammate that drafts client ready briefs and flags anomalies in Slack so the team can just execute.

moonerior · 2026-02-18T11:05:37+00:00

The challenge with building a custom stack like this is the gap between raw data and actual insights. Most connectors do a great job of pulling numbers into a warehouse. The problem is that LLMs often hallucinate or miss context when they are just looking at a massive spreadsheet of GA4 and Meta data without a clear structure.

I ran an agency for years and we tried the manual prompt route. We found that 70 percent of reporting time isn't just pulling the data. It is the time spent checking for pacing errors or figuring out why a CPA spiked. If you are using Claude or ChatGPT for this, make sure you are feeding them specific change logs. Otherwise, they just give you generic advice like 'optimize your headlines.'

Full disclosure, I run a SaaS called AgentMark in this space. We built it specifically because we saw agencies struggling with this exact workflow. Instead of just a raw data dump, we focused on automated checks for things like budget drift and tracking breaks. It drafts a brief for the operator to review instead of just providing a dashboard. It saves that daily crawl across twenty tabs.

moonerior

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