The AGL Methodology
One AI project manager per client. It holds the context, runs the agents, connects to the tools the client already pays for, and produces human-approved outputs.
This is how we install it and run it. 4 stages. Every stage has a specific output the client can point at. Every stage feeds the next.
Stage 1 · Opportunity
Find the growth opportunity before you build anything.
We start with a read of every system the client already pays for. HubSpot or Salesforce. Google Ads and Meta Ads. Google Analytics. Shopify or Stripe. Gmail or Outlook. LinkedIn. Whatever the stack has, the AI PM reads it.
The output of this stage is a written map of the 3 or 4 workflows closest to revenue that are currently either not running, running late, or running by hand. That map becomes the shortlist.
If you want to see what this stage produces without hiring us, take the free Teardown. It is a stripped down version of what we deliver here.
Stage 2 · Strategy
A human sets the direction. The AI PM runs it.
We do not sell AI-driven strategy. Strategy is a human job. The AGL team sits with the client (or with the client's fractional CMO or founder) and picks 1 workflow off the shortlist to run first. Just one. The workflow has to be measurable inside 30 days. It has to be close to revenue. It has to fit the stack.
The output of this stage is a one-page brief. What the workflow is. What number it moves. Who approves outputs before they ship. What "done" looks like at day 30.
We refuse to skip this stage even for clients who want to move faster. Every failed AI installation we have watched skipped strategy and jumped straight to execution.
Stage 3 · Execution
The AI PM runs the workflow. A human on the AGL team reviews every material output before it ships.
Execution means the AI PM is wired into the client's stack, running the specialized agents behind the scenes, and drafting outputs. The AGL reviewer checks the draft. If it is right, it ships. If it needs an edit, the reviewer edits and the PM learns.
The tools stay. Your team keeps using ChatGPT, Claude, Perplexity, Jasper, whatever they already like. The PM uses them, coordinates them, and holds the context. The team stops re-pasting. The PM already has it.
- The chosen workflow (a follow-up sequence, a weekly report, a lead qualification loop, an outbound batch, a content ops pipeline)
- Human QA on every material output
- Write-back to the client's CRM, calendar, or ad accounts gated behind approval
- A weekly checkpoint with the client's owner
Stage 4 · Performance
Measure the outcome. Compare it to the same 30 days before install. If it worked, add the next workflow. If it did not, we fix it before adding anything.
Every installation ships with a specific measurement. Meetings booked. Replies received. Dollars in pipeline. Deals closed. Content pieces shipped. The measurement is chosen in Stage 2 and locked before execution starts, so nobody can move the goalposts once numbers are in.
This is the discipline that stops scope creep. AI installations fail when teams stack a second workflow onto the PM before the first one is producing measurable results. We do not stack. We measure first, then expand.
Why one PM per client, not one per agency
Cross-client context leaks kill AI installations at scale.
Every client has different tone, different offer, different brand voice, different KPIs, different tools, different stakeholders. If one AI PM tries to hold all of that, the context gets muddied and outputs regress. Every agency we watched try the "one PM for the whole book" model stalled inside 90 days.
The fix is per-client isolation. One AI PM per client. Per-client memory. Per-client credentials. Per-client audit log. Nothing crosses.
Why a human stays in the loop
Because clients pay for judgment, and judgment does not scale automatically.
Every AGL output is reviewed by a human on the AGL team before it ships to the client's system of record. This is not a policy we plan to relax as models improve. It is the reason clients trust us with CRM and ad account write access.
The math still works. One reviewer can QA the output of 10 to 15 client AI PMs in the time it used to take to produce the work by hand. The economics come from the PM doing the drafting. The trust comes from the human doing the review.
What the tools look like at each stage
Where this comes from
We built this after running the loop ourselves. AGL started as an AI-forward marketing agency. We stalled at 8 clients because the coordination tax on tool 11 broke the model. We built the AI project manager to fix it. Now we install it for other agencies, PE portfolio companies, and founders running the same trap.
Trained on $17M+ in client revenue across 200+ founders. Based in Chicago, IL.
Next step
Two paths.
- Take the free 3-minute diagnostic at /sam. It surfaces whether your bottleneck is AI operations sprawl or a marketing problem, and points you at the right resource.
- Get the free Teardown. It maps your stack and calculates the exact dollar cost of your current sprawl.