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We have all shipped something important only to realize a few minutes after we hit the send button that there was an embarrassing mistake.

• Board deck with an internal consistency error

• Planning model with a formula error

• Financial statement rounding issue

I made a mistake in my OnlyCFO newsletter last week where I accidentally showed Shopify’s stock price falling by over 90% over the past 5 years (in a table with lots of other numbers). It didn’t….Shopify had a 10:1 stock split and my starting stock price was capturing pre-split for some reason. 39,000 of my closest friends saw the mistake….

After being upset for missing a silly error, I realized that I didn’t have the same AI quality checkers for my newsletter as I do in my actual job.

One of my favorite “AI agents" I use all the time is the Finance QA (quality assurance) Analyst.

How To Create a Finance QA Analyst

The Finance QA Analyst has already caught many potentially embarrassing mistakes on board packages and investor updates. I use it every time I put together something important.

If I was using something like this for my newsletter, I would have avoided the embarrassing Shopify price error. I corrected my mistakes and have now implemented a Newsletter QA Analyst to check my work.

*I built my QA Analyst in ChatGPT, but you can do the same things in Claude.

Step 1: Create a Project

Scope projects tightly for the best results. The more you try to combine, the longer the instructions and more chance of AI confusion/errors.

A Board Deck Review project should likely be separate from a Forecast Model project.

Step 2: Feed AI Context

When people complain about the output of AI it is often a context problem. The more context and direction (see next section), the better the output.

Project Sources:

In ChatGPT projects you can upload files or link to Google Drive folders/documents to provide context. There is a lot of context you can provide but below are a few things that I uploaded for my board deck review:

• Metrics Dashboard: confirms all board deck numbers agree to our dashboard

• Company Design Guidelines: logo, colors, formatting rules, and examples so it can keep materials on-brand.

• Board Decks: I provide all prior board decks and have it look at the most recent ones. With this context, the AI agent can compare the current material to other board material. If ARR in Q2 was $52M and now we are showing $51M for Q2, the QA Analyst should flag it and check why previous numbers changed from what was already presented.

MCP Connections:

MCP connections / custom apps are separate from project sources. In ChatGPT, these show up as Apps. Project sources are the files, docs, dashboards, and folders you want ChatGPT to reference inside the project. Apps/MCPs are the external systems ChatGPT can access when they are connected and available in the tools/app menu.

In your Skill/instructions, tell the Finance QA Analyst exactly when to use those connections. Example below:

When reviewing board deck revenue, check the financial report in Snowflake before signing off.

You want to provide fairly tight instructions in the Skill so it doesn’t just go looking everywhere via MCP.

Step 3: Create Your Skill/Instructions

💡 Pro tip: In ChatGPT I put the “Skill” (which ChatGPT just refers to as instructions) in a Google Doc so I don’t have to manually update the Skill/instruction each time. So then as I want to make changes to the instructions I just prompt ChatGPT to update for [X] in the instructions.

In the project settings I just tell it to reference the “Finance QA Analyst Skill”, which is a Google Doc that is linked in the project sources.

Skill/Instructions: Describe what you want the QA analyst to do in your Skill file. Below are a few things I have listed in my Finance QA Analyst:

• Internal consistency in numbers - very embarrassing to have the same metric with two different numbers in the board deck

• Review grammar and spelling

• Review formatting consistency - review prior board material for formatting

• Apply company design - reference our marketing company designs that is linked as a source

• Confirm numbers from source data - call relevant MCPs to confirm data from ERP, Snowflake, etc

For every important metric, tell the QA Analyst which source wins. ARR might come from Snowflake, cash from ERP, headcount from HRIS, bookings from Salesforce, and budget variance from the planning model. The QA Analyst shouldn’t guess. It should flag mismatches and tell you which source disagrees.

Finance QA Analyst Output Instructions:

It is important to define what and how you want the AI agent to provide feedback. Otherwise you will never read it…

In my instruction file I tell it to be concise and order it in the below categories:

• Major issues

• Must-fix before sending

• Data items not verified

• Grammar/spelling

• Open questions

Stop Making Stupid Mistakes

I don’t use AI to write anything for my newsletters. I don’t like how it writes and I enjoy the writing process myself. I do use it for research though. And I am now using it for quality review. Let’s see if I make any obvious errors like the Shopify one in the future…

Anyone putting together important materials (board materials, investor updates, exec presentations, fundraising, M&A stuff, etc) should create a smart AI agent to double-check your work.

Use AI to prevent embarrassing mistakes.

Footnotes:

• Subscribe and forward this newsletter to your team.

• Reply to this email and tell me what AI stuff you want to see

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Just a few weeks ago Anthropic announced Mythos, an AI model so dangerously proficient at cybersecurity hacking that they paused releasing it to the public until large companies and security firms could use it to find their security vulnerabilities first.

TLDR: Anthropic was worried (at least for marketing purposes) that Mythos could disrupt the global economy…

Yesterday they released a safety-modified version of Mythos called Fable 5.

Fable 5 is VERY Powerful

Everyone who has used Fable 5 (me included) reports a major jump in capabilities. And the benchmarks reflect that jump…

Fable 5 is EXPENSIVE

With great power comes great costs…

Fable 5 is 2x more expensive than the previous most expensive model (Opus 4.8).

Currently Fable 5 is included in the subscription plans (subject to existing usage limits). But in a couple of weeks that changes. You will only be able to use Fable 5 with usage credits (pay-as-you-go). Anthropic did say they hope to bring it back to subscriptions soon. Anthropic is compute-constrained and Fable 5 is expensive, so they are limiting usage.

Fable 5’s Privacy Concern (breaking NDAs?)

Using Fable 5 means your prompts and the model's outputs sit on Anthropic's systems for 30 days and can be human-reviewed for safety.

It overrides the zero-retention deals enterprises negotiated. This applies even if your enterprise agreement says otherwise…So uploading anything under an NDA or other confidentiality obligations (M&A, earnings announcements, etc.) would likely put you in breach.

Don’t automatically turn on Fable 5 for all users and make sure users know not to upload confidential information to Fable 5. You could be breaking NDAs…

Three Finance Use Cases With Fable 5

I haven’t used Fable 5 with real data for finance use cases yet given the privacy concern, but below are a few things I am excited about (that I have tested with some dummy data).

1. Drafting S-1s

Gabe, President of Harvey, demonstrated that Fable 5 is a huge improvement for drafting an S-1 with better formatting, structure, and writing.

Similarly, Fable 5 is much better for drafting board memos, board decks, and other long, complex, analytical work.

2. BvA Commentary and Accounting Flux Explanations

A lot of time in both accounting and FP&A is spent researching and documenting financial trends, budget vs actuals, etc. To date, most finance folks have been disappointed by AI’s ability to draft this commentary.

But Opus 4.8 was already saving my team a bunch of time so given Fable’s jump in benchmarks, it should be really good at this.

3. ASC 606 Revenue Recognition Review

Fable 5 is particularly strong with document reasoning and research.

I currently have the below built out using Opus 4.8, which is good. I tested Fable with dummy data and it blew me away. It catches things I would have missed and documents it exactly how I would think a director-level revenue person would…

We created an AI agent that has our ASC 606 policies, customer-specific memos, and historical contract reviews.

1. Reviews draft quotes and flags potential issues to the sales rep before it’s even discussed with the customer.

2. Creates an ASC 606 summary for each contract that documents key terms, rev rec considerations, etc

In Summary…

Make sure your IT team carefully rolls out Fable 5. It’s very expensive and has privacy concerns you want to watch out for.

But…Fable 5 is a major leap on the benchmarks that matter for finance and accounting. Once the privacy concerns are resolved, revisit use cases in finance and if/when Fable should be used (versus a less expensive model)

Footnotes:

• I am opening up sponsorships for the first time for this newsletter (CFOpilot). Reach out now and get amaaazing prices! Email onlycfo@onlycfo.io

• Subscribe and forward this newsletter to your team.

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For this post, I asked Alex Altman (Head of Finance at Coram) to explain how he built a small army of AI agents to automate a lot of his review work and catch things he’d normally miss across finance, accounting, and sales ops. He’s built an agent system that’s keeping his team lean and more productive than ever.

How I Built a Team of Finance AI Agents

—by Alex Altman

At Coram I own finance, accounting, legal, HR, ops, inventory, and shipping. It’s a lot to cover while we’re growing quickly. Instead of immediately hiring more people, I started building AI agents for every major function that was higher risk and took up my time.

It was fairly easy to set up with immediate ROI:

• Output has improved (numbers are more accurate, fewer mistakes, etc)

• I’ve freed up a lot of my time for other things

The agents give me better visibility and confidence into the areas I own but don’t run day-to-day, and they take direct work off my plate where I am the one doing the work.

Each agent watches a specific part of the business, runs on its own schedule, and only talks to me when something needs my attention. They all live in Slack and they’re all connected to our tools - ERP, CRM, AP, procurement, Google Drive, and more.

Here’s what I’ve built so far, and where it’s going.

How these actually get built

Before the agents themselves, the practical part - because this is more accessible than most people think, and there are two paths.

Path 1: MCP connections. You connect Claude directly to your tools through MCP. A finance person can do this immediately, no engineering required. The tradeoff is that each agent queries those APIs live every time it runs, which is slower, can get expensive, and makes it harder to trace a flag back to its source. Great for getting started and proving value.

Path 2: Centralized database. Once you find this valuable, you move the underlying data into a database like Supabase, and the agents read from that instead of hitting each API live. Everything lives in one place, every agent works off a consistent source of truth, and when something gets flagged you can drill straight through to the underlying data. This is what makes the whole thing sustainable at scale.

The build mechanics are the same regardless of path. For each agent you:

• Write a skill file that defines what the agent is responsible for, the data sources it watches, and exactly what should get flagged to you

• Set up a scheduled prompt so it runs on its own cadence

• Tell it to notify you in Slack, or wherever works for you (that instruction lives in the skill file too)

And if the above sounds complicated…it’s not. You just tell Claude and it will do it for you!

One rule that matters: give each agent a clear, narrow responsibility. Don’t try to make one agent do everything - the instructions get muddy and the results get worse. And expect to tune the flags over time, because too many flags is just noise and too few means you’ll miss things.

Building My AI Agent Finance Team

I have segmented my finance AI agents into four categories based on what they do.

1. Watchers (AP, AR, GL, Cash) monitor transactions and operations in real time

2. Analysts (variance engine, financial analyst) compare results against expectations

3. Investigators dig into flags, filter signal from noise, and decide what to escalate

4. Controller routes escalations to the right person and confirms resolution

The AP Agent (watcher)

The AP agent reviews every bill and every credit card expense that hits our accounts. For each one it does three things:

1. Checks Google Drive to see whether the vendor has a contract on file, and flags any new vendor without one

2. Compares the GL coding against how this vendor has been coded historically, and flags anything different

3. Compares the size of the bill against historical bills from the same vendor, and flags outliers

The simplest way to set this up: upload your AP and expense policy, and tell the agent what you want flagged to you.

I get an alert every morning with the day’s bills, what passed, what was flagged, and what the agent recommends for each one.

The first week it ran, it caught a bill from a vendor we’d been paying for months that didn’t have a contract saved in Drive. Filing discipline I’d been meaning to enforce, now enforced automatically.

The AR / Sales Ops Agent (watcher)

The AR agent watches the other side of the business. For every closed-won deal in our CRM it checks three things:

1. That the deal record is clean - a number of fields are supposed to auto-populate from the signed quote, but they don’t always populate correctly, so the agent verifies the numbers tie

2. That an invoice has actually been raised for the deal

3. That any overdue invoices have a documented response or collections plan

It runs every three hours and gives me a daily summary.

It’s a sales ops agent as much as a finance agent, because CRM cleanliness is a constant battle. The agent doesn’t fix the data itself, but it surfaces every issue so I can route it to the right person.

Right when I set this up, it caught four closed deals where line items hadn’t flowed through correctly from our quote-to-cash pipeline. Without catching it, we would have had incorrect revenue, COGS, and ARR downstream. I fixed the deals manually before quarter-end close and sent the underlying bug to our engineer. That one catch alone paid for the time it took to build.

The GL Review Agent (watcher)

This agent watches the books directly. Every transaction that hits the GL - journal entry, expense, bill, anything - gets evaluated against historical patterns and the prior period. It only surfaces transactions that look off: a coding that doesn’t match how similar transactions were handled before, an amount outside the normal range, anything unexpected. The clean, normal transactions never reach me.

This is the cleanest example of how I think about using AI in finance. The accounting itself happens deterministically through the systems I’ve already built. The monitoring happens probabilistically, with the agent watching for anomalies. I look at the flags when there are flags, and otherwise I leave it alone.

The Cash Agent (watcher)

The cash agent watches the thing every founder actually cares about: where the money is and where it’s going. It pulls our bank activity, AP, and collections together and maintains a rolling 13-week cash forecast, so instead of someone rebuilding a cash model every couple of weeks, it’s always current. It flags the things that move the picture - a big outflow week coming up, collections slipping behind, runway dropping faster than expected.

What makes it powerful is that it rolls up into the controller alongside everything else. If the cash agent sees collections falling behind, that’s not just a number on a forecast - it becomes a routed action item to the right person to chase the specific overdue accounts driving it. It connects the “what’s happening to our cash” question to the “who needs to do something about it” answer, which is usually where cash forecasts fall apart.

The Analyst Agents

The agents above all look at transactions as they happen. The analyst agents do something different - they look at results against expectations.

I actually have two, because they do different jobs.

1. Variance Engine Agent. It compares actuals to plan every Monday morning across the metrics that matter (Net New ARR, Gross Margin %, Runway, Burn, etc) and surfaces anything beyond a defined threshold. Those thresholds live in a simple config file I can edit (any metric more than X% off plan, any opex line over $Y in variance, departments more than Z heads behind hiring plan, and so on), so I can tune what’s worth flagging without touching the underlying logic.

2. Financial Analyst Agent. This one doesn’t compare to plan - it studies the actuals themselves and asks “what’s odd here?” MoM and QoQ deltas, gross margin drift, DSO trends, customer concentration, inventory days, mix shifts. It runs monthly after close and produces a handful of numbered observations, each with a quantified change, a hypothesis, and a suggested follow-up. This is the “what does Alex ask his analyst at 7pm on a Sunday” content.

Where the variance engine asks “are we tracking to plan,” the financial analyst asks “what should we be paying attention to that we didn’t think to ask about?”

Both feed into the investigation agent.

The Investigation Agents

Flagging something and explaining it are two different jobs. The analyst agents are good at noticing that hardware margin compressed. But figuring out why requires its own toolset and its own playbook - pull SKU costs, look at average discount and ASP, check for allocation errors. Every domain is like that. So the investigation agent isn’t one generalist. It’s a set of specialists, each with its own analytical playbook, and it routes a flag to whichever specialist is right for it.

The proof it works: on its sixth run, the working capital AI agent self-reported a data gap. It said “inventory data missing from working capital pack” and stopped. That’s exactly the right behavior - a specialist using its tools, finding the data wasn’t there, and being honest about it rather than making something up.

The Controller Agent

This is the layer that turns a pile of agents into an actual team. The watchers and analysts all generate flags, the investigator decides which ones matter, and the controller takes it from there - it routes each escalation to the right person and owns the follow-through. CRM cleanliness issues go to sales ops, billing issues go to our accountant, AP issues go to our admin, and so on. It tags the person in Slack, makes the ask clear, and then actually checks back to confirm the work got done instead of letting it disappear into a backlog.

Once this went live, I stopped being the router. Before, I was the one reading every agent’s output, deciding who needed to act, and chasing it down. Now I only get pulled in when something genuinely needs me. The controller handles the coordination layer that used to eat a surprising amount of my week - and nothing slips, because following up is the agent’s job.

The Finance AI Agent Stack

Once you put all of these AI agents together (watchers, analysts, investigators, and controller), you create an AI team that does meaningful work. Your numbers and accuracy improve. Things get done faster because the agents work 24/7. There are more controls as agents ensure your policies are followed.

Start building AI agents now and you’ll quickly see the ROI. They won’t get everything perfect the first time (just like a new hire), but continue to refine the skills and they will get better.

Footnotes:

• I am opening up sponsorships for the first time for this newsletter (CFOpilot). Reach out now and get amaaazing prices! Email onlycfo@onlycfo.io

• Subscribe and forward this newsletter to your team.

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CFOs Are Reviewing AI Model Costs

The average business is spending 13x more on AI tokens than in January 2025, according to Ramp’s spend data 🤯. AI is quickly becoming the largest vendor expense on your P&L and it may soon (if not already) crush your gross margins as well.

Internal AI model adoption is being pushed harder than ever (tokenmaxxing) and everyone is releasing more AI products with worse gross margins. Growth on AI spend has gone exponential in the last few months.

The size of our AI model (chatgpt/claude) bills have gone from nothing to pretty massive in a short period of time. And now every CFO should be paying close attention.

In this post, I will share some of the things that companies can pretty easily do to slash their AI bills.

AI Model Pricing

The two charts below show the different AI model pricing for the two way communication that a user has with AI:

1. Input tokens: Your prompt, convo history, attached files, tool definitions, etc

2. Output tokens: What is returned (text, file, etc), tool calls made, code returned, etc

The big take away is that the “smartest” AI model is 5x+ more expensive than the cheapest model.

You should also understand that there are three ways that AI model companies will bill enterprise licenses:

1. Seat-based pricing: flat fee paid annually upfront for the privilege of simply accessing the platform (~$18/user/month)

2. Claude/ChatGPT usage: billed monthly in arrears based on actual usage (on top of the seat-based pricing) from the rates noted in the charts above

3. API calls: Paid upfront on a credit card. This is for your product that you sell or for internally built apps that are hosted.

How We Cut AI Model Costs By 40%

TLDR: You don’t use a heart surgeon to take your temperature. Similarly, you shouldn’t have your CFO reviewing T&E expense reports.

This is the primary problem at most companies.

They are using the most expensive model (or even the 2nd most expensive model) when they could be using a cheaper one that provides results that meet their needs. There is HUGE potential savings by simply using the right model.

Let’s say you have a $100K/month bill for Claude:

You currently use Opus 4.7 (the most expensive model) for everything, but if the company was to only use the model it actually needs then it could look something like the below:

Opus is rarely needed for every task. So if users shifted to using Sonnet and Haiku when appropriate then the company would save nearly 50% ($576K per year)…

We want everyone using AI and adopting into all their workflows, but they don’t need the most expensive AI model for every task. When the Anthropic bill was small, no one cared. But as the costs have grown, there is significant ROI in making sure you are using the least expensive model needed to get the job done.

Most folks on an enterprise usage plan are paying premium rates (the heart surgeon) for basic responses (checking your temperature).

How Do You Actually Do it?

There are two different paths based on how AI is used and priced:

1. Enterprise subscriptions/usage

2. API calls

#1 is where almost all your internal AI usage comes from.

Enterprise Subscriptions/Usage (internal AI usage)

You have fewer controls here because users can choose which model they use, but there are a few things you should do that will save a lot of money.

Below are three things that every company should do:

1. AI Admin Controls

The Claude/ChatGPT admin should set the default model to the middle tier (e.g. Sonnet for Claude). If you do that, then 90%+ of users will just use that model. While they usually won’t be using the most expensive model, they will also almost never downgrade to the cheapest model. But just doing this may save you up to 40% (price difference between Opus and Sonnet).

Benioff said they will spend $300M on Claude this year. And I have also heard from various people that everyone just defaults to Opus for everything. They could probably save $100M+ by using cheaper models when appropriate…

User Usage Limits: Obviously you should put controls on users so they don’t create something that just blows up AI usage. Admins can put dollar limits by user (e.g. engineers get $5K per month while finance gets $1K per month)

The below was just reported by Axios 😱. Maybe not entirely true (or missing some details), but you MUST have spend controls on your AI stuff so this doesn’t happen to you.

2. Admin Manual Tracking

Admins get dashboard reporting that show what each user is spending and what models they are using. If someone is burning a ton of tokens using the most expensive model on easy tasks, then the admin should have a conversation with those users and encourage them to use something else.

Don’t decrease AI usage…but choose your AI models more wisely.

3. Employee Education

Make sure employees understand the cost difference between these models. Many just don’t know. Forward them this article :)

There are also several other things (besides different AI model costs) that you can teach employees so they spend significantly less on tokens. For example:

• Use a new chat window for new tasks. Each time you prompt in the same chat window it re-reads the entire chat history. This gets expensive quick (especially if you are on a premium model). I have seen people just default to the same chat window every time they go to AI…

• Be upfront with everything you need in one prompt. Back-and-forth exchanges compound quickly. Every reply re-reads the full history. Give Claude the full context and ask for exactly what you want in one shot.

• Update the AI model in your automations, agents, skills, etc. A lot of scheduled tasks are structured and repetitive (often fine for a cheaper model). Users can define which AI model to use in agents/skills/automations. Test out a lower tier model and see if it works fine.

API Calls (internal apps and your AI products)

API calls happen when you aren’t in the ChatGPT/Claude app. It is when you build an internal app or customers use your product and it calls an AI model for something.

There are more options here to control what AI models are used in the response, which is great because you will want to control it to improve AI gross margins.

Model Routing: directs AI requests based on a defined criteria to the right AI model

The goal is to use the cheapest passing model for each task. “What is the minimum requirements we are OK with to check someone’s temperature and can they do just as good a job as the heart surgeon?”

I talked to one CFO that saved a few percentage points on their AI gross margins after they switched from the most expensive AI model embedded in their product to a model routing system that switched a lot of tasks to a cheaper model.

While a well-structured model routing process is going to have the highest ROI, there are other ways to also cut down costs on AI API calls. Some examples I have done:

• Analyze your actual usage: Anthropic’s console shows token usage by API key. You can also tag calls by feature or workflow and see exactly what is driving costs. Do this so you can figure out how to make the expensive stuff more efficient

• Batch API: If a task doesn’t need a real-time response, you can use Anthropic’s Batch API and save 50%. A lot of stuff doesn’t need to be real-time

• Prompt caching: If you’re sending the same large context on every API call (a system prompt, a reference document, a knowledge base), then you’re paying full input price every single time. Prompt caching stores that content so repeat calls cost ~90% less on the cached portion. Make sure your engineering team built this…

Final Thoughts

AI spend has become very large in the last few months. It’s time for CFOs to pay attention. We want to drive AI adoption. It will likely be the difference between companies that survive and those that die in the AI world.

But…

It doesn’t mean we should just light money on fire and throw all of our investors’ money to Anthropic/OpenAI. It’s time to add more process and educate employees so you can cut your largest vendor bill in half.

Footnotes:

• Email me and tell me what you are building. Or reach out with questions!

• Subscribe and forward this newsletter to your team.

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AI Stuff From The Week:

The below tweet blew up because I think it feels so real for all finance leaders right now… This was me reviewing the April usage bill lol.

And it’s why I spent the last two weeks pushing all the things I wrote in this article!

Hearing many cases of people double-clicking into the ROI of their AI spend. It’s not a question if AI is valuable (it obviously is), but rather is how much they are spending and how they are spending it have decent ROI.

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AI Agents for Accounts Receivable

I built an accounts receivable AI agent in <10 minutes. It worked surprisingly well right out of the gate. And it’s only getting better as I fine-tune it.

AR/collections is a near-perfect use case for AI…incredibly boring, repetitive, and requires context from multiple places.

A lot of finance folks think it’s too hard or it requires a lot of technical expertise to build this, but here are some facts about my AI agent:

• Built it myself (with ChatGPT) in <10 minutes

• Didn’t need to build an app for it

• No separate API calls or complex integration work (all within ChatGPT/Claude)

• Was immediately useful. And is getting better every day.

• I control what gets just drafted to my email versus auto replies. Everything started as email drafts that we reviewed first.

Save your AR/collection team hours every week and keep reading 👇

The Collection/Billing Problem

Invoices get automatically sent from the ERP. And then the ERP has automatic “dunning” reminders for unpaid invoices that look something like the below.

Hello Customer - Your invoice [xxxx] is past due by 30 days. Please provide payment as soon as possible.

But humans have historically handled all the subsequent back and forth with customers, which can take a lot of time…

• Customer: “Can you send me the contract and all the outstanding invoices?”

  • Accountant: Open ERP, find relevant invoices, download invoices, attach them to email. Then open up the CRM, download the relevant contract and then write an email response.

• Customer: “Can you send me your W9?”

  • Accountant: Grab latest W9, attach it, and email a reply

• Customer: “We will pay the invoice next Friday.”

  • Accountant: Read email, updates the collection tracker, turns off automatic dunning notifications for that customer, email a reply that says, “Thanks! I’ll look out for it”

This is where my AI Accounts Receivable agent comes in….

How I Built My Accounts Receivable AI Agent

The Context Layer

The main reason building this only took me 10 minutes is that I already had ChatGPT connections (via MCP) to all the necessary tools. If your AI model doesn’t have context, then you or your IT team needs to do that immediately. You are greatly behind already if you haven’t done this step yet…

The Initial Prompt

I built the AR agent in ChatGPT’s Codex. Below is my initial prompt.

Prompt: I want to automate billing/collections emails with AI draft replies. The model should already be connected to my email, CRM, ERP, and Slack. Let’s build it so it runs via Codex (not a local or hosted app) and it triggers every 10 minutes via automations so email drafts get pushed to Gmail frequently. There should be controls, guardrails, and email best practices and styling rules that I provide. I want to run the prompt as efficiently as possible. Suggest what to add so I follow best practices.

After the above prompt, ChatGPT asked me a few questions to make sure it was doing the right thing and then it created an “Automation” (see below)

There is no separate app. No need to do additional API calls or other wiring. It’s all done via ChatGPT and the connections that should already be in place.

It’s a simple and very effective way to get started. And probably works perfectly if collections is handled by one person. While you may outgrow this solution eventually, it can work for a long time and it will help create the framework if/when you build an internally hosted app (which adds a lot more complexity). Or it will help you decide what you need if you buy a dedicated tool from a vendor.

How It Works

The AI Wiring

• Trigger: “Cron” automation in ChatGPT runs every 10 minutes (while your laptop is open) and fires off instructions.

• Automation Instructions: Below are the simple instructions that live in the Automation tab. This initial prompt used call all the other detailed instructions that live in the project folder (which are on my desktop).

💡 Pro Tip: In the Automations settings you can change your AI model. For something that doesn’t need to be super smart (like collection emails) you can change the model to a less expensive one.

• Remaining Instructions: You could put all your instructions in one super long file in the Automation prompt, but that isn’t efficient and it’s hard to track/update. So it’s usually better to break it out. Below are a few different files I have for this:

  • Email Style (.md file): I provide some general instructions like “Keep it concise and informal.” And then I wrote, “Use the ‘approved replies’ email tag in Gmail for examples to reference when drafting emails”. This means I can improve AI drafting by crafting email responses that I like and tagging the email to “approved replies”. The AI is now instructed to use those emails as examples.

  • Guardrails (.md file): This is where I document all the rules I want the model to follow. Like “Never send emails automatically. Only draft them into Gmail.” or “Only use the ERP and the facts I provide as a source of truth when drafting emails”.

  • Runbook (.md file): This file defines when to draft a reply. Like only draft a reply if it relates to collections or billing (don’t draft replies to spam or non-related emails).

Improving AI Responses

Below was a pure AI response automatically drafted to Gmail on an example I set up. The AI model got invoice information from the example in QuickBooks and it grabbed the W9 and invoices from a Google Drive folder (via MCP connector) after confirming the email address was a contact in our ERP.

The above AI draft already saved me a bunch of time. It listed out invoices (and attached them all), attached my W9, and drafted the response. But I wanted to change one thing…so I edited it to show the days overdue next to each invoice. I then added this response to the “approved replies” Gmail label so that the AI model will use this as an example for the next similar email.

And it did. The next AI email draft response included days overdue…

And if there are other rules, guardrails, style, etc that I want to permanently add later, I will just go into the ChatGPT project and tell ChatGPT what I want and then it will update the necessary files. Easy.

Should You Have AI Auto Reply?

Not at first. Definitely not.

And for certain things, I would never have AI respond automatically. But it certainly can make sense in many circumstances (like a simple request for a W9, invoices, outstanding balances, etc) with the right guardrails in place.

But even if you don’t want to take that risk yet (I totally understand), you can have AI push the email drafts right to your email so replying is 10x faster.

Final Thoughts

The setup I have described is fast and works great when one person is managing collections. But unless you have a large volume of customer billing, one person + AI can probably manage collections for a really long time now.

However, when you need to move beyond one person managing AR emails, then this set up breaks since it is dependent on one person’s laptop and their ChatGPT account.

Building an internally hosted app adds a lot more complications (API calls, security, team-based roles and access, etc). I see a lot of people trying to jump immediately to internally hosted apps when they are working with AI because they think they have to. And then they stop because of the effort and continued maintenance required with complicated hosted apps.

If you decide you need an full-blown app then make sure it’s worth your time. This is the real build vs buy territory. And building an app (and maintaining it) is often still not worth your time. There are lots of great vendors who provide AR AI agents for companies that need more functionality and features.

Start simple.

Footnotes:

• Reply to this email if you have any AI use cases you want to share! I’d love to hear what you are building and doing in finance or accounting

• Subscribe and forward this newsletter to your team. It will help them leverage AI and be more efficient

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My 6-Step Process to Build an Accounting AI Agent

I have been deep building with AI for the past year. Every person (leaders included) needs to be building with AI. It’s important to understand how to use AI, what to expect of your team, and how to rebuild your org for the age of AI.

I built our first AI agent to tackle accounting reconciliations (focused on prepaids initially)

Here is the process I ended up with…

An “agent” is essentially the worker that navigates all of these systems and steps to complete a task.

Step 1: The “Skill” Itself

Skill: A self-contained workflow an agent runs. A system prompt that defines what to do. MCP connectors to the systems it needs. A trigger that kicks it off. A config that drives the behavior. You build it once and it runs forever.

Every skill has a SKILL.md file. The skill defines the inputs, workflows, rules, output, edge cases, etc. My prepaid skill is ~200 lines.

You’re not writing a prompt every time you run it. You’re writing it once. And then updating it if/when something changes.

Step 2: The Config File

My config (yaml file) contains:

• Prepaid GL account codes

• Vendor → coding map

• Materiality threshold

• Partial-month convention

• Routing: who gets pinged, in which channel

When something changes, you edit the config (not the skill).

This is one thing I was doing wrong before. I was dumping everything into a prompt (or a skill). Every time something changed I would have to update the skill and re-do it.

Having a separate config file with the stuff that might change breaks things up so it’s easier to maintain. It’s like why you don’t put your entire model and all the components in one giant tab or make Excel model formulas huge.

Putting everything in one skill file also makes it so you can’t reuse your skill in other recons, entities, etc because it contains the exact detail for one prepaid recon process. To make it scalable/reusable you separate out those details into the config file. Then you can reuse the skill and just update the config file.

Step 3: MCP Connector to the ERP

MCP (Model Context Protocol): A standard way for an AI agent to plug into your software systems (Deel, NetSuite, Google Drive, etc) so it can read data, do work, and take actions in those systems on your behalf.

A surprisingly large number of companies I talk to still use AI like a simple chatbot.

The biggest unlock comes from giving AI visibility to everything…Only then can AI agents perform meaningful work end-to-end. The primary way to do that is via MCP connectors.

If AI is not connected to all your systems, then it lacks context so it will require a lot more manual human effort for each handoff and coordination. AI needs to “see” all the context and have the ability to take actions (even if read-only) to be really useful.

My prepaid agent queries my ERP GL directly via the MCP. No CSV exports/imports.

The skill I created reads the config file to know which prepaid accounts to pull. The agent calls the ERP connector and pulls current period activity for those GL accounts.

If you don’t have an MCP connector to your ERP, that’s the first thing to fix.

Step 4: MCP Connector to the Shared Drive

The workpapers don’t live in your ERP. They live in GDrive, Box, SharePoint, etc.

The skill needs to:

• Read last month’s workpaper

• Write the new workpaper to a new file

• Enable the AI agent to create and edit the new prepaid workpaper

An MCP connector for the shared drive handles this.

Step 5: Slack Is the Trigger and the UI

Slack is the UI for my AI agent.

Slash command: I kick off the recon by typing “/prepaid April”. Or I just chat in Claude “I want to kick off the prepaid recon for the April” and it will find it.

The agent will find the prior workpaper (location is defined in the config file), copy that workpaper to the current month-end close folder. It will then use that as the starting point for adding new prepaids, adding a new month of amortization, etc. The agent is trained just like a human would be…it brings over Excel formulas just like the prior month so the amortization calculates correctly.

When the agent finishes, it posts a digest with the workpaper, the JE CSV, and a tag for the preparer.

Below is an AI generated Slack example of what I see (done with AI so you don’t see my actual Slack…)

If the AI agent flags something I need to fix, then I’ll do one of two things:

1. Open up the workpaper and fix the errors/flags (e.g. add the Cloudflare term from the example above) and then I add a ✅ to the thread which signals to the agent that I am done so it re-reads the files to see if I fixed the flags. It updates the files and posts a follow-up message for the reviewer

2. If the issue will be repeating, then I will update the skill or config files to automatically fix it going forward

Step 6: The Human Part

The skill doesn’t auto-post the JE. We have a human reviewer who then uploads the JE to the ERP from the file the agent created.

We don’t let agents post journals to the GL. At least not yet…

In Summary…

What previously took lots of manual steps (copy workpaper to new location, open up ERP, download detail, update workpaper, create JE upload file, etc) is now 95% completed by our AI agent.

From ~2 hours of human work to ~5 minutes.

It did take longer the first time I ran the reconciliation, but then the team took it and kept iterating and improving the skill and the config to automate more and more. Eventually, you get some major time savings.

And then repeat that across your other recons and you will shave off days from your month-end close…Smaller accounting teams, faster close, less errors, etc

There is a lot more detail behind what we are doing with AI agents in accounting/finance…Subscribe👇 to follow along.

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Footnotes:

• Have a cool AI use case in finance/accounting? I would love to feature it. Email me at onlycfo@onlycfo.io

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