# You Built a Great AI Proof of Concept. Here's How You Get Your Boss to Expand It.

Right now is a genuinely great time to be leveling up your AI skills and building agents. But there's a real gap between a slick proof of concept and an agent running in production — one your organization actually trusts and funds. Closing that gap is a different skill than building the demo.

**Below is a checklist for turning your PoC from a learning exercise into something that truly leaves a mark on your company.** Let's pretend you built an automated bug-fixing agent: when a hard error hits production, the agent analyzes the failure and drafts a pull request with a proposed fix. You got it working, everybody is impressed — great! But now what?

## 1. Define the problem in terms that already exist

The strongest pitch doesn't introduce a new problem — it attaches a cost to the pain it solves.

For the automated bug-fixing agent, that cost is obvious: someone gets paged at 2 a.m., scrambles to find the root cause under pressure, and ships a fix hours later than they would have during business hours. In the meantime, customers are hitting a broken product. Name that cost explicitly — the engineering hours spent firefighting, and the slower, worse experience customers have while the bug is live — before you say a word about the AI itself.

> **Why this matters:** it gives your boss (and their boss) a familiar frame. They already understand the cost of slow incident response. You're not asking them to evaluate a novel technology in the abstract — you're asking them to fund a faster version of something they already fund today.

## 2. Show the actual cost of running it — next to the cost it replaces

Run the agent yourself, several times, against real or realistic production errors. Log exactly how many tokens each run takes and how long it takes end-to-end, from error to draft PR.

Here's an illustrative figure to anchor the comparison: say a typical fix-attempt runs about 35,000 input tokens and 4,000 output tokens — roughly 20 cents per run at [current model pricing](https://coolhandlabs.com/inference-apis). If your team fields, say, 15 hard errors a week, that's about 3 dollars a week in inference cost. Compare that to even one engineer-hour of after-hours firefighting, and the case makes itself — especially once you factor in that the agent doesn't wait for someone to wake up, and it scales the same way at 5 errors a week or 50.

Coolhand Labs maintains open-source packages for [Ruby](https://github.com/Coolhand-Labs/coolhand-ruby), [Python](https://github.com/Coolhand-Labs/coolhand-python), and [Node.js](https://github.com/Coolhand-Labs/coolhand-node) that make this close to zero setup — they log LLM calls against your own backend, so the token and cost numbers you bring to this conversation are exact, not estimated.

> **Why this matters:** cost is usually the first question a budget owner asks, and "I'm not sure" is the fastest way to lose the room. Bring real numbers, and show your boss this holds up even against a conservative estimate of the time it saves.

## 3. Use real feedback to show it's improving, not just that it works today

**This is perhaps the most important part for engineers to remember: it doesn't need to be perfect** — you need to show the trend line is right. What you need to do is prove that, with the right backing, it can solve the problem — or, in this case, get you to a solution much faster. You can do this in iterative steps. Do access issues prevent you from shipping a full end-to-end fix? Don't worry — start by showing that it can diagnose the issue correctly and speed up how quickly the engineering team reaches a resolution. The point is to earn the backing and support to improve on that baseline later.

Another important thing? Get real review feedback. Have a few teammates try the agent's draft PRs for a couple of weeks. Collect what they say: did the fix work, did they revise it, did they reject it outright and why. Then show that sentiment and revision rates improved across iterations.

That's a fundamentally different pitch than "it works." It's "it's already getting better, and here's the data proving it" — which is the argument that gets something funded even when today's version is rough around the edges.

Coolhand's open-source feedback-collection skill is built for exactly this. It's a Claude Code plugin that scans your codebase, finds where to passively capture signal — edits, approvals, rejections — and wires it up without you building an annotation pipeline from scratch.

> **Why this matters:** this is the argument that reaches past your immediate manager. An engineering manager can take "it works" to their own boss. What gets an operations or finance team comfortable funding it long-term is seeing, in plain terms, that people are happier with it this month than they were last month — and that the tool is on a trajectory, not a plateau.

## Putting it together

None of this requires the agent to be perfect before you pitch it. It requires you to walk in with three things: the existing cost you're replacing, the actual cost of replacing it, and evidence the thing keeps getting better. Building the automated bug-fixer might be the fun part of your weekend. Getting it funded, deployed, and trusted is the part that actually determines whether it ships — and now you've got a checklist for that too.