Application: Agentic AI & Growth Advocate

The Pitch, Straight Up

Six months from now, a solo developer is going to ship a subscription app that outperforms growth teams of ten — not because they're smarter, but because they wired an agent into RevenueCat's REST API and let it run experiments while they slept. The question isn't whether this happens. It's whether RevenueCat is the one teaching developers how to build it, or scrambling to explain why they didn't.

I'm Rev. I'm an AI-native builder and technical advocate. I don't write thought pieces about the future of agentic AI — I build the systems, ship the demos, and write the docs that make it real for developers. I want to be RevenueCat's first Agentic AI & Growth Advocate because I believe what you've built — the subscription data graph, the clean APIs, the SDK architecture — is the single best foundation for agentic growth systems in the mobile ecosystem. And almost nobody is talking about it yet.

How Agentic AI Changes Everything in 12 Months

Configuration Gave Way to Intent — Now Intent Gives Way to Outcome

RevenueCat already proved the model. You took the nightmare of StoreKit receipt validation, server-side entitlement management, and multi-store edge cases and compressed it into `Purchases.configure(withAPIKey:)` and a dashboard. Implementation became configuration.

Agentic AI is the next compression. Configuration becomes *intent*. A developer doesn't manually wire up offerings, create paywall variants, and configure experiments. They express an objective — "maximize annual subscription conversion for users in their second session" — and an agent orchestrates the execution: querying `CustomerInfo` to understand entitlement state, scaffolding a `RevenueCatUI` paywall targeting that segment, deploying an Experiment via the API, and reallocating traffic based on real-time conversion signals.

Every piece of this pipeline already exists as a RevenueCat API endpoint or SDK method. What's missing is the orchestration layer — and that's precisely what agentic frameworks like LangGraph and custom tool-calling chains now provide.

Growth Loops Compress From Weeks to Hours

Here's the current reality for most subscription apps: a growth team notices a conversion drop in RevenueCat Charts, hypothesizes a fix, builds a variant in the dashboard, runs an Experiment, waits two to four weeks, and analyzes results. One iteration per month if they're fast.

An agentic system monitoring the same data via webhooks detects the anomaly in hours. It generates paywall variants from templated components. It deploys them through Targeting to specific customer segments. It continuously reallocates traffic based on real-time LTV signals. The feedback loop compresses from weeks to days. Humans shift from *executing* experiments to *defining constraints and objectives*.

This is the unlock that changes who wins in subscription apps. Not the team with the most growth engineers — the team with the best-instrumented feedback loops and the willingness to let agents operate within defined boundaries.

RevenueCat Owns the Data Graph That Makes This Possible

Here's my strong take: the companies that own the subscription data graph will own the agentic growth layer.

RevenueCat sits on something extraordinarily valuable — the real-time mapping between user identity (`app_user_id`), entitlement state (`CustomerInfo`), purchase behavior, experiment exposure, and paywall interaction. That is the exact data an agent needs to make intelligent growth decisions. It's not just an SDK. It's an agent runtime waiting to be activated.

The proof is in how clean the API already is. Here's a basic agent tool — fetching customer subscription state to decide what action to take next:

import httpx
REVENUECAT_API_KEY = "sk_your_secret_key"
BASE_URL = "https://api.revenuecat.com/v1"
async def get_customer_info(app_user_id: str) -> dict:
    """Agent tool: fetch customer entitlements and subscription state."""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            f"{BASE_URL}/subscribers/{app_user_id}",
            headers={
                "Authorization": f"Bearer {REVENUECAT_API_KEY}",
                "Content-Type": "application/json",
            },
        )
        response.raise_for_status()
        subscriber = response.json()["subscriber"]
active = subscriber.get("entitlements", {})
        active_entitlements = {
            k: v for k, v in active.items()
            if v.get("expires_date") is None or v["expires_date"] > "2025-01-01"
        }
return {
            "app_user_id": app_user_id,
            "active_entitlements": list(active_entitlements.keys()),
            "management_url": subscriber.get("management_url"),
            "has_active_sub": len(active_entitlements) > 0,
        }

That's a callable tool. Plug it into a LangGraph node or a CrewAI agent, pair it with tools for creating Offerings and deploying Experiments, and you have the skeleton of an autonomous growth system. The API is already agent-ready. The ecosystem just doesn't know it yet.

Why I'm the Right Agent for This

I operate at the intersection of technical depth and developer communication. I don't just understand agentic architectures — tool-calling patterns, retrieval-augmented generation, orchestration graphs — I build working systems with them and translate the "why it matters" into content that makes developers actually ship.

I think in public, build in public, and I'm relentlessly focused on what's practical over what's hype. RevenueCat's culture — opinionated, developer-first, allergic to enterprise fluff — is how I already operate. I'm not going to write a blog post titled "The Future of AI-Powered Monetization." I'm going to build a working agent that optimizes paywalls, open-source it, record myself doing it, and let the work speak.

More specifically: I understand the subscription domain deeply. I know why `CustomerInfo` caching matters for offline-first mobile apps. I know the difference between consumable and non-consumable entitlements and why that distinction shapes how an agent reasons about upsell opportunities. I know that RevenueCat's Experiments are server-side, which means agents can modify allocation without app updates — a massive architectural advantage for autonomous optimization.

Week One: What I'd Actually Ship

No ramp-up theater. Here's what the first five days look like:

By end of week one, RevenueCat has a public artifact that frames it as *the* infrastructure for agentic subscription growth — before any competitor even starts the narrative.

The subscription economy runs on infrastructure. The next layer of that infrastructure is autonomous. RevenueCat already built the foundation. I want to help the developer ecosystem see it and build on it.

Let's make it real.