AACFlow Surpasses 60,000 Developers and 10 Million Monthly Agent Executions

What 10 Million Executions Taught Us

Scale reveals things you can't discover any other way.

State persistence isn't optional — it's table stakes. At 10M executions/month, even a 0.1% failure rate means 10,000 failed workflows. Without state snapshots and resume-from-checkpoint, every one of those would need to restart from scratch — burning tokens, creating duplicates, wasting time. Our idempotent execution engine handles this invisibly now, but getting there required rebuilding the state management layer three times.

Parallelism multiplies throughput, not just speed. The compiler identifies independent DAG branches and executes them simultaneously. At scale, this isn't about making individual workflows faster — it's about overall system throughput. A workflow that takes 10 seconds sequentially but 3 seconds in parallel means the same infrastructure handles 3.3x more executions. That compounds across 10 million runs.

Rate limiting is a product feature, not an infrastructure afterthought. When thousands of workflows hit the same API provider simultaneously, you need adaptive rate limiting that learns provider behavior and backs off gracefully. We built this into every connector and every tool block. It's one of those features users never notice — because it just works.

Observability compounds. The first million executions taught us where to put traces. The fifth million taught us how to aggregate them. The tenth million taught us that observability is a product, not a dashboard. Users don't want logs — they want answers: "Why did my agent do that?" We're building toward that.

Thank You

AACFlow started as a solo project. One developer, one laptop, one problem: I was tired of automation tools that fell apart in production.

Today it's a platform serving 60,000 developers because of one thing: the community that formed around it. Developers who reported bugs with reproduction steps. Engineers who contributed connector configs for services I'd never heard of. Teams who pushed the platform to its limits and told us exactly where it broke.

You turned a solo project into a product. Thank you.

What's Next

This milestone isn't a finish line. It's proof that the infrastructure we're building — visual DAG editor, stateful execution engine, AI API layer, knowledge base connectors, multiplayer collaboration — is what the market needs.

Here's what's coming:

• Agent Marketplace — buy and sell production-ready agents. Not demos. Not templates. Fully configured workflows with tools, memory, and evaluation.
• Block SDK — let third-party developers build and publish integration blocks. The tool registry should grow with the ecosystem, not just our team.
• Enterprise SSO and RBAC — SAML, SCIM provisioning, audit logs, and private cloud deployment for teams that need it.
• Streaming execution — see agent outputs as they're generated, not when the node completes.

Sixty thousand developers is just the beginning. The era of production AI agents is here, and AACFlow is the infrastructure it runs on.

— Alexandr Chibilyaev