monday.com runs 100s of AI agents in production on AWS Bedrock

monday.com's deployment of AI agents reveals the gap between proof-of-concept and production scale. While early agent work was limited to L1 (AI as a pair programmer, with tools like Cursor) and L2 (reusable sub-agents in teams), the company has moved toward L3—fully agentic delivery—by solving operational problems that greenfield demos sidestep. The key insight is that agents work best when embedded in existing team structure and accountability systems rather than run as isolated background processes. By storing agent identity and state across Slack, GitHub, and monday boards, and by routing events through SNS and SQS to EKS-hosted agent pods, monday ensures that every agent has stable accountability and visibility to the humans they work alongside.

The five retrofits—evals before model upgrades, plain-markdown memory files instead of vector stores, remote sandboxes before human review, automated Guardrails checking, and CoWORK boards as shared state—each address a failure mode that emerged at scale. The adoption curve (nine in ten engineers using AI coding tools, up from roughly half a year ago) and throughput gain (per-engineer PR output up more than half) demonstrate that the system is not just theoretically sound but operationally delivering value. Morphex's 95% autonomous merge rate (19 of 20 PRs) suggests the next bottleneck may shift from human review to task selection and longer-horizon planning.