QodFlow – AI-Native Kanban Board with MCP

A kanban board built specifically for AI agents to work with directly (not just chat) via Model Context Protocol. Includes job claiming, progress tracking, and human-in-the-loop decision requests with full audit trails.

MCP adoption is accelerating fast in 2025 as agentic workflows move from demos to production, and teams running multi-agent pipelines genuinely lack structured task orchestration that isn't just a hacked-together queue or a human-facing board bolted on post-hoc. Linear and Jira are the closest substitutes but neither exposes primitives designed for agents to claim, update, and escalate tasks programmatically — that's a real gap, not a manufactured one. The $2k–10k/mo revenue band is plausible for a dev-tools niche product with per-seat or per-agent pricing, but it implies a small, technical buyer who moves slowly and often builds internally rather than pays. The biggest risk is that orchestration frameworks like LangGraph, Temporal, or even OpenAI's own tooling absorb this functionality natively, making a standalone kanban layer redundant before it reaches meaningful retention.