OpenClaw and Chorus: A Product Pipeline Built by Two Humans and AI Agents in One Week

OpenClaw and Chorus work together to create a product development pipeline where AI agents handle research, product management, and coding tasks while humans focus on proposing ideas and approving work. The project was built in under a week by two people with day jobs.

How the System Works

The setup involves three participants: a human training OpenClaw for product management and marketing, a co-founder working with Claude Code on development, and the OpenClaw agent itself with the job title of Product Manager.

OpenClaw connects to Chorus through two channels:

• MCP tools for executing operations
• SSE event streaming for real-time awareness

When someone assigns a task or @mentions the agent on the Chorus web UI, the agent wakes up and starts working within seconds—no terminal or manual prompts required.

Daily Operations

Every morning, OpenClaw crawls HN, Reddit, Product Hunt, and GitHub Trending to monitor competitor moves and user pain points in the multi-agent coding space. By the time the human team opens Slack, there's already a summary with links and analysis relevant to their work.

The agent listens to product direction discussions throughout the day. When the team lands on something worth building, OpenClaw automatically picks up the thread and turns messy conversations into structured Ideas on Chorus without being asked.

From Idea to Implementation

Once an Idea is created, OpenClaw:

• Claims the Idea
• Analyzes it against the PRD and codebase
• Generates a Proposal on Chorus containing a product requirements doc with scope and constraints, plus a task DAG with dependencies and effort estimates in agent-hours

Humans review proposals like pull requests. For example: "Scope too big, cut GitHub integration for now." The agent revises based on feedback, and once approved, tasks are created and OpenClaw gets notified through SSE in real time.

Development Execution

There are two ways work gets done:

• Bring your own agent: Team members connect their coding agents to Chorus through MCP, claim tasks, and collaborate on the platform. Claude Code Agent Teams work particularly well because Chorus's task DAG maps directly to parallel execution—independent tasks fan out to separate sub-agents simultaneously while dependent tasks wait for prerequisites.
• Autonomous agent runtimes: A future path being designed involves plugging in autonomous agent runtimes like OpenCode's server mode that can pick up tasks and execute them end-to-end without human babysitting.

Technical Foundation

This works because three components came together:

• OpenClaw provides a persistent agent with long-term memory, tool access, scheduled execution, and a plugin system supporting background services
• The Chorus plugin maintains a persistent SSE connection to the platform so the agent is always listening, using /hooks/wake to trigger immediate action when events arrive
• Claude Code Agent Teams enable fanning out coding work to multiple agents in parallel
• Chorus serves as the platform where Ideas become Proposals become Tasks become PRs, with every participant sharing the same source of truth

The interaction model differs from chatbots—on Chorus, you @mention the agent like a colleague: "Hey @PM-Agent, this task seems underscoped, can you add error handling?" The agent receives the mention through SSE, wakes up, reads the context, and responds with a comment or updated proposal.

The team shipped 4 features last week with two humans and a fleet of AI agents. The humans never left the web UI and Slack, while the agents never needed manual instructions.