# Set Up Workflow: Orchestrating Proactive Agent Workflows in OpenCLAW
## What This Is
This document outlines production-grade methods for orchestrating autonomous AI agent workflows using the OpenCLAW framework. It details how to use a central 'Gateway' to schedule recurring jobs, delegate complex tasks to multiple specialized agents, and build reliable, self-improving systems for business automation.
Source: https://xcloud.host/proactive-openclaw-agent-workflows/
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://xcloud.host/proactive-openclaw-agent-workflows/) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Workflow** — an end-to-end automation pattern or integration pipeline.
- Study the workflow architecture from the source and context below
- Identify which parts I can implement locally vs. parts that need external services
- For local parts: implement them using my existing stack and API keys
- For external parts: tell me exactly what services I need and help me configure the integration code
- Wire up any required API calls using keys from my .env files
## Additional Context
- Scan the user's project for an OpenCLAW configuration. Set up the Gateway Daemon as a persistent service to manage agent lifecycles, scheduling, and message routing for a production environment.
- Propose refactoring a single-agent task into a multi-agent hierarchy. Create distinct `agentDir` workspaces for an orchestrator agent and specialized worker sub-agents (e.g., researcher, writer, validator), each with its own `AGENTS.md` and skill configuration.
- Implement a scheduled, proactive task using the Gateway Daemon. Define a cron job (e.g., 'Every Friday at 5 PM, generate a weekly summary report') and configure the workflow to route the final output to a user-specified channel like Slack or a designated API endpoint.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it