workflowadvancedOpenCLAW

OpenCLAW v2026.3.7-beta.1: Agentic Workflows with Sub-agents and Orchestration

OpenCLAW has a new beta version of its self-hosted AI agent framework. This update allows agents to create specialized 'sub-agents' to handle complex tasks together and includes a new, optional safety system to better control their actions, though security risks still require careful management.

AI SETUP PROMPT

Paste into Claude Code or Codex CLI — it will scan your project and set everything up

# Set Up Workflow: OpenCLAW v2026.3.7-beta.1: Agentic Workflows with Sub-agents and Orchestration

## What This Is
OpenCLAW has a new beta version of its self-hosted AI agent framework. This update allows agents to create specialized 'sub-agents' to handle complex tasks together and includes a new, optional safety system to better control their actions, though security risks still require careful management.

Source: https://www.nccgroup.com/securing-agentic-ai-what-openclaw-gets-wrong-and-how-to-do-it-right/

## 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://www.nccgroup.com/securing-agentic-ai-what-openclaw-gets-wrong-and-how-to-do-it-right/) 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

- Search GitHub for the 'OpenClaw' repository, identifying the official one based on star count (over 100k) and recent activity around March 2026.
- Clone the identified OpenClaw repository and check out the `v2026.3.7-beta.1` tag to a new local directory named `openclaw-beta`.
- Scan the project for configuration files (e.g., `config.yaml.example`, `.env.example`). Create the necessary production config files and populate them, prompting the user for an LLM provider API key from their environment variables if not specified in the project's documentation.

## 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

3,088 charactersCompatible with Claude Code & Codex CLI

MANUAL SETUP STEPS

01Search GitHub for the 'OpenClaw' repository, identifying the official one based on star count (over 100k) and recent activity around March 2026.
02Clone the identified OpenClaw repository and check out the `v2026.3.7-beta.1` tag to a new local directory named `openclaw-beta`.
03Scan the project for configuration files (e.g., `config.yaml.example`, `.env.example`). Create the necessary production config files and populate them, prompting the user for an LLM provider API key from their environment variables if not specified in the project's documentation.

FIELD OPERATIONS

Automated Security Audit Team

Create a multi-agent system where a 'Manager' agent receives a GitHub repository URL. It spawns a 'Code Scanner' sub-agent to run static analysis tools, a 'Dependency Checker' sub-agent to audit dependencies for vulnerabilities, and a 'Report Writer' sub-agent to consolidate findings into a markdown report.

Content Generation Pipeline

Build a workflow where a 'Research' agent takes a topic, scrapes 3-5 relevant articles, and passes summaries to a 'Writer' agent. The 'Writer' agent drafts a blog post, which is then sent to an 'Editor' agent that checks for grammar, style consistency, and SEO keywords before saving the final draft.

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