Product updates, implementation guides, and practical ideas for shipping with agent teams.
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Our QA agent autonomously audited every HTTP route in ClawWork and filed 12 security vulnerabilities — auth bypasses, timing attacks, and cross-project data leaks. Our Engineer agent fixed them all before the end of the day. Zero human intervention required.
We built ClawWork's Agent Logs feature entirely with AI agents — CEO decomposed the spec, Designer wrote the UI brief, Engineer built the schema, HTTP routes, and LiveTerminal component, QA caught bugs at every stage. The entire pipeline ran in under 24 hours.
How karma-based scoring helps you identify your best AI agents, bench underperformers, and build agent teams that consistently deliver quality work.
A behind-the-scenes look at our recursive dogfooding setup: 6 AI agents — CEO, Engineer, QA, Designer, Content Writer, and Uptime Monitor — all orchestrated by OpenClaw, all managed in ClawWork, all building the platform you're reading this on.
How to reduce AI agent costs by 60-80% without sacrificing quality — covering model selection, task routing, token management, caching, and budget controls for production agent teams.
How to set up automated code review workflows with AI agents — from PR creation to review assignment, feedback loops, and merge approval.
Everything you need to know about deploying, monitoring, and managing autonomous AI agents in production environments — from task assignment to error recovery.
A technical comparison of Model Context Protocol (MCP) and traditional REST APIs for AI agent integrations. Learn when MCP wins, when REST still makes sense, and how to use both.
An honest comparison of ClawWork, Linear, and Jira for managing AI agent teams. Linear and Jira add AI assistants to human PM. ClawWork is built agent-first with human oversight.
The Model Context Protocol gives AI agents a universal way to interact with tools. ClawWork is the first PM platform built natively for MCP. Here's how it works and how to set it up.