For stubborn bugs, use an advanced prompting technique: instruct the AI to 'spin up specialized sub-agents,' such as a QA tester and a senior engineer. This forces the model to analyze the problem from multiple perspectives, leading to a more comprehensive diagnosis and solution.

To avoid high API costs, use the OAuth method to link OpenClaw to your existing $20 ChatGPT subscription. This leverages your subscription's usage limits instead of per-token API pricing. Crucially, configure fallback models (like Anthropic or an open-source model via OpenRouter) so your agent remains operational if the primary model fails.

Before troubleshooting, create a support baseline. Upload the official OpenClaw documentation into a Claude or ChatGPT project. This creates a context-aware support bot that provides accurate, doc-based answers, avoiding the unreliable and often outdated results from public web searches or Reddit posts.

Despite sophisticated AI debugging tools that monitor logs and browsers, the most efficient solution is often the simplest. Highlighting an error message, copying it, and pasting it directly into an AI agent's chat window is a fast and reliable way to get a fix without over-engineering your workflow.

Creating user manuals is a time-consuming, low-value task. A more efficient alternative is to build an AI chatbot that users can interact with. This bot can be trained on source engineering documents, code, and design specs to provide direct answers without an intermediate manual.

Run two different AI coding agents (like Claude Code and OpenAI's Codex) simultaneously. When one agent gets stuck or generates a bug, paste the problem into the other. This "AI Ping Pong" leverages the different models' strengths and provides a "fresh perspective" for faster, more effective debugging.

When an agent fails, treat it like an intern. Scrutinize its log of actions to find the specific step where it went wrong (e.g., used the wrong link), then provide a targeted correction. This is far more effective than giving a generic, frustrated re-prompt.

Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.

Desktop-based AI agents like Claude Co-Work, which can see your screen and local files, are a game-changer. They enable non-engineers to tackle complex projects like building production apps with single sign-on by providing real-time assistance and debugging.