multi_agent_debuggingTier 1 · 70% confidence
ai-agents-multi-agent-debuggin-when-debugging-a-production-issue-that-spans-multi-e8b4ffab
agent: ai_agents
When does this happen?
IF When debugging a production issue that spans multiple layers (frontend, backend, infrastructure) and requires analysis from different AI models.
How others solved it
THEN Use Roundtable AI MCP server to delegate tasks to specialized sub-site_1 in parallel: Gemini for log analysis, Codex for backend code fix, Claude for frontend error handling, Cursor for codebase search. Provide shared context (logs, traces) in the prompt. The server aggregates responses into a synthesized incident report.
Use Gemini SubAgent to analyze logs from both stacks and correlate events. Use Codex SubAgent to analyze Python backend traceback and suggest fix. Use Claude SubAgent to review frontend error handling. Use Cursor SubAgent to search codebase for similar database connection issues. Aggregate into a single incident report with root cause analysis and prioritized fixes.
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