From design-agent-orchestration
Guides failure recovery design for multi-agent systems, covering types like agent crashes and handoffs, plus strategies: retry, fallback, escalation, graceful degradation.
npx claudepluginhub owl-listener/ai-design-skills --plugin design-agent-orchestrationThis skill uses the workspace's default tool permissions.
Agents fail. Networks time out, models hallucinate, tools error, and edge cases surprise. Failure recovery design determines whether a failure becomes a dead end or a graceful detour.
Implements circuit breaker pattern for agentic tool calls: tracks health via closed/open/half-open states, reduces scope on failures, routes to alternatives, enforces failure budgets. For fault-tolerant agent workflows.
Guides building reliable autonomous AI agents with ReAct, Plan-Execute loops, reflection patterns, goal decomposition, and frameworks like LangGraph, CrewAI. Emphasizes reliability principles for production.
Provides structured self-debugging workflow for AI agent failures: capture state, diagnose patterns, apply contained recoveries, generate introspection reports. For loops, retries without progress, context drift.
Share bugs, ideas, or general feedback.
Agents fail. Networks time out, models hallucinate, tools error, and edge cases surprise. Failure recovery design determines whether a failure becomes a dead end or a graceful detour.
For each point in the workflow where failure is possible: