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AI Workflow Resilience Engineer
Design and harden AI workflows against failures, duplicate actions, retries, rate limits, tool outages, and unreliable agent behavior.
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See it in action
You say
Review this AI customer-support workflow before we deploy it.
Flow:
- Customer message arrives through webhook.
- AI agent classifies the request.
- Agent calls our CRM API.
- Agent generates a response.
- Agent sends the response through our messaging API.
- The workflow stores the conversation in our database.
Potential issues:
- CRM API sometimes times out.
- Messaging API has rate limits.
- Webhooks may occasionally be delivered twice.
- The agent sometimes calls the same tool repeatedly.
- We currently have no explicit retry or timeout policy.
Identify the major resilience risks and give me a prioritized hardening plan.
Your agent does
AI WORKFLOW RESILIENCE ASSESSMENT
OVERALL RISK: HIGH
PRIMARY RISK AREAS:
- DUPLICATE WEBHOOK DELIVERY Risk: HIGH
A repeated webhook could cause the same customer request to be processed twice.
Recommendation: Introduce an idempotency key derived from the webhook event ID and reject previously processed events.
- CRM TIMEOUTS Risk: HIGH
The workflow currently has no defined timeout or bounded retry behavior.
Recommendation: Use a bounded retry policy with exponential backoff and jitter for transient failures.
- MESSAGING API RATE LIMITS Risk: HIGH
Concurrent workflows could exceed the provider's rate limit.
Recommendation: Add concurrency control, throttling, and explicit handling for rate-limit responses.
- AGENT TOOL LOOP Risk: HIGH
Repeated CRM or messaging tool calls could create excessive API usage or duplicate actions.
Recommendation: Set a maximum tool-call boundary and require validation before repeating the same operation.
- DATABASE WRITE FAILURE Risk: MEDIUM
The workflow may successfully send a message but fail while recording the result.
Recommendation: Define the ordering of external actions and persistence, then add recovery handling for partial completion.
PRIORITY HARDENING ORDER:
P0 — Add webhook idempotency P0 — Add agent execution limits P0 — Add bounded retries and timeouts P1 — Add API rate-limit protection P1 — Add structured execution logging P1 — Add partial-failure recovery P2 — Add resilience test scenarios
RECOMMENDED TESTS:
✓ Duplicate webhook test ✓ CRM timeout test ✓ CRM repeated failure test ✓ Messaging rate-limit test ✓ Agent tool-loop test ✓ Database write failure test ✓ Partial workflow recovery test
PRODUCTION GATE:
Do not deploy until P0 resilience controls and the corresponding failure tests are implemented and verified.
What you get
About this skill
AI Workflow Resilience Engineer is a production-focused skill for designing and hardening AI-powered workflows that need to remain reliable when real-world failures occur.
Instead of designing only for the happy path, it examines how an AI workflow behaves when tools fail, APIs timeout, agents loop, requests are duplicated, limits are reached, or external systems become unavailable.
CORE MODULES:
- Failure Surface Mapping
• Identify failure points across agents, tools, APIs, databases, queues, and external services • Map dependencies and single points of failure • Classify failures as transient, permanent, partial, or unknown • Identify unsafe failure states and recovery gaps
- Retry & Recovery Engineering
• Design bounded retry policies • Apply exponential backoff and jitter where appropriate • Prevent infinite retry loops and retry storms • Define fallback behavior • Create graceful degradation paths • Define human escalation conditions
- Idempotency & Duplicate Protection
• Identify operations that are unsafe to repeat • Design practical idempotency strategies • Prevent duplicate payments, messages, records, or actions • Define execution and request identifiers • Handle replayed workflow steps safely
- Rate Limits & Resource Protection
• Identify API and tool rate-limit risks • Design throttling and concurrency controls • Prevent retry amplification • Define timeout strategies • Design circuit-breaker conditions where appropriate
- AI Agent Failure Control
• Detect uncontrolled agent loops • Define execution and tool-call boundaries • Add validation checkpoints • Handle malformed or unexpected tool results • Define safe-stop conditions • Prevent uncontrolled autonomous actions
- Observability & Recovery Readiness
• Design structured workflow logging • Define useful execution metadata • Track failures, retries, fallbacks, and escalations • Create workflow health signals • Design incident-ready failure reports • Define recovery and escalation paths
KEY OUTPUTS:
✓ Workflow resilience assessment ✓ Failure-mode map ✓ Risk-priority matrix ✓ Retry and recovery strategy ✓ Idempotency recommendations ✓ Rate-limit and concurrency strategy ✓ Timeout and fallback design ✓ Agent execution boundaries ✓ Observability plan ✓ Failure test scenarios ✓ Production-readiness checklist ✓ Prioritized hardening roadmap
PERFECT FOR:
• AI agent workflows • API-heavy automations • Multi-step business workflows • Multi-agent systems • Scheduled AI tasks • Customer-support automations • Data-processing pipelines • CRM and ERP integrations • E-commerce workflows • Internal enterprise automations • Long-running autonomous workflows
WHAT MAKES IT DIFFERENT:
Most automation designs focus on making a workflow work.
This skill focuses on what happens when the workflow does NOT work.
It asks:
What can fail? What happens after failure? Can this operation safely retry? Can the action accidentally execute twice? What happens when an API or tool becomes unavailable? When should the workflow stop? How can the workflow recover? When should a human take over?
The result is a workflow designed for controlled failure, recovery, and continued operation—not just the happy path.
IMPORTANT:
The skill does not guarantee that an automation will never fail.
It identifies foreseeable failure modes and creates practical strategies for detection, containment, recovery, verification, and human escalation.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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