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Grok+17 agentsMulti Agent Architecture Fallback Strategy Designer
Design resilient multi-agent architectures with failure-mode analysis, fallback policies, and token budgets.
$10
Multi Agent Architecture Fallback Strategy Designer
Example session with this skill installed
Design a multi-agent system for an automated customer support agent that can process refunds and check order status. Use LangGraph and GPT-4o. Run /full.
- Read your context and instructions
- Compiled the multi agent architecture
The system is designed as an Orchestrator-Worker topology with a specific emphasis on transactional integrity for refund processing.
Scope Summary
System: Automated Support & Refund Agent
Risk class: Transactional
Workload: 50 RPM, peak 100 RPM
Targets: 99% success rate, p95 < 15s, max cost $0.05/task
Stack: LangGraph, GPT-4o/GPT-4o-mini, PostgreSQL state store
Human-in-the-loop: Required for refunds > $100
Topology: Orchestrator-Worker
The orchestrator manages intent and state, delegating to specialists. This prevents context bloat in workers and allows model tiering (Small for Status, Frontier for Refunds).
| Agent | Single responsibility | Criticality | State | Model tier | Tools |
|---|---|---|---|---|---|
| Triage | Route request by intent | Critical | Stateless | Small | None |
| Status | Retrieve order/shipping info | Important | Stateless | Small | OrderDB |
| Refund | Validate and process refunds | Critical | Stateful | Frontier | PaymentsAPI |
Failure Mode Register
| ID | Component | Failure mode | S | L | D | RPN | Blast radius | Detection | Fallback ID |
|---|---|---|---|---|---|---|---|---|---|
| F05 | Refund | PaymentsAPI Timeout | 5 | 3 | 4 | 60 | Refund Flow | HTTP 504 | FB-01 |
| F03 | Triage | Invalid Route Schema | 4 | 2 | 2 | 16 | System | Validation | FB-02 |
Fallback Policy (YAML)
policy_id: FB-01
applies_to: RefundAgent
failure_modes: [F05]
timeout_ms: 15000
retry:
max_attempts: 3
backoff: exponential
base_delay_ms: 1000
fallback_ladder:
- rung: alternate_model
target: gpt-4-turbo
- rung: human_escalation
sla_minutes: 5
idempotency:
key: "order_id + refund_amount_hash"
Protocol Specification
Messages must use a correlation_id to track the refund lifecycle across retries. The trust_level for PaymentsAPI results is set to system to prevent downstream injection.
Next steps
- Implement the idempotency key logic in your LangGraph state schema.
- Configure the circuit breaker for the PaymentsAPI tool.
- Conduct a chaos test simulating a 429 rate limit on the primary LLM provider.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Multi-agent systems often fail silently, loop indefinitely, or burn through token budgets without warning. This skill provides a rigorous framework for designing resilient AI architectures that degrade gracefully rather than crashing. It forces engineering discipline onto agentic workflows by treating them as distributed systems, complete with circuit breakers, fallbacks, and typed communication protocols.
What it does
- Topology mapping defines agent responsibilities, model tiers, and communication patterns using patterns like orchestrator-worker or pipelines.
- Failure mode analysis identifies high-risk triggers like context overflow, rate limits, and hallucination propagation using FMEA-style registers.
- Fallback engineering specifies multi-rung recovery ladders including exponential backoff, alternate models, and human-in-the-loop escalation.
- Protocol design enforces a structured message envelope with versioning, idempotency keys, and trust boundaries.
- Budgeting & optimization calculates token costs with retry amplification and applies model tiering to reduce overhead.
How it works
- Scope the system purpose, SLAs, risk class, and technology stack constraints.
- Map the agent topology and assign single responsibilities to every node.
- Analyze failure modes and assign RPN scores to prioritize mitigation.
- Define recovery policies and budget constraints for each agent.
Frameworks & tools
Language and framework agnostic logic suitable for LangGraph, CrewAI, AutoGen, or custom TypeScript/Python orchestrators.
Why this beats prompting it yourself
Generic prompts ignore the hidden costs of agentic retries and the complexity of state synchronization. This skill applies reliability engineering principles to prevent infinite loops, cost runaways, and prompt injection propagation that simple prompts miss.
Use cases
- Designing transactional agent systems where data integrity and idempotency are mandatory.
- Auditing existing multi-agent codebases to identify single points of failure.
- Creating chaos-testing plans to validate how agents handle provider outages.
- Planning cost-efficient scaling for high-volume agentic workloads.
Known limitations
Does not provide executable code for specific proprietary framework versions. Numeric defaults require calibration against production telemetry.
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- 1
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- 2
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- 3
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