Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
agent-designer
Design, architect, and evaluate multi-agent AI systems using patterns, role definitions, communication strategies, and safety guardrails for scalable workflows.
skill openclawclawhub Free
Design, architect, and evaluate multi-agent AI systems using patterns, role definitions, communication strategies, and safety guardrails for scalable workflows.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, agent_evaluator.py, agent_planner.py, assets/sample_execution_logs.json, assets/sample_system_requirements.json
Validation
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- Review SKILL.md after the package is downloaded.
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.1
Provenance
- Publisher
- alirezarezvani
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Documentation
Agent Designer - Multi-Agent System Architecture
Tier: POWERFUL Category: Engineering Tags: AI agents, architecture, system design, orchestration, multi-agent systems
Overview
Agent Designer is a comprehensive toolkit for designing, architecting, and evaluating multi-agent systems. It provides structured approaches to agent architecture patterns, tool design principles, communication strategies, and performance evaluation frameworks for building robust, scalable AI agent systems.
1. Agent Architecture Patterns
Single Agent Pattern Use Case: Simple, focused tasks with clear boundaries Pros: Minimal complexity, easy debugging, predictable behavior Cons: Limited scalability, single point of failure Implementation: Direct user-agent interaction with comprehensive tool access Supervisor Pattern Use Case: Hierarchical task decomposition with centralized control Architecture: One supervisor agent coordinating multiple specialist agents Pros: Clear command structure, centralized decision making Cons: Supervisor bottleneck, complex coordination logic Implementation: Supervisor receives tasks, delegates to specialists, aggregates results Swarm Pattern Use Case: Distributed problem solving with peer-to-peer collaboration Architecture: Multiple autonomous agents with shared objectives Pros: High parallelism, fault tolerance, emergent intelligence Cons: Complex coordination, potential conflicts, harder to predict Implementation: Agent discovery, consensus mechanisms, distributed task allocation Hierarchical Pattern Use Case: Complex systems with multiple organizational layers Architecture: Tree structure with managers and workers at different levels Pros: Natural organizational mapping, clear responsibilities Cons: Communication overhead, potential bottlenecks at each level Implementation: Multi-level delegation with feedback loops Pipeline Pattern Use Case: Sequential processing with specialized stages Architecture: Agents arranged in processing pipeline Pros: Clear data flow, specialized optimization per stage Cons: Sequential bottlenecks, rigid processing order Implementation: Message queues between stages, state handoffs
2. Agent Role Definition
Role Specification Framework Identity: Name, purpose statement, core competencies Responsibilities: Primary tasks, decision boundaries, success criteria Capabilities: Required tools, knowledge domains, processing limits Interfaces: Input/output formats, communication protocols Constraints: Security boundaries, resource limits, operational guidelines Common Agent Archetypes Coordinator Agent Orchestrates multi-agent workflows Makes high-level decisions and resource allocation Monitors system health and performance Handles escalations and conflict resolution Specialist Agent Deep expertise in specific domain (code, data, research) Optimized tools and knowledge for specialized tasks High-quality output within narrow scope Clear handoff protocols for out-of-scope requests Interface Agent Handles external interactions (users, APIs, systems) Protocol translation and format conversion Authentication and authorization management User experience optimization Monitor Agent System health monitoring and alerting Performance metrics collection and analysis Anomaly detection and reporting Compliance and audit trail maintenance
3. Tool Design Principles
Schema Design Input Validation: Strong typing, required vs optional parameters Output Consistency: Standardized response formats, error handling Documentation: Clear descriptions, usage examples, edge cases Versioning: Backward compatibility, migration paths Error Handling Patterns Graceful Degradation: Partial functionality when dependencies fail Retry Logic: Exponential backoff, circuit breakers, max attempts Error Propagation: Structured error responses, error classification Recovery Strategies: Fallback methods, alternative approaches Idempotency Requirements Safe Operations: Read operations with no side effects Idempotent Writes: Same operation can be safely repeated State Management: Version tracking, conflict resolution Atomicity: All-or-nothing operation completion
4. Communication Patterns
Message Passing Asynchronous Messaging: Decoupled agents, message queues Message Format: Structured payloads with metadata Delivery Guarantees: At-least-once, exactly-once semantics Routing: Direct messaging, publish-subscribe, broadcast Shared State State Stores: Centralized data repositories Consistency Models: Strong, eventual, weak consistency Access Patterns: Read-heavy, write-heavy, mixed workloads Conflict Resolution: Last-writer-wins, merge strategies Event-Driven Architecture Event Sourcing: Immutable event logs, state reconstruction Event Types: Domain events, system events, integration events Event Processing: Real-time, batch, stream processing Event Schema: Versioned event formats, backward compatibility
5. Guardrails and Safety
Input Validation Schema Enforcement: Required fields, type checking, format validation Content Filtering: Harmful content detection, PII scrubbing Rate Limiting: Request throttling, resource quotas Authentication: Identity verification, authorization checks Output Filtering Content Moderation: Harmful content removal, quality checks Consistency Validation: Logic checks, constraint verification Formatting: Standardized output formats, clean presentation Audit Logging: Decision trails, compliance records Human-in-the-Loop Approval Workflows: Critical decision checkpoints Escalation Triggers: Confidence thresholds, risk assessment Override Mechanisms: Human judgment precedence Feedback Loops: Human corrections improve system behavior
6. Evaluation Frameworks
Task Completion Metrics Success Rate: Percentage of tasks completed successfully Partial Completion: Progress measurement for complex tasks Task Classification: Success criteria by task type Failure Analysis: Root cause identification and categorization Quality Assessment Output Quality: Accuracy, relevance, completeness measures Consistency: Response variability across similar inputs Coherence: Logical flow and internal consistency User Satisfaction: Feedback scores, usage patterns Cost Analysis Token Usage: Input/output token consumption per task API Costs: External service usage and charges Compute Resources: CPU, memory, storage utilization Time-to-Value: Cost per successful task completion Latency Distribution Response Time: End-to-end task completion time Processing Stages: Bottleneck identification per stage Queue Times: Wait times in processing pipelines Resource Contention: Impact of concurrent operations
7. Orchestration Strategies
Centralized Orchestration Workflow Engine: Central coordinator manages all agents State Management: Centralized workflow state tracking Decision Logic: Complex routing and branching rules Monitoring: Comprehensive visibility into all operations Decentralized Orchestration Peer-to-Peer: Agents coordinate directly with each other Service Discovery: Dynamic agent registration and lookup Consensus Protocols: Distributed decision making Fault Tolerance: No single point of failure Hybrid Approaches Domain Boundaries: Centralized within domains, federated across Hierarchical Coordination: Multiple orchestration levels Context-Dependent: Strategy selection based on task type Load Balancing: Distribute coordination responsibility
8. Memory Patterns
Short-Term Memory Context Windows: Working memory for current tasks Session State: Temporary data for ongoing interactions Cache Management: Performance optimization strategies Memory Pressure: Handling capacity constraints Long-Term Memory Persistent Storage: Durable data across sessions Knowledge Base: Accumulated domain knowledge Experience Replay: Learning from past interactions Memory Consolidation: Transferring from short to long-term Shared Memory Collaborative Knowledge: Shared learning across agents Synchronization: Consistency maintenance strategies Access Control: Permission-based memory access Memory Partitioning: Isolation between agent groups
9. Scaling Considerations
Horizontal Scaling Agent Replication: Multiple instances of same agent type Load Distribution: Request routing across agent instances Resource Pooling: Shared compute and storage resources Geographic Distribution: Multi-region deployments Vertical Scaling Capability Enhancement: More powerful individual agents Tool Expansion: Broader tool access per agent Context Expansion: Larger working memory capacity Processing Power: Higher throughput per agent Performance Optimization Caching Strategies: Response caching, tool result caching Parallel Processing: Concurrent task execution Resource Optimization: Efficient resource utilization Bottleneck Elimination: Systematic performance tuning
10. Failure Handling
Retry Mechanisms Exponential Backoff: Increasing delays between retries Jitter: Random delay variation to prevent thundering herd Maximum Attempts: Bounded retry behavior Retry Conditions: Transient vs permanent failure classification Fallback Strategies Graceful Degradation: Reduced functionality when systems fail Alternative Approaches: Different methods for same goals Default Responses: Safe fallback behaviors User Communication: Clear failure messaging Circuit Breakers Failure Detection: Monitoring failure rates and response times State Management: Open, closed, half-open circuit states Recovery Testing: Gradual return to normal operation Cascading Failure Prevention: Protecting upstream systems
Architecture Decision Process
Requirements Analysis: Understand system goals, constraints, scale Pattern Selection: Choose appropriate architecture pattern Agent Design: Define roles, responsibilities, interfaces Tool Architecture: Design tool schemas and error handling Communication Design: Select message patterns and protocols Safety Implementation: Build guardrails and validation Evaluation Planning: Define success metrics and monitoring Deployment Strategy: Plan scaling and failure handling
Quality Assurance
Testing Strategy: Unit, integration, and system testing approaches Monitoring: Real-time system health and performance tracking Documentation: Architecture documentation and runbooks Security Review: Threat modeling and security assessments
Continuous Improvement
Performance Monitoring: Ongoing system performance analysis User Feedback: Incorporating user experience improvements A/B Testing: Controlled experiments for system improvements Knowledge Base Updates: Continuous learning and adaptation This skill provides the foundation for designing robust, scalable multi-agent systems that can handle complex tasks while maintaining safety, reliability, and performance at scale.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts2 Config
- SKILL.md
- README.md
- agent_evaluator.py
- agent_planner.py
- assets/sample_execution_logs.json
- assets/sample_system_requirements.json