agent-designer

name	agent-designer
description	Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"engineering","domain":"ai-agents","tier":"POWERFUL","updated":"2026-03-31T00:00:00.000Z"}

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.

Core Capabilities

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

Implementation Guidelines

Architecture Decision Process

1. Requirements Analysis: Understand system goals, constraints, scale
2. Pattern Selection: Choose appropriate architecture pattern
3. Agent Design: Define roles, responsibilities, interfaces
4. Tool Architecture: Design tool schemas and error handling
5. Communication Design: Select message patterns and protocols
6. Safety Implementation: Build guardrails and validation
7. Evaluation Planning: Define success metrics and monitoring
8. 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.

Troubleshooting

Problem	Cause	Solution
Pattern selection returns single_agent for complex systems	Low complexity score due to vague task descriptions	Provide detailed task descriptions including keywords like "parallel", "sequential", or "distributed" to improve heuristic matching
Supervisor bottleneck under high agent count	All specialist agents report to one supervisor, overwhelming its coordination capacity	Switch to hierarchical pattern for teams exceeding 8 agents, or introduce sub-supervisors within the supervisor pattern
Swarm agents produce conflicting outputs	No consensus mechanism configured; agents act on stale shared state	Set consensus_threshold above 0.6 and implement event-driven communication with conflict resolution strategies
Pipeline stage timeouts cascade downstream	A slow stage blocks the entire sequential chain with no backpressure handling	Add per-stage circuit breakers, increase stage_timeout, and implement buffered message queues between stages
Generated Mermaid diagrams render incorrectly	Agent names contain special characters or spaces that break Mermaid syntax	Use snake_case agent names without special characters; the planner sanitizes names automatically
Tool schema validation failures in production	Input schemas generated without sufficient constraints for edge-case data	Run tool_schema_generator.py with --validate to catch schema gaps before deployment
Evaluation report shows 0 throughput	Execution logs missing or malformed start_time/end_time fields	Ensure logs use ISO 8601 datetime format (e.g., 2026-01-15T10:30:00Z) for all timestamp fields

Success Criteria

• Architecture pattern accuracy: Selected pattern matches system requirements in 90%+ of evaluations (validated by team review)
• Agent role completeness: Every task in the requirements maps to at least one agent's responsibilities with no orphaned tasks
• Communication topology coverage: All agent pairs that need to exchange data have explicit communication links defined
• Tool schema compliance: 100% of generated schemas pass validation against both OpenAI function calling and Anthropic tool use formats
• Evaluation report actionability: Performance reports identify at least 3 concrete optimization recommendations with estimated impact
• Design-to-implementation time: Architecture designs reduce multi-agent system implementation time by 40%+ compared to ad-hoc design
• Failure handling coverage: Every agent in the design has defined retry policies, fallback strategies, and escalation paths

Scope & Limitations

Covers:

• Multi-agent architecture pattern selection (single agent, supervisor, swarm, hierarchical, pipeline)
• Agent role definition with responsibilities, capabilities, tools, and communication interfaces
• Tool schema generation in OpenAI and Anthropic formats with validation rules and error handling
• Performance evaluation from execution logs including bottleneck analysis and optimization recommendations

Does NOT cover:

• Runtime agent orchestration or execution engines (see engineering/agent-workflow-designer for workflow execution)
• LLM prompt engineering or system prompt design (see engineering/prompt-engineer-toolkit)
• MCP server implementation or protocol details (see engineering/mcp-server-builder)
• Self-improving agent feedback loops or autonomous learning (see engineering/self-improving-agent)

Integration Points

Skill	Integration	Data Flow
engineering/agent-workflow-designer	Workflow definitions consume architecture designs from Agent Designer	Agent roles and communication topology feed into workflow step definitions
engineering/prompt-engineer-toolkit	System prompts are crafted per agent role defined by Agent Designer	Agent role specifications and responsibilities inform prompt structure and constraints
engineering/mcp-server-builder	Tool schemas generated here map to MCP server tool implementations	tool_schema_generator.py output provides the schema contract that MCP servers implement
engineering/self-improving-agent	Evaluation reports feed into self-improvement loops	agent_evaluator.py bottleneck analysis drives autonomous optimization decisions
engineering/observability-designer	Monitoring architecture aligns with agent topology and communication links	Agent definitions and communication patterns define what to instrument and alert on
engineering/agent-protocol	Protocol standards govern inter-agent message formats designed here	Communication topology patterns must comply with agent protocol specifications

Tool Reference

agent_planner.py

Purpose: Designs multi-agent system architectures from system requirements. Selects an architecture pattern, defines agent roles, generates communication topology, produces a Mermaid diagram, and creates an implementation roadmap.

Usage:

python agent_planner.py <input_file> [-o OUTPUT] [--format {json,yaml,both}]

Parameters:

Parameter	Type	Required	Default	Description
input_file	positional	Yes	--	JSON file containing system requirements (goal, description, tasks, constraints, team_size, performance_requirements, safety_requirements, integration_requirements, scale_requirements)
-o, --output	string	No	agent_architecture	Output file prefix for generated files
--format	choice	No	both	Output format: json, yaml, or both

Example:

python agent_planner.py requirements.json -o my_system --format both

Output Formats:

• {prefix}.json -- Full architecture design including agents, communication topology, guardrails, scaling strategy, and metadata
• {prefix}_diagram.mmd -- Mermaid diagram of the agent architecture (generated when format is both)
• {prefix}_roadmap.json -- Implementation roadmap with phases, tasks, deliverables, risks, and success criteria (generated when format is both)
• Console summary showing pattern, agent count, communication links, and estimated duration

---

agent_evaluator.py

Purpose: Evaluates multi-agent system performance from execution logs. Calculates success rates, cost analysis, latency distribution, error patterns, bottleneck identification, and optimization recommendations.

Usage:

python agent_evaluator.py <input_file> [-o OUTPUT] [--format {json,both}] [--detailed]

Parameters:

Parameter	Type	Required	Default	Description
input_file	positional	Yes	--	JSON file containing execution logs (array of log entries with task_id, agent_id, task_type, status, duration_ms, tokens_used, cost_usd, tools_used, error_details, etc.)
-o, --output	string	No	evaluation_report	Output file prefix for generated report files
--format	choice	No	both	Output format: json or both
--detailed	flag	No	off	Include detailed per-agent and per-task-type breakdowns in the report

Example:

python agent_evaluator.py execution_logs.json -o perf_report --format both --detailed

Output Formats:

• {prefix}.json -- Complete evaluation report with system metrics, agent metrics, task type metrics, tool usage analysis, error analysis, bottleneck analysis, and optimization recommendations
• Console summary with key performance indicators (when format is both, additional breakdowns are written to separate files)

---

tool_schema_generator.py

Purpose: Generates structured tool schemas compatible with OpenAI function calling and Anthropic tool use formats. Includes input validation rules, error response formats, example calls, and rate limit suggestions.

Usage:

python tool_schema_generator.py <input_file> [-o OUTPUT] [--format {json,both}] [--validate]

Parameters:

Parameter	Type	Required	Default	Description
input_file	positional	Yes	--	JSON file containing tool descriptions (array of objects with name, purpose, category, inputs, outputs, error_conditions, side_effects, idempotent, rate_limits, dependencies, examples, security_requirements)
-o, --output	string	No	tool_schemas	Output file prefix for generated schema files
--format	choice	No	both	Output format: json or both
--validate	flag	No	off	Validate generated schemas against JSON Schema standards and report any issues

Example:

python tool_schema_generator.py tools.json -o my_tools --format both --validate

Output Formats:

• {prefix}.json -- Complete tool schemas including OpenAI format, Anthropic format, validation rules, error responses, rate limits, and example usage for each tool
• Console summary with schema count, validation results (when --validate is used), and any detected issues