majiayu000 Agent Designer - Multi-Agent System Architecture
A 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
- Architecture pattern selection — single agent, supervisor, swarm, hierarchical, and pipeline patterns with use-case fit and trade-offs.
- Agent role definition — identity, responsibilities, capabilities, interfaces, and constraints; common archetypes (coordinator, specialist, interface, monitor).
- Tool design — schema design, error handling, idempotency requirements, and validation rules.
- Communication & orchestration — message passing, shared state, event-driven architecture; centralized, decentralized, and hybrid orchestration.
- Guardrails & safety — input validation, output filtering, and human-in-the-loop checkpoints.
- Evaluation frameworks — task completion, quality, cost, and latency metrics with bottleneck analysis.
- Memory, scaling & failure handling — short/long/shared memory, horizontal/vertical scaling, retries, fallbacks, and circuit breakers.
When to Use
- Building AI agent systems or designing multi-agent workflows.
- Creating tool schemas for OpenAI function calling or Anthropic tool use.
- Selecting an architecture pattern for a new system.
- Evaluating agent performance from execution logs.
Clarify First
Before designing the system, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- System goal & scale — the task the agents perform and expected load (drives which architecture pattern: single, supervisor, swarm, hierarchical, or pipeline)
- Tool protocol target — OpenAI function calling vs Anthropic tool use (sets the schema format
tool_schema_generator.pyemits) - Optimization priority — cost, latency, or quality (determines agent roles, model tiers, and which metrics the evaluator weights)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool | Purpose | Command |
|---|---|---|
agent_planner.py |
Design architecture from requirements (pattern, roles, topology, Mermaid diagram, roadmap) | python agent_planner.py requirements.json -o my_system --format both |
agent_evaluator.py |
Evaluate performance from execution logs (success, cost, latency, bottlenecks) | python agent_evaluator.py execution_logs.json -o perf_report --format both --detailed |
tool_schema_generator.py |
Generate OpenAI/Anthropic tool schemas with validation | python tool_schema_generator.py tools.json -o my_tools --format both --validate |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/core-capabilities.md — the full Core Capabilities catalog (architecture patterns, role definition, tool design, communication, guardrails, evaluation, orchestration, memory, scaling, failure handling) plus Implementation Guidelines. Read when designing any part of a system.
- references/agent_architecture_patterns.md — deep catalog of architecture patterns with structure diagrams, characteristics, use cases, and implementation considerations. Read when selecting or comparing patterns.
- references/tool_design_best_practices.md — best practices for designing tools in multi-agent systems (single responsibility, idempotency, composability, schemas, error handling). Read when designing tools or schemas.
- references/evaluation_methodology.md — full evaluation methodology across performance, reliability, cost, and satisfaction dimensions. Read when planning evaluation or interpreting reports.
- references/troubleshooting-and-tool-reference.md — troubleshooting table, success criteria, and the complete CLI parameter reference for all three scripts. Read when a tool misbehaves or you need full command options.
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-designerfor 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 |