agent-designer

# 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, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used. - **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.py` emits). - **Optimization priority:** cost, latency, or quality (determines agent roles, model tiers, and which metrics the evaluator weights). Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top. ## 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](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](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](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](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](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-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 |