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.py emits)
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-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

agent-designer