Agent Designer - Multi-Agent System Architecture

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

References