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name: agent-designer description: | Design multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI workflows.

AI Agent Designer

Overview

Designs AI agent architectures — single agents or multi-agent systems — with clear roles, tools, memory, and orchestration patterns.

Workflow

Step 1: Define the Agent

1. Objective: What should the agent accomplish?
2. Scope: What's in and out of bounds?
3. Tools needed: What APIs/services should it access?
4. Autonomy level: Fully autonomous, human-in-the-loop, or supervised?
5. Platform: LangChain, CrewAI, AutoGen, custom, or conceptual?

Step 2: Choose the Architecture

Pattern	When to Use
Single agent + tools	One task, multiple steps
Sequential chain	Linear workflow, each step feeds the next
Router agent	Different tasks need different specialists
Parallel agents	Independent tasks that can run simultaneously
Hierarchical	Manager delegates to worker agents
Collaborative	Agents discuss and refine outputs

Step 3: Design the System

For each agent:

## Agent: [Name]

### Role
[One sentence — what this agent does]

### System Prompt

[Full system prompt]

### Tools Available
| Tool | Purpose | Input | Output |
|------|---------|-------|--------|
| [tool] | [why] | [format] | [format] |

### Memory
- **Short-term**: [conversation context]
- **Long-term**: [persistent storage approach]
- **Shared**: [what other agents can access]

### Decision Logic
- IF [condition] → [action]
- IF [condition] → [escalate to human]
- IF [error] → [fallback]

### Output
[What this agent produces and where it goes]

Output Format

# AI Agent System: [Name]

## Architecture Overview
**Pattern**: [architecture type]
**Agents**: [count]
**Orchestration**: [how they coordinate]

## System Diagram

[Text-based architecture diagram]

## Agent Specifications

### Agent 1: [Name]
[Full specification per template above]

### Agent 2: [Name]
[Full specification]

## Orchestration Logic
[How agents communicate and coordinate]

## Error Handling
[What happens when things go wrong]

## Human Touchpoints
[Where and when humans intervene]

## Implementation Guide
- **Platform**: [recommendation]
- **Models**: [which LLMs for which agents]
- **Estimated cost**: [per-run cost estimate]
- **Setup steps**: [how to build it]

Quality Checklist

• Each agent has a clear, single responsibility
• Tools defined with input/output formats
• Orchestration logic handles all paths
• Error handling and fallbacks defined
• Human oversight points identified
• Cost estimate included