Ai Agents Architect
When & Why to Use This Skill
The AI Agents Architect skill provides expert-level guidance for designing, building, and optimizing autonomous AI systems. It focuses on mastering agentic workflows through advanced patterns like ReAct loops and Plan-and-Execute strategies. By balancing autonomy with oversight, this skill helps developers implement robust tool-use, sophisticated memory systems, and multi-agent orchestration while avoiding common pitfalls like infinite loops and tool overload.
Use Cases
- Autonomous Workflow Design: Implementing a Reason-Act-Observe (ReAct) cycle to allow agents to solve complex, multi-step problems by dynamically selecting and invoking tools.
- Multi-Agent System Orchestration: Architecting collaborative environments where a 'Planner' agent decomposes tasks and delegates them to specialized 'Executor' agents for higher efficiency.
- Reliable Tool Integration: Building dynamic tool registries with precise schemas and error handling to ensure LLMs can accurately call functions and recover from execution failures.
- Agent Memory Management: Designing selective memory systems that allow agents to retain critical context across long-running tasks without overwhelming the context window.
- Performance Debugging and Safety: Implementing iteration limits, tracing, and observability to prevent agent loops and ensure graceful degradation in production environments.
| name | ai-agents-architect |
|---|---|
| description | "Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling." |
| source | vibeship-spawner-skills (Apache 2.0) |
AI Agents Architect
Role: AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Capabilities
- Agent architecture design
- Tool and function calling
- Agent memory systems
- Planning and reasoning strategies
- Multi-agent orchestration
- Agent evaluation and debugging
Requirements
- LLM API usage
- Understanding of function calling
- Basic prompt engineering
Patterns
ReAct Loop
Reason-Act-Observe cycle for step-by-step execution
- Thought: reason about what to do next
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan-and-Execute
Plan first, then execute steps
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
Tool Registry
Dynamic tool discovery and management
- Register tools with schema and examples
- Tool selector picks relevant tools for task
- Lazy loading for expensive tools
- Usage tracking for optimization
Anti-Patterns
❌ Unlimited Autonomy
❌ Tool Overload
❌ Memory Hoarding
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Agent loops without iteration limits | critical | Always set limits: |
| Vague or incomplete tool descriptions | high | Write complete tool specs: |
| Tool errors not surfaced to agent | high | Explicit error handling: |
| Storing everything in agent memory | medium | Selective memory: |
| Agent has too many tools | medium | Curate tools per task: |
| Using multiple agents when one would work | medium | Justify multi-agent: |
| Agent internals not logged or traceable | medium | Implement tracing: |
| Fragile parsing of agent outputs | medium | Robust output handling: |
Related Skills
Works well with: rag-engineer, prompt-engineer, backend, mcp-builder