Agent skill

ai-agents-architect

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.

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Install this agent skill to your Project

npx add-skill https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/ai-research/ai-agents-architect

SKILL.md

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

javascript
- 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

javascript
- 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

javascript
- 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

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