AI Agents Architect
SkillDocs & knowledgeThis is an architect skill that turns an AI agent into an advisor for designing and building autonomous AI agents. It covers tool use, memory systems, planning patterns like ReAct and plan-and-execute, and multi-agent orchestration, and it flags common pitfalls such as unlimited loops, overloaded tools, and hoarded memory with fixes for each.
Available today. Use it from your connected AI after setup.
No other account needed.
Have access to an LLM API and a basic understanding of function calling and prompt engineering.
Then ask your AI: use the AI Agents Architect skill
What your AI can do with it
- Design agent architectures that balance autonomy with oversight and clear failure modes
- Advise on tool and function calling, including tool registries with schemas and lazy loadi
- Set up agent memory systems with selective memory instead of storing everything
- Apply planning patterns like ReAct loops and plan-and-execute with replanning
- Design multi-agent orchestration and judge when one agent would suffice
- Evaluate and debug agents, including iteration limits, tracing, and robust output handling
Getting started
- Have access to an LLM API and a basic understanding of function calling and prompt engineering.
- Add the ai-agents-architect skill to the agent's available skills.
- Ask the agent for help when building an agent, working on tool use, or designing autonomous agent behavior.
- Use its guidance on patterns like ReAct and plan-and-execute, and apply its fixes for pitfalls like missing iteration limits or tool overload.
What this skill tells your AI
The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/ai-agents-architect/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 32k
- Forks
- 4k
- Last commit
- Sep 2026
Others that do the same job
Questions
- When should I use this skill?
- Use it when building an agent, an AI agent, or an autonomous agent, or when working on tool use and function calling.
- What planning patterns does it cover?
- It covers the ReAct loop (reason-act-observe cycles with max iteration limits) and plan-and-execute, where a task is decomposed into steps, executed, and replanned based on results.
Advanced
- Item type
- skill
- Key
ai-agents-architect-davila7- Source
- github.com/davila7/claude-code-templates