Ai Agents Architect

davila7/claude-code-templates/cli-tool/components/skills/ai-research/ai-agents-architect

by davila78da17d671b6fNo license32K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

Instructions onlyAI & Agents
AI-generated overview

Guides the design of autonomous AI agents, covering tool use, memory, planning strategies and multi-agent orchestration.

What it does
This skill provides architectural guidance for building autonomous AI agents that stay controllable. It covers agent architecture design, tool and function calling, memory systems, planning and reasoning strategies, multi-agent orchestration, and agent evaluation and debugging. It also documents recurring patterns such as the ReAct loop, plan-and-execute, and a tool registry, plus anti-patterns and failure-mode warnings with suggested fixes.
When to use it
Use it when designing or reviewing an autonomous agent, deciding how tools and function calling should be structured, or choosing between planning strategies and single- versus multi-agent setups. It also fits when diagnosing common agent failure modes such as runaway loops, vague tool descriptions, or untraceable internals.
Requirements
No scripts are shipped; it is instructions only. It assumes familiarity with LLM API usage, function calling, and basic prompt engineering.

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

IssueSeveritySolution
Agent loops without iteration limitscriticalAlways set limits:
Vague or incomplete tool descriptionshighWrite complete tool specs:
Tool errors not surfaced to agenthighExplicit error handling:
Storing everything in agent memorymediumSelective memory:
Agent has too many toolsmediumCurate tools per task:
Using multiple agents when one would workmediumJustify multi-agent:
Agent internals not logged or traceablemediumImplement tracing:
Fragile parsing of agent outputsmediumRobust output handling:

Related Skills

Works well with: rag-engineer, prompt-engineer, backend, mcp-builder

Source and attribution

Source:davila7/claude-code-templatesincli-tool/components/skills/ai-research/ai-agents-architectat commit8da17d6

License: No license

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