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ai-agents-architect

ai-agents-architect

github.com/davila7Agent workflows33.0k685 installsUpdated 1d ago
Repository

About ai-agents-architect

A skill for AI agent system architects, covering architecture, tool use, memory, planning, and evaluation. It emphasizes controlling autonomy through limits, error handling, and traceability.

Key features

  1. 01Agent architecture design
  2. 02Tool and function calling
  3. 03Memory system design
  4. 04Planning and reasoning strategies
  5. 05Multi-agent orchestration
  6. 06Evaluation and debugging

Use cases

  1. 01Design agents with oversight
  2. 02Plan stepwise execution and replanning
  3. 03Manage tool registration and lazy loading
  4. 04Diagnose loops, errors, and missing traces

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/davila7/claude-code-templates --skill ai-agents-architect

After installing, type/ai-agents-architectin Claude Code to confirm it loaded.

Details

From the project README

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

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

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