ai-agents-architect

name	ai-agents-architect
description	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.
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	2026-02-27

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:
Agent workflows lost on crash or restart	high	Use durable execution (e.g. DBOS) to persist workflow state:

Related Skills

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.