ai-agents-architect

• Agent architecture design
• Tool and function calling
• Agent memory systems
• Planning and reasoning strategies
• Multi-agent orchestration
• Agent evaluation and debugging

• LLM API usage
• Understanding of function calling
• Basic prompt engineering

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

```

❌ Unlimited Autonomy

❌ Tool Overload

❌ Memory Hoarding

| 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: |

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