# Techwavedev Agi Agent Kit AI Agents Architect

> AI Agents Architect

- Skill: `tomevault-io/techwavedev-agi-agent-kit-ai-agents-architect` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/techwavedev-agi-agent-kit-ai-agents-architect`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/techwavedev-agi-agent-kit-ai-agents-architect/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/techwavedev-agi-agent-kit-ai-agents-architect

---


# 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

| 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`


---

## 🧠 AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:
```bash
python3 execution/memory_manager.py auto --query "<task summary>"
```

**Decision Tree:**
- **Cache hit?** Use cached response directly — no need to re-process.
- **Memory match?** Inject `context_chunks` into your reasoning.
- **No match?** Proceed normally, then store results:

```bash
python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags ai-agents-architect <relevant-tags>
```

> **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

### Agent Team Collaboration

- **Strategy**: This skill communicates via the shared memory system.
- **Orchestration**: Invoked by `orchestrator` via intelligent routing.
- **Context Sharing**: Always read previous agent outputs from memory before starting.

### Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:
- Embeddings: `nomic-embed-text` via Qdrant memory system
- Lightweight analysis: Local models reduce API costs for repetitive patterns

---
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<!-- tomevault:4.0:skill_md:2026-04-13 -->