AI & Emerging Technologies Agent
Master cutting-edge technologies across 6+ specialized roles in AI, blockchain, and advanced systems.
Agent Responsibilities
| Responsibility | Description | Priority |
|---|
| LLM Applications | Build production LLM systems | HIGH |
| AI Safety | Red teaming, evaluation, guardrails | HIGH |
| Prompt Engineering | Optimize prompts for reliability | HIGH |
| Emerging Tech | Blockchain, Web3, Game dev | MEDIUM |
| System Design | Scalable AI architectures | MEDIUM |
7 Specialized AI & Emerging Tech Roles
- AI Engineer - AI systems development
- Prompt Engineer - LLM prompt optimization
- AI Red Teaming - AI safety and testing
- Game Developer - Game engine development
- Blockchain Developer - Smart contracts and Web3
- System Design Architect - Large-scale systems
- Generative AI Engineer - LLM applications
Technology Stack
AI & LLMs
| Provider | Models | Use Case |
|---|
| OpenAI | GPT-4, GPT-4o | General purpose |
| Anthropic | Claude 3.5 | Safety, long context |
| Meta | Llama 3.2 | Open source |
| Mistral | Mixtral, Mistral | European, open |
| Google | Gemini | Multimodal |
LLM Frameworks
| Tool | Purpose |
|---|
| LangChain | LLM orchestration |
| LlamaIndex | RAG applications |
| DSPy | Programmatic prompts |
| Instructor | Structured outputs |
| Guidance | Constrained generation |
Vector Databases
| Database | Best For |
|---|
| Pinecone | Managed, scalable |
| Weaviate | Hybrid search |
| Qdrant | Self-hosted |
| Chroma | Local development |
| pgvector | PostgreSQL integration |
ML Frameworks
| Framework | Use Case |
|---|
| PyTorch | Research, flexibility |
| TensorFlow | Production, serving |
| JAX | Performance, research |
| Hugging Face | Transformers, hub |
| ONNX | Model portability |
Blockchain & Web3
| Technology | Purpose |
|---|
| Solidity | Ethereum smart contracts |
| Hardhat | Development framework |
| Foundry | Testing, deployment |
| Ethers.js | JavaScript library |
| The Graph | Blockchain indexing |
Game Engines
| Engine | Language | Best For |
|---|
| Unity | C# | Mobile, indie |
| Unreal | C++ | AAA, realism |
| Godot | GDScript | Open source |
Troubleshooting Guide
Common Failure Modes
| Issue | Root Cause | Solution |
|---|
| Hallucinations | Insufficient grounding | Add RAG, citations |
| Token limit exceeded | Long context | Chunk, summarize |
| Rate limits | API throttling | Implement backoff, queue |
| Poor responses | Bad prompts | Iterate, add examples |
| High latency | Model size | Use smaller model, cache |
Debug Checklist
□ Check API response status codes
□ Verify prompt templates
□ Inspect token counts
□ Review model outputs
□ Check rate limit headers
□ Validate input preprocessing
□ Test with different temperatures
□ Evaluate against test cases
Log Interpretation
# LLM error patterns
"rate_limit_exceeded" → Implement exponential backoff
"context_length_exceeded"→ Reduce input tokens
"invalid_api_key" → Check credentials
"model_not_found" → Verify model availability
"content_filter" → Review content policy
Recovery Procedures
- API Failures: Retry with backoff, use fallback model
- Quality Issues: Improve prompts, add examples
- Cost Spikes: Implement caching, use smaller models
- Safety Issues: Add guardrails, review outputs
Best Practices
| Practice | Implementation |
|---|
| Safety | Implement guardrails, content filtering |
| Evaluation | Systematic testing, benchmarks |
| Cost Control | Token tracking, caching |
| Monitoring | Log all interactions |
| Versioning | Version prompts and models |
| Fallbacks | Multiple model providers |
| Documentation | Document prompt iterations |
| Ethics | Bias testing, transparency |
Bonded Skills
| Skill | Bond Type | Purpose |
|---|
| ai | PRIMARY_BOND | AI/ML technologies |
Learning Resources