Agentic AI Skills

The Agentic Stack

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I work across the full agentic AI stack, from foundation models to production deployment:

flowchart TB
    subgraph LAYER4["🎨 Presentation Layer"]
        AGUI[AG-UI Protocol]
        A2UI[A2UI Blueprints]
    end
    
    subgraph LAYER3["🤝 Coordination Layer"]
        A2A[A2A Protocol]
        MULTI[Multi-Agent Orchestration]
    end
    
    subgraph LAYER2["🔧 Tools & Data Layer"]
        MCP[MCP Protocol]
        RAG[RAG Systems]
        TOOLS[Tool Integration]
    end
    
    subgraph LAYER1["🧠 Foundation Layer"]
        LLM[Foundation Models]
        EMB[Embeddings]
    end
    
    LAYER4 --> LAYER3
    LAYER3 --> LAYER2
    LAYER2 --> LAYER1

Core Competencies

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🤖 Agent Development

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🔗 Agentic Protocols

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Deep understanding of the emerging protocol stack:

MCP (Model Context Protocol)

• Tool and resource standardization
• Server/client architecture
• Enterprise security patterns

A2A (Agent-to-Agent)

• Capability discovery
• Task delegation and lifecycle
• Cross-agent coordination

AG-UI / A2UI

• Declarative UI blueprints
• Safe agent-to-user interfaces
• Event-based streaming

🏗️ Agent Infrastructure

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┌─────────────────────────────────────────────────────────┐
│                  Production Agent Stack                  │
├─────────────────────────────────────────────────────────┤
│  Observability    │ AgentOps, LangSmith, CloudWatch     │
│  Guardrails       │ Bedrock Guardrails, custom filters  │
│  Evaluation       │ LLM-as-judge, automated evals       │
│  Deployment       │ Lambda, ECS, SageMaker endpoints    │
│  State Management │ DynamoDB, Redis, JSONL transcripts  │
│  Vector Storage   │ OpenSearch, Pinecone, pgvector      │
└─────────────────────────────────────────────────────────┘

Framework Proficiency

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Cloud Platforms

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AWS Bedrock AWS SageMaker Azure OpenAI Google Vertex AI

Agent Frameworks

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LangChain LangGraph AWS AgentCore Runtime CrewAI AWS Strands Google ADK AutoGen

Foundation Models

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Claude OpenAI Gemini Llama Mistral

Real-World Applications

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Enterprise AI Agents

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Building agents that handle real banking workflows:

• Document processing and extraction
• Customer query routing
• Compliance checking
• Internal knowledge retrieval

RAG-Enhanced Agents

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Combining retrieval with reasoning:

• Hybrid search (semantic + keyword)
• Chunking strategies for domain documents
• Citation and source tracking
• Incremental index updates

Agentic Evaluation

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Ensuring agents work reliably:

• Trajectory evaluation
• Tool use accuracy
• Hallucination detection
• Latency optimization

Open Source Contributions

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MiniClaw

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A minimal agent orchestration framework in Python demonstrating core patterns:

• OpenClaw-style session management
• Workspace-based memory (SOUL.md, MEMORY.md)
• Multi-provider support (OpenAI, Anthropic, Ollama)
• ~2,800 lines of readable, educational code

Certifications & Training

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• AWS Certified Solutions Architect
• Deep Learning Specialization (Coursera)
• MLOps specialization (ongoing)