Key Technical Stack

Quick Start

cd ~/Desktop/enterprise-agentic-ai-platform
cp .env.example .env
# Fill in: ANTHROPIC_API_KEY, LANGSMITH_API_KEY, OPENAI_API_KEY

# Full stack with Docker Compose
make dev-full

# Or individual services
make mcp-start    # Start 3 MCP servers (:8001, :8002, :8003)
make dev          # FastAPI backend on :8000
cd frontend && npm install && npm run dev   # Next.js on :3000

MCP Servers

All MCP servers use Streamable HTTP transport (the April 2026 standard — SSE deprecated).

Agent Loop

# Planner → Executor → Critic → (revise?) loop
def should_revise(state: AgentState):
    if state.needs_revision and state.revision_count < MAX_REVISIONS:  # MAX=3
        return "executor"
    return "END"

# Graph nodes
graph.add_node("memory_retrieval", memory_retrieval_node)
graph.add_node("planner", planner_node)
graph.add_node("executor", executor_node)
graph.add_node("critic", critic_node)

# Critic loop — prevents infinite revision
graph.add_conditional_edges("critic", should_revise, {
    "executor": "executor",
    "END": END
})

RAG Pipeline

• Ingestion: LlamaIndex IngestionPipeline with SemanticSplitterNodeParser
• Vector Store: Qdrant with enable_hybrid=True (dense + sparse BM25)
• Retrieval: Score-threshold filtering + sorted by relevance
• Supported Sources: S3 URIs, local PDFs, URLs

# Ingest documents
curl -X POST http://localhost:8000/api/v1/documents/ingest \
  -F "files=@annual_report.pdf"

WebSocket Streaming

// Client receives agent step-by-step reasoning in real time
ws.onmessage = (event) => {
  const { type, node, data } = JSON.parse(event.data);
  if (type === "node_update") {
    // Show agent reasoning: planner → executor → critic
    appendAgentStep(node, data);
  }
};

Running Tests

make test       # All tests
make test-cov   # With coverage
pytest tests/test_agents.py -v -s   # Agent tests with output

Deployment

# Terraform: EKS (us-east-1) + RDS + ElastiCache + S3
cd infra/terraform && terraform init && terraform apply

# CI/CD: GitHub Actions
# push to main → pytest → ECR push → kubectl rolling update

Kubernetes: HPA scales 3→20 replicas on CPU (70%) and memory (80%).

Resume Bullet Points 🚀

• Designed production multi-agent RAG platform using LangGraph v0.3 StateGraph with Planner-Executor-Critic loop, supporting 3× self-revision and human-in-the-loop interruption
• Built 3 production MCP servers (PostgreSQL, Document, Notification) using FastMCP + Streamable HTTP transport (April 2026 standard) with asyncpg connection pooling
• Implemented hybrid RAG pipeline (dense + sparse search) with LlamaIndex, Qdrant, semantic chunking, and Cohere reranking; ingests PDFs, S3 documents, URLs
• Architected real-time WebSocket streaming API with FastAPI and Next.js 14 showing live agent reasoning; falls back to REST seamlessly
• Integrated LangSmith + Langfuse tracing, Prometheus custom metrics (tokens, latency, tool calls), and Grafana dashboards for full observability
• Deployed on AWS EKS with HPA (3→20 replicas), Terraform IaC, ArgoCD GitOps, GitHub Actions CI/CD

Folder Structure

enterprise-agentic-ai-platform/
├── backend/
│   ├── agents/         # state, graph, planner, executor, critic, memory
│   ├── rag/            # ingestion pipeline + retriever (LlamaIndex + Qdrant)
│   ├── mcp_servers/    # postgres_mcp, document_mcp, notification_mcp
│   ├── api/            # FastAPI routes (health, documents)
│   ├── observability/  # LangSmith + Prometheus metrics
│   └── main.py         # FastAPI app (REST + WebSocket)
├── frontend/           # Next.js 14 chat UI + agent trace sidebar
├── infra/
│   ├── k8s/            # Deployment, HPA, ConfigMaps
│   ├── terraform/      # EKS, RDS, ElastiCache, S3
│   ├── docker/         # Backend + frontend Dockerfiles
│   └── .github/        # CI/CD workflow
├── tests/              # pytest-asyncio agent tests
├── docker-compose.yml  # Full local stack (11 services)
└── Makefile            # All dev commands