Update README and add agentic example (#374)

Author: v1r3n

README.md

@@ -67,6 +67,21 @@ See: `task_context_example.py`, `worker_example.py`
 
 ---
 
+### AI/LLM Workflows
+
+See [agentic_workflows/](agentic_workflows/) for the full set of AI agent examples.
+
+| File | Description | Run |
+|------|-------------|-----|
+| **agentic_workflows/llm_chat.py** | Automated multi-turn LLM chat | `python examples/agentic_workflows/llm_chat.py` |
+| **agentic_workflows/llm_chat_human_in_loop.py** | Interactive chat with WAIT task pauses | `python examples/agentic_workflows/llm_chat_human_in_loop.py` |
+| **agentic_workflows/multiagent_chat.py** | Multi-agent debate with moderator routing | `python examples/agentic_workflows/multiagent_chat.py` |
+| **agentic_workflows/function_calling_example.py** | LLM picks Python functions to call | `python examples/agentic_workflows/function_calling_example.py` |
+| **agentic_workflows/mcp_weather_agent.py** | AI agent with MCP tool calling | `python examples/agentic_workflows/mcp_weather_agent.py "What's the weather?"` |
+| **rag_workflow.py** | RAG pipeline: markitdown, pgvector, search, answer | `python examples/rag_workflow.py file.pdf "question"` |
+
+---
+
 ### Monitoring
 
 | File | Description | Run |
@@ -174,6 +189,65 @@ python examples/prompt_journey.py
 
 ---
 
+### RAG Pipeline Setup
+
+Complete RAG (Retrieval Augmented Generation) pipeline example:
+
+```bash
+# 1. Install dependencies
+pip install conductor-python "markitdown[pdf]"
+
+# 2. Configure (requires Orkes Conductor with AI/LLM support)
+#    - Vector DB integration named "postgres-prod" (pgvector)
+#    - LLM provider named "openai" with a valid API key
+export CONDUCTOR_SERVER_URL="http://localhost:7001/api"
+
+# 3. Run RAG workflow
+python examples/rag_workflow.py examples/goog-20251231.pdf "What were Google's total revenues?"
+```
+
+**Pipeline:** `convert_to_markdown` → `LLM_INDEX_TEXT` → `WAIT` → `LLM_SEARCH_INDEX` → `LLM_CHAT_COMPLETE`
+
+**Features:**
+- Document conversion (PDF, Word, Excel → Markdown via [markitdown](https://github.com/microsoft/markitdown))
+- Vector database ingestion into pgvector with OpenAI `text-embedding-3-small` embeddings
+- Semantic search with configurable result count
+- Context-aware answer generation with `gpt-4o-mini`
+
+---
+
+### MCP Tool Integration Setup
+
+MCP (Model Context Protocol) agent example:
+
+```bash
+# 1. Install MCP weather server
+pip install mcp-weather-server
+
+# 2. Start MCP server
+python3 -m mcp_weather_server \
+  --mode streamable-http \
+  --host localhost \
+  --port 3001 \
+  --stateless
+
+# 3. Run AI agent
+export OPENAI_API_KEY="your-key"
+export ANTHROPIC_API_KEY="your-key"
+python examples/agentic_workflows/mcp_weather_agent.py "What's the weather in Tokyo?"
+
+# Or simple mode (direct tool call):
+python examples/agentic_workflows/mcp_weather_agent.py "Temperature in New York" --simple
+```
+
+**Features:**
+- MCP tool discovery
+- LLM-based planning (agent decides which tool to use)
+- Tool execution via HTTP/Streamable transport
+- Natural language response generation
+
+---
+
 ## 🎓 Learning Path (60-Second Guide)
 
 ```bash
@@ -189,7 +263,11 @@ python examples/worker_configuration_example.py
 # 4. Workflows (10 min)
 python examples/dynamic_workflow.py
 
-# 5. Monitoring (5 min)
+# 5. AI/LLM Workflows (15 min)
+python examples/agentic_workflows/llm_chat.py
+python examples/rag_workflow.py examples/goog-20251231.pdf "What were Google's total revenues?"
+
+# 6. Monitoring (5 min)
 python examples/metrics_example.py
 curl http://localhost:8000/metrics
 ```
@@ -214,6 +292,15 @@ examples/
 │   ├── workflow_status_listner.py      # Workflow events
 │   └── test_workflows.py               # Unit tests
 │
+├── AI/LLM Workflows
+│   ├── rag_workflow.py                 # RAG pipeline (markitdown + pgvector)
+│   └── agentic_workflows/             # Agentic AI examples
+│       ├── llm_chat.py                # Multi-turn LLM chat
+│       ├── llm_chat_human_in_loop.py  # Interactive chat with WAIT
+│       ├── multiagent_chat.py         # Multi-agent debate
+│       ├── function_calling_example.py # LLM function calling
+│       └── mcp_weather_agent.py       # MCP tool calling agent
+│
 ├── Monitoring
 │   ├── metrics_example.py              # Prometheus metrics
 │   ├── event_listener_examples.py      # Custom listeners
@@ -245,14 +332,11 @@ examples/
 │   └── other_workers/
 │
 └── orkes/                             # Orkes-specific features
-    ├── ai_orchestration/              # AI/LLM integration
-    │   ├── open_ai_chat_gpt.py
-    │   ├── open_ai_function_example.py
-    │   └── vector_db_helloworld.py
-    └── workers/                       # Advanced patterns
-        ├── http_poll.py
-        ├── sync_updates.py
-        └── wait_for_webhook.py
+    ├── vector_db_helloworld.py        # Vector DB operations
+    ├── agentic_workflow.py            # AI agent (AIOrchestrator)
+    ├── http_poll.py
+    ├── sync_updates.py
+    └── wait_for_webhook.py
 ```
 
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