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Agentic AI – ServiceNow – Project Document
ServiceNow Autonomous Agent Business Report
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Lang Graph – Syllabus
Lang Graph Syllabus (1) Lang Graph Overview (2) Get Started Installation Quickstart Local Servers Changelog Thinking In LangGraph Workflows + Agents (3) Capabilities Persistence Checkpointers Stores Fault Tolerance Event Streaming Streaming Interrupts Time Travell Memory Subgraphs (4) Production Application Structure Test Backward Compatibility LangSmith Studio Agent Chat UI Deployment LangSmith Observability (5) Frontend Overview Graph Execution Custom Stream Channels (6) LangGraph APIs Graph API Functional API Studio
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Python Library: “os”
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Python Library : “getpass”
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Agentic AI Use Case -1: How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks .
How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks . %pip install autogen langgraph import getpass import os def _set_env(var: str): if not os.environ.get(var): os.environ[var] = getpass.getpass(f"{var}") _set_env("OPENAI_API_KEY") import autogen import os config_list =[{"model": "gpt-40", "api_key": os.environ["OPENAI_API_KEY"]}] llm_config = { "timeout": 600, "cache_seed":42, "config_list":config_list, "temperature": 0 } autogen_agent = autogen.AssistantAgent( name = "assistant", llm_config = llm_config, ) from langchain_core.messages import convert_t0_openai_message, BaseMessage from langgrapg.func import entrypoint , task from langgraph.graph import add_messages from langgraph.checkpoint.memory import InMemorySaver @task def call_autogen_agent(messages: list[BaseMessage]): # Convert To An openai-style Messages messages = convert_to_openai_messages(messages) response = user_proxy.initiate_chat( autogen_agent, message = messages[-1], #
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RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ?
RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ? Best Practices : Set 1 Best Practices : Set 2 Real Life Analogy
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RAG – What Is Distibuted Tracing & How To Implement It ?
RAG – Explain Me About Distributed Tracing ?
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RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
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RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
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RAG – HNSW Indexing Technique .
