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SAP Building Agentic Systems (Milvus Community Meetup)

This is Nicholas Roze-Freitas's speaker slides from the October Milvus Community Meetup.

Technology◦
Related topics:
Intelligent AI Agents•
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CONFIDENTIAL Building Agentic Systems By Nicholas Roze-Freitas
My Background • 10 Months in SAP Academy for Product & Engineering • Studied Data Science and Math at UCSB • Data Scientist building agents at SAP Concur
Agentic Systems? • Different categories of Agentic Systems • independent, fully autonomous • More prescriptive implementation that follows a flow • LangChain’s blog writes often times Agentic Systems in production are a combination
Complex Reasoning Dynamic Decision-making Unstructured Data When are Agents good to use?
Main Components Of An Agent • LLM • Tools • Memory • Short term • Long term Langgraph
Building Agents • Tons of frameworks • Langchain / Langgraph • CrewAI • Google ADK • OpenAI Agents SDK • … and more! • Agents themselves are pretty simple • Most frameworks implement the same loop Also Langgraph
The Loop • Feed Messages to LLM • If LLM called tools • Call tools & add results to messages • If LLM responds with text • Get user feedback if applicable • Or do something custom Again Langgraph
Some Hidden Challenges • Security • Malformed & incorrect tool calling • Message history and context management Agents Sometimes
Hidden Challenge: Security • LLMs can generate malicious outputs • User/company data isolation • Console commands • Solution? • Design interactions with implicit guardrails • User/company based instances of tools Agents Sometimes (again)
Hidden Challenge: Malformed Function Calls • LLMs can fail to generate function calls • Malformed function calling can break up flows • Solution? • Define tools with primitives • Implement custom error handling
Hidden Challenge: Context • Agents can forget things • Context helps agents understand their task and get information • Solution? • Keep most important info at top of context • Design RAG systems to pull context dynamically Agents Thinking Really Hard
My Library of choice: LangChain & LangGraph
Why I like Lang(Chain/Graph) • Offers high and low level APIs • Represents agentic systems as a graph • Low level APIs give the ability to build highly customizable solutions • Building blocks • Mitigations • Compliance Some Companies Using LangGraph
Getting Started • LangGraph/Langchain offers high level APIs that are easy to start with • create_agent • middleware • Or get into the weeds and just start with LangGraph!
What I’m Excited About • Fine-tuned small language models • Large model inference is expensive & unnecessary • Agent’s often handle specialized tasks
Thank you + Questions Connect with me on LinkedIn @ Nicholas Roze-Freitas