This hands-on engagement will equip you with everything you need to understand, build, and deploy your coded agent—combining the power of UiPath’s Agentic SDK with cutting-edge AI framework like Llama-index. Whether you’re building automation today or architecting the solutions of tomorrow, this workshop is your gateway to future-proof skills and practical learning in the agentic world🚀 🗓️Agenda: Introduction to Coded Agents Use case overview (end goal & context setting) UiPath SDK – quick walk-through Modern agent frameworks Llama-index overview RAG fundamentals Hands -on- Step-by-Step: building a UiPath coded agent starting from existing Llama-index code V2 enhancement: agent interaction with UiPath platform components (with LlamaIndex being preferred for RAG use cases, the focus will remain on RAG) Q&A + Open discussion, clarifications. ❗ Prerequisites (for hands-on experience) Bring your laptop Python 3.10+ Code editor (VS Code or equivalent) UiPath Automation Cloud account Sign in to cloud.uipath.com Enable preview features in your organisation settings 🎯 Good to Have Basic Python scripting knowledge Understanding of Agent fundamentals See you on the other side of the screen super soon🎉😎

1. Coded Agents – with UiPath
SDK + LlamaIndex
Virtual Hands-on Workshop
For Session Artefacts: Click here

2. 2
Senior RPA Developer
UiPath
RPA Developer I
UiPath
RPA Developer II
UiPath
Anshuman Rai Shivaansh Srivastava Adarsh Thomas
Speakers
2

3. 3
Agenda
• Use case overview (end goal & context setting)
• Introduction to Coded Agents
• UiPath SDK – quick walk-through
• Modern agent frameworks
• Llama-index overview
• RAG fundamentals
• Hands-on:
Step-by-Step: building a UiPath coded agent starting from existing
Llama-index code
V2 enhancement: agent interaction with UiPath platform components
(with LlamaIndex being preferred for RAG use cases, the focus will
remain on RAG)
• Q&A + Open discussion, clarifications.

4. How UiPath is enabling
Agentic Automation?

5. 5
Unified Intelligent Automation Platform
UiPath Platform integrates AI agents, robots, and people into a single automation ecosystem —
enabling autonomous execution across enterprise workflows.
Agentic Automation Defined
Software agents powered by advanced AI (LLMs, GenAI) that think, plan, and act autonomously
— expanding beyond traditional RPA’s rule-based task execution.
Agents think. Robots do. People lead.
This central design principle emphasizes human strategic oversight while agents make decisions
and robots handle execution.
UiPath: Agentic Automation Platform

6. 6
AI Agents
AI agents plan, adapt, and complete complex, context-aware tasks — not just repetitive work.
Agentic Orchestration
Orchestrates hybrid work between humans, AI agents, and robots — providing end-to-end visibility and
control.
Enterprise-Grade Governance & Security
Built-in compliance, auditability, and secure integrations ensure safe scaling across the organization.
Low-Code to Pro-Code Flexibility
Supports rapid development and deployment of agents using low-code and pro-code tools —
accelerating automation ROI.
Why UiPath is an Agentic Automation Platform?

7. Travel HelperAgent
What are we Building
Today?

8. 8
Travel Helper Agent
Travel helper agent demonstrates the implementation of a Retrieval-Augmented
Generation (RAG) system using UiPath Context Grounding.
Agent Responsibilities
 Accepts a user query with details about an upcoming business trip.
 Verifies if new data should be added to RAG indexes.
 Initializes two QueryEngineTools, each based on distinct context-grounding
indexes.
 Breaks down the initial query into multiple sub-questions.
 Assigns ReAct agents to address each sub-question.
 Combines the responses to generate a travel summary, including the permitted
budget, employee preferences, and recommendations.

9. 9
Travel Helper Agent Flow

10. Beyond Drag-and-Drop: The Power of Code in
AgenticAutomation
How Coded Agents are
Changing the Automation
Game?

11. 11
A Simple Agent, A Complex Problem

12. 12
What is a Coded Agent?
A coded agent is an AI-powered automation built using programming languages
(Python) that gives you complete control over logic, integrations, and behavior.
Core Characteristics:
→ Written in code (Python) using frameworks like LangGraph
→ Full programmatic control over state, logic, and integrations
→ Deploys to UiPath Cloud Platform like any other automation
→ Integrates with LLMs, databases, APIs, and UiPath services

13. 13
UiPath Agent Builder vs UiPath Coded Agents
Aspect Agent Builder (Low-Code) Coded Agents
Complexity Handling Good for linear workflows
Handles complex loops, recursion, state
management
Development Speed Fast for simple cases Slower initially, faster iterations
Custom Integrations Connector Builder for APIs Same + custom logic, error handling
State Management Basic session handling Full control with Workflow
Debugging UI-based, limited visibility Full IDE support, logging, testing

14. 14
Lifecycle of a Coded Agent

15. Let's talk about UiPath SDK

16. 16
UiPath SDK
Language/Framework Functionality
Python
Provides a CLI to create, package, and deploy agents while
enabling programmatic interaction with UiPath Platform
resources such as processes, assets, and buckets.
Python with LangGraph
Enables developers to build and deploy LangGraph agents on
the UiPath Platform, offering programmatic access to UiPath
services with human-in-the-loop support via Action Center.
LLamaIndex
Enables developers to build and deploy LlamaIndex agents on
the UiPath Platform with human-in-the-loop (HITL) support and
programmatic interaction with UiPath services.

17. 17
 Python 3.10 or higher
 pip or uv package manager
 An IDE (VS Code) of your choice
 A UiPath Cloud Platform account with appropriate permissions
Prerequisite
UiPath SDK

18. 18
UiPath SDK
Purpose Command
Creating Virtual Environment python -m venv .venv
Activating Virtual Environment .venv/Scripts/activate
Installing UiPath LLamaIndex Package pip install uipath-llamaindex
Creating a new Agent uipath new <agent_name>
Authenticating with UiPath uipath auth
Initializing the Project uipath init
Executing the Project uipath run agent <Schema>
Packing the Project uipath pack
Publishing the Project uipath publish

19. Modern Agent Frameworks

20. 20
Challenge LLMs alone are stateless and hard to control
Integrating tools, data, and multi-step logic is brittle
Limited visibility into why an agent behaved a certain way
What
frameworks
provide
Lightweight orchestration for tools & prompts
State & flow control for multi-step tasks
Tracing & evaluation for production use
Why do we need frameworks?
Modern Agent Frameworks

21. 21
Modern Agent Frameworks
Adds structured, stateful
workflows for more
reliable agents
Observability and
evaluation for debugging
and monitoring agents
Simple orchestration layer
to connect LLMs with tools
and prompts
Data-focused framework
to connect LLMs with
enterprise knowledge

22. Retrieval-Augmented
Generation (RAG)

23. 23
Overview
Retrieval-Augmented Generation (RAG)
Enhances coded agents with enterprise and process data
• Automation artifacts (workflows, logs, run data)
• Business documents and internal knowledge bases
• Enterprise systems via APIs and databases
Uses LlamaIndex to connect agents to:
• No access to real-time or process-specific data
• Hallucinations in decision-making
• Limited reasoning without context
Solves key challenges in agentic automation:
Grounds agent actions in retrieved, verifiable context
Enables agents to reason → retrieve → act safely at runtime

24. 24
Core Workflow
Retrieval-Augmented Generation (RAG)
Ingest
Documents
(PDFs, Word,
etc.)
APIs
Databases
Chunk
Chunk data
and convert
into vector
embeddings
Store
Store
embeddings
in a vector
index
Convert
Convert user
query into an
embedding
Perform
Perform
semantic
similarity search
(top-k)
Send
Send
retrieved
context to
LLM for
grounded
response
generation

25. LlamaIndex

26. 26
LlamaIndex
Data Workflow
Open-source framework built to simplify RAG
Bridges raw data and large language models
• Structured data (SQL, tables)
• Semi-structured data (JSON)
• Unstructured data (text, PDFs, images)
Supports:
160+ built-in and community data connectors
Loads data as Documents with metadata
Prepares data for indexing and querying

27. 27
LlamaIndex
Indexing, Querying & Agents
VectorStoreIndex for semantic search
Documents split into Nodes (atomic chunks)
Each node converted into a vector embedding
Query pipeline:
• Retrieval
• Post-processing (reranking, filtering)
• Response synthesis
Supports LLM-powered data agents
• Multi-step reasoning (ReAct)
• Tool and API usage
• Stateful interactions

28. Hands-on

29. Tell us what you think!

30. Q & A