WORKSHOP
The Agentic Stack: Designing and Deploying Autonomous Workflows and Agents (WS11)
Date: 08 October 2026 | Time: 03:30 to 05:30 PM
Venue: Workshop Room 2, NIMHANS Convention Centre, Bangalore
FEES:
• Rs.299 for Leaders Pass holders
• Rs.2009 for Professionals Pass holders
• Rs.2249 for Knowledge Pass holders
• Rs.2699 for Community++ or Student Pass holders
• Rs.2999 for all others
(Limited seats available)
A technical deep dive into turning static LLMs into dynamic agents that can use tools, search data, and reason
Arvind Devaraj
AI / LLM Engineer, Juspay
- This session is for decision-makers and engineers looking to make informed choices in real-world AI projects
- Foundations — embeddings, similarity metrics
- Transformers — the engine behind foundation models
- Vector databases — long-term memory that stores data as embeddings
- RAG evolution — moving beyond simple vector search to Agentic RAG
- Reasoning models — Chain of Thought, and what powers models like DeepSeek-R1
- Tool calling — how models learn to interact with external systems, APIs, and datastores
- Agents — combining LLMs with tool calling and persistent memory
- The bigger picture — an overview of LLM training, fine-tuning, alignment, and inference
Benefits/Takeaways of this workshop for the attendees (What will attendees do after attending the workshop which they were not able to do before attending this)
A workshop designed to provide conceptual clarity for leaders and working code for engineers.
You’ll get the most out of this session with a baseline understanding of:
- LLM basics — familiarity with tokens, context windows, and the difference between training and inference
- Technical setup — a laptop with an IDE and Python installed, and/or familiarity with Google Colab
- API access — an API key for an LLM provider (OpenAI, Anthropic, or Gemini)
About Speakers
Arvind has a strong understanding of Computer Science and Machine Learning. He
secured All India Rank 7 ( out of 53,000 aspirants ) in GATE Computer Science exam. He has demonstrated record of success taking products from conceptual stage to production and has interest in applying Data Science for Business problems