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In-person Workshop at GIKI Campus, Swabi

Agentic AI Workshop

atomcamp, in collaboration with Ghulam Ishaq Khan Institute (GIKI), is organizing a 2-day in-person workshop at GIKI Campus, Swabi.

This hands-on workshop focuses on Agentic AI architectures and Retrieval-Augmented Generation (RAG). It is designed for students, developers, and data professionals who want to move beyond basic LLM API usage and learn how to build context-aware AI systems using LangChain and modern agent frameworks.

Fee: PKR 20,000

  • 18th & 19th April
  • 10:00 AM - 04:00 PM (Saturday, Sunday)
Application Closed
  • Accommodation is available on campus. Students may stay in hostel common rooms, or choose the GIKI Guest House (paid accommodation).

STANDARD

PKR 20,000
  • For atomcamp & GIKI Alumni
    PKR: 10,000
  • Prerequisite
  • Proficiency in Python and basic understanding of LLM APIs (OpenAI/Anthropic)
  • Accommodation at GIKI
  • For Students: 
    Complimentary shared hostel accommodation(No Charges)

  • GIKI Guest House
    PKR 5,500 per room per night
data science, Data science bootcamp, Data science trainings, AI Bootcamp, Artificial Intelligence Bootcamp

A 2-day in-person workshop, organized by atomcamp in collaboration with Ghulam Ishaq Khan Institute (GIKI), Swabi, is a hands-on program designed to help students, and professionals transition from simple generative AI use cases to structured, intelligent AI systems.

Across two hands-on days, you’ll learn how modern AI agents think, plan, remember information, and use tools. You’ll also build Retrieval-Augmented Generation (RAG) pipelines that allow AI models to answer questions using your own data instead of guessing.

The focus will be on the practical application of agentic ai tools and concepts. You will work with LangChain, vector databases, memory systems, and real implementation patterns. At the end of Day 2, you will have built a working Knowledge Agent that can understand and respond to queries from a private dataset.

This workshop is ideal for developers, students, and professionals building AI products, ai solutions, copilots, or automated workflows.

Here’s What You’ll Learn:

  • How modern AI agents reason, plan, and act
  • Designing memory-aware AI systems
  • Building RAG pipelines for private data
  • LangChain & LCEL best practices
  • Architecting AI systems that scale beyond demos

Curriculum

The Shift from RAG to Autonomy: Why Agents?

  • From static chatbots to autonomous AI systems

  • Motivation behind Agentic AI and enterprise value

  • Limitations of traditional RAG pipelines


The P-P-A-R-M Framework

  • Perception: Understanding inputs and environment

  • Planning: Task decomposition and goal setting

  • Action: Tool usage and execution

  • Reflection: Self-evaluation and correction

  • Memory: Short-term and long-term context


Architectural Foundations

  • Layers of reasoning, orchestration, and execution

  • Designing scalable agent workflows

  • Separation of concerns in agent systems

Hands-On Lab: Build and configure a basic AI agent with defined identity, goals, and structured prompts.

LangChain Core Concepts

  • LangChain Expression Language (LCEL)

  • Building composable AI chains

  • Prompt templates and orchestration

  • Managing state with ChatMessageHistory


RAG Data Pipeline

  • Document loading and preprocessing

  • Text chunking strategies

  • Vector embeddings and similarity search

  • Context injection into prompts


Vector Databases

  • Introduction to vector databases

  • Implementing ChromaDB

  • Indexing and retrieval workflows

Hands-On Lab: Create a Private Knowledge Bot that answers questions from uploaded documents using semantic search.

Tool Binding & Agent Types

  • Converting Python functions into AI tools

  • Pydantic validation for tool inputs

  • Zero-shot, Conversational, and Structured agents


Error Handling & Self-Correction

  • Tool invocation strategies

  • Self-correction loops

  • Handling failed actions gracefully

Hands-On Lab: Develop an AI agent that invokes external APIs and executes real-world tasks with validation.

Agent Roles & Collaboration

  • Defining agent roles and backstories

  • Researcher, Writer, Reviewer personas

  • Context sharing between agents


Task Orchestration

  • Sequential and hierarchical workflows

  • Designing collaborative pipelines

  • Coordinating multiple agents toward one goal

Hands-On Lab: Build a multi-agent content engine for research, drafting, and automated review.

Containerization & Deployment

  • Dockerizing the backend (FastAPI) and frontend (Streamlit / web UI)

  • Environment configs: .env, secrets, and build-time vs run-time variables

  • Local-to-prod parity (same setup across environments)


Cloud Deployment Strategies

  • Serverless backend deployment (functions / APIs)

  • Static hosting for frontend (fast delivery + scalable)

  • Managing API keys, rate limits, and secure access in production


Monitoring & Observability

  • Logging best practices for agent workflows

  • Request tracing across the stack (agent → tools → APIs)

  • Basic monitoring for latency, failures, and cost tracking

Final Project

  • Deploy the full-stack Agent Web App to a production environment

  • Live endpoint + working UI + monitoring enabled

Who This Is For

For Students & Fresh Graduates

Student building AI-powered products

For Professionals

Professionals wanting to upskill fast

For Career Changers

Anyone seeking career growth through AI tools

For Freelancers & Entrepreneurs

Freelancers, entrepreneurs, and side-hustlers

Meet Our Incredible Trainers

Dr Ahmar Rashid

Fareeha Amjad

Soman Ali

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