Introduction to SDLC and Generative AI | AI-Powered Software Development – GenAInstein
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Agentic AI Development

Instructor: Arun Kumar A R Instructor - Brisa Technologies

Centre for Development of Advanced Computing (C-DAC)

Course Fees: ₹4,999/-

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Starting On:
May 1, 2025

Format:
Online + Interactive Demo
Language:
English
Duration:
3 hours
Certificate:
Digital Copy
Audience:
Students of VTU 7th & 8th Semester Engineering...

Course Overview

This course provides a foundational and advanced exploration of artificial intelligence and deep learning technologies. It covers key concepts from data science, probability, and statistics to machine learning pipelines, neural networks, and deep learning architectures such as CNNs and RNNs. Students gain practical experience in model deployment, MLOps, and professional skills development, including Agile collaboration and ethical AI practices. The curriculum balances theoretical understanding with hands-on programming and a capstone project to solidify learning.

What You'll Learn

Undersand the foundational principles of Agentic AI, including agent architectures, autonomy levels, and the differences from traditional LLM applications.
Design, build, and deploy both single-agent and multi-agent solutions using practical frameworks such as LangChain, AutoGen, and OpenAI Operator.
Integrate Retrieval-Augmented Generation (RAG) pipelines to enable agents to dynamically leverage external knowledge for informed decision-making.
Master advanced prompt engineering for agent behavior adjustment, tool integration, and reliable chaining of complex workflows.
Implement and evaluate agent-based systems using metrics for relevance, coherence, and safety, with hands-on debugging and optimization.
Apply best practices for ethical and secure agent deployment, including explainability, auditability, and compliance with regulations.

Why Enroll in this Course?

Be at the forefront of AI innovation by mastering the design and deployment of autonomous, agent-driven systems that reflect the future of enterprise automation.

Bridge the gap between theory and practice with hands-on labs using the most current frameworks like LangChain, AutoGen, and OpenAI Operator.

Build expertise in Retrieval-Augmented Generation and multi-agent workflows, empowering you to create scalable, intelligent solutions for real-world problems.

Learn in-demand skills for integrating agents with external APIs, cloud environments, and secure, ethical deployment practices.

Position yourself for next-generation roles in AI product development, solution architecture, and automation engineering.

Gain a credential recognized by cutting-edge organizations adopting agentic AI in business, research, and operations.

Stay ahead by engaging with industry-relevant case studies and collaborative teamwork, expanding your career network in the agentic AI field.

Course Syllabus

Overview of Agentic AI: key concepts, agent autonomy levels (reactive, deliberative, hybrid), agent architectures, and decision-making loops.
Contrast agentic AI with traditional LLM applications.
Discuss real-world use cases such as automated customer service, healthcare assistants, and autonomous bots.
Students engage in initial design sketches of simple agents considering goals, inputs, and actions.
Introduction to frameworks and toolkits for agent development: LangChain, AutoGen, OpenAI Operator.
Overview of capabilities like chaining prompts, tool integrations, memory management, and action planning.
Students explore example projects and documentation.
Deep dive into different autonomy levels and interaction models: single-agent decision loops, multi-agent collaboration, environment interaction models.
Case studies of deployed agentic systems.
Discuss agent goals, policies, and reward mechanisms briefly to frame students’ understanding.
Guide through installation and configuration of essential software and libraries: Python, Conda environments, Git/GitHub, LangChain, AutoGen, OpenAI SDKs, Jupyter/Colab notebooks, and Docker basics.
Validate setups with test scripts.
Students research a real-world use case of an AI agent (e.g., customer service bot, autonomous assistant) and prepare a one-page summary discussing functionality, architecture, and impact.
Share findings in a forum or group chat.
Study the history and evolution of agentic AI, from early symbolic agents to modern LLM-powered autonomous workflows.
Students analyze the gap between traditional AI and contemporary agentic paradigms.
Explore LangChain basics: chains, agents, memory, and tools.
Learn how to build simple chains and plug in prompts dynamically.
Hands-on: create a basic chain integrating a simple API call.
Explore the idea of multi-agent systems: role assignment, communication protocols, coordination patterns.
Overview of frameworks supporting multi-agent deployments, including AutoGen and CrewAI.
Guided development environment setup for LangChain.
Build a simple question-answering agent using OpenAI API integration.
Practice debugging and iterative improvement.
Research emerging multi-agent frameworks or orchestration techniques.
Summarize differences and potential applications.
Post reflections and engage in peer discussion.
Understanding RAG: combining generative models with external knowledge
Discuss document chunking, embeddings, vector stores (FAISS, ChromaDB), and semantic search concepts.
Hands-on building of RAG pipelines: creating embeddings, indexing, and querying
Use pre-built examples to integrate retrieval with generative responses.
Deep dive into embedding models, similarity metrics, and vector databases for agentic workflows.
Experiment with vector search and performance tuning.
Build an end-to-end RAG agent that retrieves relevant documents to augment generation.
Test on sample datasets (e.g., FAQs or knowledge bases).
Research on commercial or open-source RAG implementations in industry.
Students prepare mini presentations highlighting use cases and best practices.
Explore expert prompt engineering techniques for multi-tool invocation, role assignment, and chaining.
Understand balancing creativity and constraints to reduce hallucinations.
Learn about constraint-based prompts, few-shot learning, system prompts, and chain-of-thought prompting for complex agent decisions.
Practice writing and testing prompts.
Study orchestration strategies for handling multi-agent systems: task distribution, communication models, coordination workflows with AutoGen and other tools.
Students write reflections on challenges in building multi-agent workflows and propose solutions.
Share feedback and suggest improvements collaboratively.
Survey methods to evaluate agents for relevance, coherence, latency, and hallucination detection.
Review logging and monitoring frameworks.
Design and implement fallback mechanisms and error handling in agents to ensure graceful degradation in failure modes.
Techniques for iterating prompts and agent actions to optimize performance, reduce API calls, and improve output quality.
Practice debugging multi-agent workflows and implement safe fallbacks using simulated error conditions.
Monitor outputs for hallucinations or inconsistencies.
Research recent papers or tools focused on agent evaluation and safety.
Prepare concise reports on key ideas.
Foundation of responsible AI and ethical considerations in agentic AI including safety, bias, privacy, transparency, and fairness.
Discuss regulations like GDPR.
Techniques and system design for implementing guardrails, audit logs, explainability in agent workflows.
Discuss accountability frameworks and monitoring.
Discuss data privacy, secure APIs, compliance strategies, and threat models relevant to agentic AI deployments.
Hands-on lab implementing guardrails and audit logs in agent workflows using LangChain or AutoGen extensions.
Practice building transparent, secure agents.
Students read and discuss key ethical AI papers and current regulatory guidance.
Debate real-world dilemmas in responsible AI deployment.
Overview of deployment techniques for agentic AI: REST APIs, serverless functions, Docker containers, cloud platforms (AWS, Azure, GCP).
Hands-on workshop building and deploying containerized agent services using Docker.
Best practices for reproducibility and scaling.
Implement continuous integration and delivery pipelines customized for agentic AI projects, including versioning and testing of agent components.
Students containerize an agentic AI application and deploy it to a cloud service or local server.
Integrate basic endpoints and test functionality.
Investigate different deployment strategies.
Share pros and cons of cloud vs edge deployments for agents.
Explore tools and design patterns for monitoring deployed agents, collecting logs and metrics, and detecting anomalies or failures.
Techniques for managing updates to deployed agents, experimental feature rollouts, and graceful rollback strategies.
Automating incident detection, alerting, and response workflows with AI-enhanced tooling
Case study discussions about incident management in production AI systems.
Build a monitoring dashboard and alert system using open-source tools or cloud solutions, configured for agent services.
Practice analyzing logs and reacting to issues.
Examine industry solutions for AIOps and production AI monitoring.
Prepare summaries and discuss trade-offs
Applying Agile methodologies (SCRUM, KANBAN) for agentic AI projects.
Milestone planning, sprint workflows, and collaborative development.
Use of Trello, Jira, Notion, GitHub Projects enhanced with AI-powered automation for issue tracking and backlog management.
Best practices for AI-assisted code and workflow reviews, integration of automated code checks, and peer review workflows.
Conduct a mock sprint with teams applying agile rituals, using AI tools to automate meeting notes, retrospectives, and task assignment.
Discuss successes and challenges implementing agile with AI. Share feedback for process improvement.
Survey deployed agentic AI systems and business cases: customer support automation, autonomous research assistants, information retrieval bots.
Techniques for scaling multi-agent systems for throughput, reliability, and latency improvements.
Explore distributed computing patterns and cloud-native design.
Hands-on connecting agents to external APIs (databases, web scraping, IoT) for enriched knowledge and actions.
Build and test a multi-agent prototype integrating API calls, external data, and distributed logic.
Reflect on design choices and present scaling strategies and API integration challenges.
Advanced components in LangChain: memory types, callback handlers, custom prompt templates.
Build sophisticated agent workflows.
Explore AutoGen framework components for multi-agent orchestration, task delegation, and workflow management.
Practical sample applications.
Using OpenAI function calls for tight integration of agents with external APIs and systems.
Practice writing function schemas and handlers.
Combine LangChain, AutoGen, and OpenAI Operator components into a single functioning multi-agent system with API integrations and memory.
Iterative testing and refinement.
Present new features or libraries related to agentic AI frameworks.
Analyze impact on development productivity or capabilities.
Define project goals focusing on agentic AI solutions.
Finalize team organization, milestones, and deliverables.
Instructor-led planning session with feedback.
Conduct research on relevant technologies and prior work related to project topic.
Document findings and identify achievable scope.
Set up project environments, repositories, APIs, and tools.
Establish collaboration and version control workflows.
Begin coding the initial agent components, integrating frameworks and APIs.
Early testing and iteration.
Agile-style sprint planning and review meetings aided by AI-generated summaries and task trackers.
Continue core development with testing and debugging
Integrate feedback loops for agentic behaviour refinement.
Execute and document performance tests, fault injections, and reliability stress tests.
Refine based on results.
Prepare deployment artifacts, demo scripts, and stakeholder presentations.
Practice presentations, conflict resolution, and communication skills relevant to technical project teams.
Experiment with advanced features or alternative architectures. Present findings.
Polish code, documentation, and presentation materials.
Ensure reproducibility and deployment readiness.
Conduct rehearsal presentations, incorporate peer and instructor feedback to improve.
Resume writing, interview preparation for roles in AI and agentic development.
Best practices for reports, blogs, and open-source documentation related to agentic AI projects.
Group reflection on learning, future directions, and course feedback.
Formal capstone presentations with Q&A to panel of faculty and peers.
Assessment on technical depth, innovation, and clarity.
Review of course content, discussion on trends in agentic AI and related career opportunities.
Industry speakers share experiences and advice on agentic AI development and deployment.
Written exams, prompt engineering tests, and coding exercises assessing comprehensive understanding.
Certificates distribution, feedback collection, and guidance on advanced study and community involvement.
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Instructor Profile

Arun Kumar A R
Arun Kumar A R

AI & ML | Data Science & Analytics | AI-ML Model Building | Generative AI | Corporate Trainer

 

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Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.
RM
Rahul M.
Software Engineer
As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!
SK
Sneha K.
Final Year Student
What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.
AS
Arjun S.
Developer
The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.
PD
Priya D.
Tech Lead

Certification Details

 Free Access: Learn at no cost
Optional Certification Exam: ₹499/-
Certification Criteria: Minimum 75% score required
Certificate Type: Digital e-certificate (no hard copies)
Exam Date: To be announced after course completion

Certificate Sample