From APIs to Production-Ready AI Systems
Learn how to design, build, orchestrate and deploy Agentic AI systems through a hands-on, industry-aligned program.
Move beyond basic Generative AI and learn how modern AI applications combine LLMs, RAG, tools, agents and multi-agent orchestration to solve real-world problems.
36 Hours | Classroom Training | Hands-on Labs | Capstone Project | Mentoring
Build AI Systems That Go Beyond Prompts
Generative AI is rapidly evolving from standalone chatbots and copilots to AI agents capable of reasoning, using tools, accessing enterprise data and working together to complete complex tasks.
The Architecting Agentic AI Systems program is designed to help programmers and technology professionals understand this transition and develop the practical skills required to build modern Agentic AI applications.
You will progress from LLM fundamentals and AI-assisted development to RAG, single agents, multi-agent orchestration, evaluation, observability, security and production deployment.
By the end of the program, you will have designed and built a production-ready multi-agent AI system as your capstone project.
What You Will Learn
Build with Generative AI
Understand LLMs, tokens, context windows, embeddings, prompt engineering, function calling and structured outputs.
Develop RAG Applications
Learn how Retrieval-Augmented Generation works and explore semantic search, hybrid search, reranking, context compression and Graph RAG.
Build AI Agents
Create agents that combine LLM reasoning with tools, APIs and external enterprise systems.
Orchestrate Multiple Agents
Design multi-agent workflows and understand orchestration patterns using frameworks such as LangGraph and other leading Agentic AI frameworks.
Evaluate AI Systems
Learn practical approaches to AI evaluation, hallucination detection, LLM-as-a-judge and human-in-the-loop validation.
Deploy to Production
Understand containerization, Docker, Kubernetes, cloud platforms, databases, authentication, authorization and production deployment.
The curriculum also covers monitoring and observability using technologies such as OpenTelemetry, Prometheus and Grafana, alongside AI-focused platforms including LangSmith, LangFuse and Phoenix.
Course Highlights
36 Hours of Applied Learning
A structured journey from AI and LLM fundamentals to production-ready Agentic AI systems.
Hands-on Labs & Assignments
Apply concepts through practical labs, assignments and mini-projects instead of learning AI purely through theory.
AI-Assisted Development
Learn how to use AI coding assistants to design, generate, refactor and test software while understanding where AI-generated code can fail.
Industry-Relevant Technology Stack
Work with APIs, RAG, vector databases, MCP, Agentic AI frameworks, evaluation tools, observability platforms, Docker and Kubernetes.
Personalized Mentoring
Get mentoring support to review your capstone architecture, implementation approach and development decisions.
Production-Ready Capstone
Design, build and demonstrate a multi-agent AI system incorporating monitoring and human-in-the-loop escalation.
Course Curriculum Modules
Foundations of Agentic AI
Understand the evolution from traditional AI to Generative AI and Agentic AI.
Topics Covered
- Evolution from non-generative AI to Agentic AI
- LLM fundamentals
- Transformers, tokens and context windows
- Embeddings
- Prompt engineering
- Function calling
- Structured outputs
- LLM providers, runtimes and model hubs
AI-Assisted Development
Learn how AI coding assistants can become part of the software development workflow.
Topics Covered
- Using AI assistants to design software
- AI-assisted code generation
- Code testing using AI
- Understanding the strengths and limitations of coding assistants
Retrieval-Augmented Generation (RAG)
Learn how AI systems can retrieve and use enterprise or domain-specific knowledge.
Topics Covered
- Why RAG?
- RAG architecture
- Embeddings and vector databases
- Semantic search
- Hybrid search
- BM25
- Context compression
- Reranking
- Graph RAG
Introduction to Agentic AI
Understand how autonomous AI applications interact with tools and external systems.
Topics Covered
- Why Agentic AI?
- Tools, agents and orchestrators
- Model Context Protocol (MCP)
- Designing tool APIs
- Connecting agents to external systems
Building AI Agents
Move from AI workflows to independently functioning agents.
Topics Covered
- Single-agent design
- Tool integration
- Model Context Protocol (MCP)
- Designing agent-driven workflows
Multi-Agent Orchestration
Learn how multiple specialized AI agents can collaborate within a coordinated system.
Topics Covered
- Multi-agent systems
- Orchestration patterns
- Building multi-agent systems using LangGraph
- Langflow as a visual orchestration framework
- Overview of leading Agentic AI frameworks:
- LangGraph
- Google ADK
- CrewAI
- AutoGen
- OpenAI Agents SDK
The framework coverage and orchestration technologies above are explicitly included in the course curriculum.
AI Evaluation
Learn how to determine whether an AI system is actually performing reliably.
Topics Covered
- AI evaluation parameters
- RAGAS
- DeepEval
- Promptfoo
- Hallucination detection
- LLM-as-a-judge
- Human-in-the-loop evaluation
Monitoring & Observability
Understand how production AI applications are monitored, analyzed and improved.
Topics Covered
- AI monitoring and observability
- OpenTelemetry
- Prometheus
- Grafana
- LangSmith
- LangFuse
- Phoenix
- Weights & Biases
Security & Governance
Understand key security and governance considerations when deploying AI applications.
Topics Covered
- Prompt injection
- Jailbreak attacks
- PII masking
- Secret management
- Responsible AI
Production Deployment
Take Agentic AI applications beyond development environments and understand what is required to run them in production.
Topics Covered
- Software engineering practices
- Source control and project structure
- Containerization
- Docker
- Kubernetes
- Orchestration and scaling
- Cloud platforms
- Databases
- Authentication and authorization
Working Effectively with AI Systems
Learn practical techniques for improving the performance and efficiency of AI applications.
Topics Covered
- Prompt engineering vs. fine-tuning
- Token optimization
- Memory systems
Modules 9 through 11 extend the program from AI development into security, responsible AI and production engineering.
Build a Production-Ready Agentic AI System
Capstone Project
Learning Agentic AI becomes meaningful when you actually build a working system.
As part of the program, you will receive specifications for a production-ready multi-agent AI system and independently build the solution using concepts learned throughout the course.
Your capstone journey includes:
- Production-ready multi-agent system specifications
- Independent architecture and development
- One-on-one mentoring to review your approach
- Application of agents, orchestration and learned modules
- Final working demonstration
- Project presentation
This gives you a practical project that demonstrates your ability to take an Agentic AI solution from architecture through implementation.
Skills You Will Build
By completing the program, you will develop practical skills across the Agentic AI development lifecycle:
Agentic AI Architecture
Design AI applications incorporating LLMs, agents, tools and orchestrators.
RAG & Enterprise Knowledge
Build AI workflows capable of retrieving and reasoning over external knowledge.
Agent Development
Develop agents combining LLM reasoning with external tools and systems.
Multi-Agent Orchestration
Coordinate multiple AI agents using modern orchestration patterns and frameworks.
AI Evaluation
Evaluate outputs, identify hallucinations and incorporate human oversight.
AI Observability
Monitor and evaluate Agentic AI applications operating in production environments.
AI Security & Governance
Understand prompt injection, jailbreak attacks, PII protection, secrets management and responsible AI.
Production Deployment
Containerize and deploy AI applications using technologies such as Docker and Kubernetes.
AI-Assisted Software Development
Use AI coding assistants effectively while critically evaluating generated code for errors, edge cases and weaknesses.
Who Should Enrol?
This program is designed for graduates and technology professionals who want to build practical skills in Generative AI and Agentic AI development.
It is particularly relevant for:
- Software Developers
- Software Engineers
- AI/ML Engineers
- Developers exploring Generative AI
- Technology professionals working with AI applications
- Solution Architects focusing on AI integration
- Professionals looking to build Agentic AI capabilities
Eligibility
Graduate with familiarity with at least one programming language. Python familiarity is preferred.
Career Opportunities
The skills developed through this program can support career paths including:
AI/ML Engineer
Build and integrate intelligent applications, models and AI-driven workflows.
Software Engineer - AI Applications
Extend traditional software development skills into LLM and Agentic AI applications.
Solution Architect - AI Integration
Design architectures that integrate AI capabilities with enterprise applications and systems.
Developer Productivity Specialist
Help engineering teams adopt AI-assisted coding and development workflows effectively.
These are the target roles identified for the program, with industry applicability across BFSI, Healthcare, Telecom and IT Services.
Learn. Build. Deploy.
Go Beyond Learning AI. Build With It.
At TechElevate, the focus is not simply on understanding what Agentic AI is.
The program is designed around learning by building, combining instructor-led classroom training, hands-on labs, assignments, real-world technology frameworks, mentoring and an independent capstone project.
You progress through the complete journey:
Meet Your Instructor
Arti Bendre
Associate Director | Head of Product Development
A technology professional with 20+ years of experience in software engineering and product development. Her recent work and learning have focused on Generative AI, Agentic AI,RAG and emerging technologies like MCP. As a facilitator she brings a practioner's perspective, focusing on understanding concepts, experimenting hands-on, and translating emerging AI technologies into practical applications.
Mentoring & Career Support
Build Skills With Guidance Along the Way
TechElevate combines technical learning with personalized support to help learners translate their skills into career opportunities.
Personalized Mentoring
Receive guidance from experienced professionals while developing your technical and project capabilities.
Capstone Guidance
Get one-on-one mentoring to review your architecture and implementation approach.
Resume & Profile Development
Present your Agentic AI skills, technology exposure and capstone project effectively to potential employers.
Interview Preparation
Prepare to explain Agentic AI concepts, architecture decisions and your hands-on project experience during technical interviews.
Placement Assistance
Get support in identifying relevant opportunities and connecting your newly developed skills with suitable technology roles.
Why Choose TechElevate?
A practical learning environment designed to help you build cybersecurity skills that matter.
Personalized Experience
We keep our batches small, focus on practical learning and personalized attention
Hands-On
Training
Lab-based learning with real-world tools like Kali Linux, Wireshark, and Burp Suite
Guidance from Experienced Faculty
Industry-recognized curriculum & certification alignment
Career Essentials Included
Resume templates, LinkedIn profile makeovers, and interview preperation
Built Around Industry Needs
Industry-recognized curriculum & certification alignment
Upcoming Batches
Batches start every month with small class sizes (10–15 learners).
Flexible timing: Weekday and weekend options available.
Location: Nigdi Pradhikaran, Pune
Architecting Agentic AI Systems