About this course
Build the skills to design, evaluate, and deploy autonomous AI agents and multi-agent systems with the NxT Certified Agentic AI & Multi-Agent Systems Architect program. This one-year course focuses on agent architectures, orchestration, enterprise retrieval-augmented generation (RAG), and intelligent decision-making workflows.
Apply your learning to practical use cases such as AI career counsellors, recruitment engines, business automation agents, and enterprise AI solutions. Explore cloud deployment, monitoring, evaluation, and responsible AI practices as part of the development lifecycle.
- Design agent workflows and coordinate interactions between multiple agents.
- Build enterprise RAG architectures and connect agents to relevant data and tools.
- Develop and evaluate AI solutions for business and workforce use cases.
- Deploy, monitor, and improve AI applications in cloud environments.
Duration: 1 year
Fees: XX,XXX
Tools you will master
Hands-on practice on the same software that companies use every day.
Google GeminiAI
GitHub CopilotAI
VS CodeAI
GitHubAI
ClaudeAI
PerplexityAI
NotebookLMAI
What you will study, module by module
- AI Basics and Everyday Uses
- Generative AI: How It Works
- Prompt Engineering Foundations
- AI for Study and Workplace Tasks
- Fact-Checking and Reliable Research
- Privacy, Safety and Responsible Use
- Integrated Practice and Review
- AI Pair Programming Setup: VS Code and GitHub Copilot
- AI-Assisted Front-End Development
- Python Coding and AI Agents in the Editor and Terminal
- GitHub Team Workflow and AI Code Review
- Calling AI APIs from Your Own App
- AI-Assisted Testing, Debugging and Documentation
- AI-Assisted Deployment and Final Project
After this course you will be able to
- Explain core concepts and design patterns for autonomous AI agents and multi-agent systems.
- Build agent workflows that use tools, APIs, context, and structured decision-making.
- Design multi-agent orchestration patterns for tasks requiring collaboration or delegation.
- Develop enterprise RAG systems, including retrieval pipelines, grounding, and evaluation.
- Create AI career counselling, recruitment, and business automation prototypes.
- Assess agent and RAG performance using suitable testing and evaluation methods.
- Apply security, privacy, reliability, and responsible AI considerations to AI workflows.
- Deploy AI applications to cloud environments and monitor their operation.
Projects you will build
AI Career Counsellor: Build an agent that uses learner information and a curated knowledge base to provide grounded career guidance.
Recruitment Intelligence Engine: Develop a workflow to assist with role matching and candidate information review, with human oversight and transparent evaluation.
Enterprise RAG Assistant: Create a retrieval-augmented assistant for organizational documents and assess the quality and grounding of its responses.
Multi-Agent Business Automation: Design a coordinated agent workflow that handles a defined business process using tools, APIs, and review checkpoints.
Cloud Deployment and Monitoring: Deploy a selected agent or RAG project and implement basic logging, performance checks, and monitoring.
Jobs you can get after this course
Who hires for these skills
Earn on your own
- Depending on their skills and portfolio, learners may offer prompt writing, AI-assisted content support or simple no-code workflow setup. Freelance work requires client communication, quality checks and responsible handling of data.
Is this course for you?
Best for
- College students and graduates curious about AI careers.
- Working professionals who want to use AI in their current work.
- Job seekers exploring digital and technology roles.
- Business owners seeking to understand AI-enabled workflows.
- Creative workers and homemakers interested in practical AI tools.
Eligibility
Suitable for learners with a foundation in programming and an interest in AI, machine learning, or software development. Familiarity with Python, APIs, and basic machine learning concepts is helpful. Applicants should be prepared to work on technical projects involving data, software, and cloud-based tools.
What next after this course
Questions students ask
The course duration is one year.
The listed course fee is XXX,XXX. Please contact the course provider to confirm the applicable currency, taxes, payment options, and any current offers.
The course focuses on autonomous AI agents, multi-agent orchestration, enterprise RAG architectures, intelligent decision-making workflows, cloud deployment, and monitoring.
A foundation in programming is recommended. Familiarity with Python, APIs, and basic machine learning concepts can help you get more from the technical projects.
Project examples include an AI career counsellor, a recruitment intelligence engine, an enterprise RAG assistant, a multi-agent business automation workflow, and a cloud deployment and monitoring exercise.
Relevant roles may include Agentic AI Engineer, AI Solutions Architect, Multi-Agent Systems Developer, Generative AI Engineer, RAG Engineer, and AI Automation Specialist. Career outcomes depend on your experience, portfolio, and the requirements of employers.