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Agentic AI Developer Training
Nevolearn Certified Agentic AI Developer Program
Master Agentic AI by building real multi-agent systems with Supervisor, NLP, DB, Reporting, and Email agents. Learn workflow automation, MCP integration, testing, and deployment through structured, hands-on training designed for future-ready AI engineers.
Agentic AI Developer Course Overview
Key Features
Who All Can Attend This Agentic AI Developer Course?
• Freshers and engineering graduates • Computer Science / IT students • BCA / B.Sc / MCA learners • Software Developers and Application Engineers • QA, Support, and Operations professionals • Professionals shifting into AI automation • Beginners interested in AI workflow engineeringPrerequisites To Take Agentic AI Developer Training for Software Engineers
- Upskill or reskill your teams
- Immersive Learning Experiences
- Private cohorts available
- Advanced Learner Analytics
- Skills assessment & benchmarking
- Platform integration capabilities
- Dedicated Success Managers
- Upskill or reskill your teams
- Immersive Learning Experiences
- Private cohorts available
- Advanced Learner Analytics
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Next Cohort starts in 2 days
This Agentic AI training empowers professionals to build structured, reliable AI systems capable of task delegation, data retrieval, reporting automation, and workflow execution. Learners move beyond prompt-based AI experimentation and gain real-world experience in building AI systems that act, decide, and integrate with tools.
For individuals, this certification enhances employability in AI automation, LLM application development, and workflow engineering roles. It demonstrates the ability to build intelligent multi-agent systems, a skill increasingly required in modern AI-powered products.
For organizations, trained professionals can build internal AI automation pipelines, intelligent workflow systems, automated reporting tools, and scalable agent-based architectures that reduce manual work and improve operational efficiency.
High Demand for Agentic AI Developer Training for Software Engineers
Soaring Demand and Accelerated Growth
Skills Focused
- What is Agentic AI vs chatbot AI
- Where agents are used in software engineering
- What you’ll build in this course (multi-agent pipeline)
- Lab: Run and observe a sample agent workflow
- Tokens, context, prompts
- Why LLMs fail (hallucinations, ambiguity)
- How agents reduce errors with tools and steps
- Lab: Structured prompts and task checklist
- Role prompts and constraints
- Task decomposition and chaining
- Consistency with templates
- Lab: Build agent-style prompt template
- Agent components: planner, tools, memory, output
- Stateless vs stateful agents
- Observability basics
- Lab: Design and simulate agent loop
- Goal-based planning
- ReAct-style thinking (simplified)
- Guardrails and stop conditions
- Lab: Build planning agent with fallback
- Tool types (APIs, search, DB, files)
- Tool selection logic
- Safe tool usage rules
- Lab: Configure and test tool-using agent
- Short-term and long-term memory
- Context window limits
- Safe memory storage principles
- Lab: Add memory behavior to agent
- Sequential and event-driven workflows
- Retry, timeout, and error handling
- Lab: Build 3-step workflow agent
- Supervisor → NLP → DB → Reporting → Email
- Agent responsibilities and handoff contracts
- Lab: Create multi-agent blueprint
- Delegation and routing
- Intent-based task assignment
- Logging and traceability
- Lab: Build Supervisor Agent
- Intent classification
- Entity extraction
- Natural language to structured JSON
- Lab: Build NLP extraction agent
- Safe data retrieval and validation
- Avoid unsafe queries
- Clean output formatting
- Lab: Build DB Agent with dataset
- Report templates
- Business summaries and insights
- Report validation checklist
- Lab: Generate report from DB output
- Email automation basics
- Attachments and formatting
- Alert rules
- Lab: Send report via email
- MCP tools, resources, prompts
- Standardized integrations
- Mapping MCP to agents
- Lab: Explore MCP server concept
- DB Agent via MCP
- Tool discovery via MCP client
- Security and safe parameters
- Lab: Integrate MCP tool access
- Supervisor → NLP → DB(MCP) → Report → Email
- Workflow reliability
- Observability and checkpoints
- Lab: Execute full pipeline
- Common failures in multi-agent systems
- Test cases and validation
- Optimization basics
- Lab: Run controlled failure scenarios
- Architecture diagram
- Data contracts
- Deployment checklist
- Lab: Finalize system design
- Demo storytelling
- Operational readiness
- Portfolio presentation
- Lab: Final system demo
Career Path
Certification Process
Connect With Reps
Frequently Asked Questions
Yes. The course starts with AI and agent fundamentals explained in simple terms before moving into structured multi-agent implementations.
No advanced programming is required. The course focuses on structured workflows and guided labs.
Yes. You will build a complete Supervisor → NLP → DB → Reporting → Email automation pipeline.
Model Context Protocol (MCP) standardizes how AI agents connect to tools, data, and reusable prompts, improving scalability and integration consistency.
Yes. Dedicated modules focus on failure handling, routing errors, optimization, and evaluation.
You will receive certification, a capstone project, architecture documentation, and a portfolio-ready multi-agent AI system.
Generative AI focuses on producing content such as text, code, or images based on prompts. Agentic AI goes beyond generation — it plans tasks, uses tools, retrieves data, makes decisions, and executes workflows autonomously.
Yes. The course teaches you how to design, architect, and implement multi-agent systems from planning logic to tool integrations and workflow orchestration.
Yes. The training is structured around enterprise-style workflows such as automated report generation, database querying, structured email notifications, and tool-integrated automation pipelines.
Yes. The course covers structured output enforcement, tool validation, guardrails, fallback strategies, and safe retrieval patterns to reduce hallucinations and improve reliability.
Move beyond prompt engineering and master real-world multi-agent AI systems. Build intelligent agents that plan, execute, retrieve data, generate reports, and automate workflows using MCP-integrated architectures. Enroll now and future-proof your AI career.
Design and implement a complete enterprise-style multi-agent pipeline:
Supervisor → NLP → Database (MCP) → Reporting → Email Notification
Gain hands-on experience in planning logic, tool usage, structured outputs, workflow orchestration, and automated reporting.
