Agentic AI Training

From Prompting to Enterprise AI Automation

Mode: Physical & Online Live Classes (Day/Night)

More than 19,000+ students have started their career after getting certified from Broadway Infosys.

Updated On: 05/2026

Created On: 03/2026

Course Overview

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This course teaches how businesses use modern LLM tools such as ChatGPT from OpenAI, Gemini from Google, and Claude from Anthropic for real-world work in Telecom and Manufacturing.

Students start with LLM basics and professional prompting methods. Then they learn about AI software connections, which use APIs to create automated workflow systems. The course teaches enterprise knowledge retrieval, spreadsheet automation, system integration using n8n, and privacy and AI monitoring practices.

After completing the course, students will be knowledgeable about AI for task automation, operational improvement, and business decision-making support.

Skills you’ll learn

Large Language Models in Business (ChatGPT, Gemini, Claude) : Understand how modern AI models are used in real business environments and daily workflows.

Prompt Writing for Professional AI Use : Learn how to write clear, structured prompts to get reliable and useful results from AI tools.

AI Integration with APIs & Automation : Connect AI tools with applications using APIs and simple automation workflows.

Enterprise AI Applications : Use AI for tasks like document analysis, data processing, and workflow automation.

Our graduates are hired by 470+ companies in Nepal

Time for you to be the next hire. With our advanced and industry relevant courses, you are on the right stage to start your dream career.

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Our syllabus outlines are only the headlines of the major modules. To ensure a complete understanding of the course, we offer free counseling. Also, if you have specific modules in mind, you can customize the course. Send your inquiry today!

Lesson 1: AI Agent Essentials

Transitioning from "Rule-Based" logic to "Reasoning-Based" autonomous agents
Why GPUs (NVIDIA) are required for parallel AI processing versus standard CPUs
Small Language Models (SLMs), Agent-to-Agent (A2A) protocols, and 2026 orchestration techniques
LLM basics and alternatives, tokens, and context windows
Automation, workflows, and AI agents
Running open source LLM models locally
Intro to Agentic Runtimes like OpenClaw and NemoClaw

Learning how agents "talk" to other software using the digital waiter analogy
Mastering GET (fetch data), POST (send data), PUT (update), and DELETE
Generating and securing API Keys; understanding Bearer Tokens and headers
Reading and structuring the data format used for agent-to-tool communication
Basic Intro to Model Context Protocol(MCP)

Lesson 2: Advanced Prompt Engineering

Character: Assigning a specific persona or role
Request: Defining the core task clearly
Examples: Providing few-shot context for better accuracy
Additions: Setting constraints, guardrails, and stylistic requirements
Type: Specifying output formats (JSON, Markdown, CSV)
Extras: Adjusting tone, reading level, and specific style nuances

Chain of Thought (CoT): Forcing agents to "think step-by-step" to solve complex logic
Few-Shot vs. Zero-Shot: Deciding when to provide examples versus relying on model pre-training
Iterative Refinement: Designing feedback loops for agent self-correction

Lesson 3: Single-Agent Systems & Debugging

Trigger-Action Logic: Connecting Webhooks, interactive forms, and email triggers to LLMs
Tool Integration: Linking agents to Slack, Google Sheets, Airtable, and various CRMs
Project 1: Build a Lead Research Agent that uses API keys to fetch, qualify, and store prospect data
Analyzing "Runs" to troubleshoot logic errors and prevent infinite loops

Lesson 4: Multi-Agent Orchestration

Differentiating between Sequential (Step A to B) and Parallel (simultaneous) flows
Implementing a "Supervisor Agent" to delegate tasks to "Specialist Agents"
Build a Content Agency system involving a Researcher, a Writer, and an Editor agent

Lesson 5: Knowledge & Agentic RAG

Training agents on private data sources like PDFs, proprietary databases, and SOPs
Using Pinecone, Airtable, or built-in tool databases as a "Fact-Checking Brain"

Lesson 6: Memory & State Management

Managing short-term conversation threads versus permanent, long-term user profiles
Keeping agents efficient and cost-effective without losing vital information
Ensuring agents remember progress across multi-step, multi-day tasks

Lesson 7: Multimodal & Voice Agents

Designing agents that can listen, speak, and interact via voice
Automating image and video generation inside agent-driven workflows
Build a Voice Assistant that utilizes APIs to manage and schedule a Google Calendar

Lesson 8: Safety, Security & Ethics

Safety Guardrails: Implementing "System Overrides" to block toxic or off-topic outputs
Prompt Injection Defense: Techniques to protect agents from malicious user inputs designed to bypass rules
PII & Privacy: Best practices for handling Personally Identifiable Information and data encryption
Governance: Managing agent reliability and hallucinations in business-critical environments