Course Program Overview
Advanced Agentic AI & Multi-Agent Systems
- Duration: 4 Months
- Format: Live Online / Classroom / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour each
- Tech Stack: Python, VS Code, LangGraph (Multi-agent), AutoGPT, CrewAI, Microsoft AutoGen, OpenAI Swarm, LlamaIndex (RAG/Memory), and Multimodal LLMs (GPT-4o/Claude 3.5).
- Outcome: Autonomous Agent Portfolio + Agentic Architect Certification + Elite Placement Support.
Get a Free Demo Class
Our placement Record
Agentic AI & Multi-Agent Systems: Overview
The Agentic AI & Multi-Agent Systems program is designed for the next generation of AI engineers who want to build autonomous systems that don’t just “answer” but “act.” You will master the art of building Multi-Agent Teams where different AI agents collaborate, use external tools, and perceive the world through Multimodal inputs (Text, Vision, Audio) to complete complex business workflows.
Certification benefits include: Mastery of autonomous reasoning, expertise in multi-agent orchestration (LangGraph/CrewAI), and the ability to deploy “AI Employees” for enterprise-scale automation.
🚀 Industry Projects & Placement Roadmap
Main Project
The Autonomous "AI Company": A multi-agent team (CEO, Coder, Researcher)
Multimodal Task
Visual Web-Agent: An agent that "sees" a browser and completes a purchase
Memory Milestone
Building an Agent with Long-Term Memory using Vector DBs
Tech Stack
VS Code, LangGraph, CrewAI, AutoGen, LlamaIndex
Placement Support
Specialised Portfolio Review for Agentic Roles & 800+ Recruiters
Career Goal
Agentic AI Engineer / Multi-Agent Systems Architect / AI Researcher
Industry Readiness
Human-in-the-loop (HITL), Tool-Use, & Agentic Ethics
Project Reviews
1-on-1 Code Audits by AI Framework Contributors
Final Status
Portfolio-Ready for Elite Autonomous AI Roles
Completed
🎯 Who Should Take This Course
- Generative AI Engineers: Looking to move beyond simple "Prompt Engineering" into building self-operating agentic workflows.
- Automation Architects: Seeking to replace rigid RPA (Robotic Process Automation) with flexible, reasoning-based AI Agents.
- Product Managers & Founders: Aiming to build "AI Employees" that can handle end-to-end customer support, research, or coding.
- Data Scientists: Interested in the intersection of Vision, Voice, and Action (Multimodal Agentic Systems).
- Software Developers: Wanting to master LangGraph, CrewAI, and AutoGPT to create autonomous software agents.
🧠 Agentic AI Domain Weightage
Agentic Reasoning & Memory
Mastering Chain-of-Thought (CoT), ReAct prompting, and giving agents "Long-Term Memory" using Vector Databases and LlamaIndex.
Multi-Agent Orchestration
Designing "teams" of AI agents that communicate and collaborate using frameworks like LangGraph, CrewAI, and Microsoft AutoGen.
Multimodal Perception & Vision
Enabling agents to process images, video, and UI screenshots to navigate the web and "see" what they are doing.
Tool-Use & Function Calling
Connecting agents to the real world—teaching them to write/execute code, search the live web, and interact with APIs and SQL databases.
🏆 Benefits of Agentic AI & Multi-Agent Systems
Mastering autonomous agents places you at the absolute forefront of the "Action-Oriented AI" era, moving beyond simple content generation to complex task execution.
- Architecting Autonomy: Gain the ability to design systems that don't just chat, but use tools and execute multi-step workflows independently.
- Multi-Agent Orchestration: Master the logic behind agent collaboration, conflict resolution, and hierarchical task delegation using frameworks like LangGraph and CrewAI.
- Multimodal Advantage: Develop agents that can "see" UIs, process documents, and interact with the physical and digital world through vision and audio.
- Efficiency at Scale: Learn to build "AI Employees" that can operate 24/7, reducing operational overhead in sectors like customer support, research, and software dev.
- High-Value Tech Stack: Become proficient in the most in-demand tools of 2026, including Microsoft AutoGen, OpenAI Swarm, and LlamaIndex.
- Elite Market Positioning: As companies shift from "Chatbots" to "Agents," your specialized skills will command the highest premiums in the global AI market.
🌟 Top 5 Reasons to Choose Adian Solutions for this course
Action-First Learning
We don't stop at LLM responses. Our labs focus on Tool-Use (Function Calling). You will build agents that write to databases, browse the live web, and execute Python code to solve real-world problems.
Master the "Multi-Agent" Shift
Individual agents have limits. We teach you how to build Collaborative Agent Teams. You will learn to orchestrate a "Researcher," "Writer," and "Reviewer" to work together seamlessly using state-of-the-art orchestration logic.
Real-World Multimodal Integration
Our curriculum includes Vision-Language Models (VLM). You will build "Web-Agents" that can navigate complex browser interfaces just like a human, opening up massive possibilities for automation.
Long-Term Memory & RAG Mastery
Learn to give your agents a "brain" that lasts. We focus on persistent memory and Knowledge Graphs, ensuring your agents learn from past interactions and stay grounded in your private enterprise data.
Direct Access to AI Research Labs
With a record placement of ₹1.4 Crore and an average of 45 LPA, we connect you directly with the high-growth AI startups and global R&D labs that are currently hiring for Agentic AI roles.
Skills You Will Get
Agentic Reasoning
Agentic Reasoning
Multi-Agent Orchestration
Multi-Agent Orchestration
Tool & API Integration
Tool & API Integration
Multimodal Processing
Multimodal Processing
State & Memory Management
State & Memory Management
Human-in-the-Loop (HITL)
Human-in-the-Loop (HITL)
Course Design – Agentic AI & Multi-Agent Systems
● Month 1
Domain Coverage: Agentic Reasoning, Tool-Use & Multi-Agent Orchestration (40%)
Foundations of Autonomous Thinking & Specialized Teams
Focus:
The journey starts with moving from "Passive LLMs" to "Active Agents" using the ReAct framework. Students learn to give an LLM a "Toolbox" (Python, Search APIs) in VS Code. The focus then shifts immediately to Multi-Agent Systems (MAS). Using LangGraph and CrewAI, learners design "AI Departments," mastering core communication patterns like Hierarchical, Sequential, and Decentralized architectures.
Practical Labs:
- Building a Search & Summarize Agent that researches a topic and writes a report.
- Implementing Function Calling to let an agent query a live SQL database.
- Building an AI Content Agency: A "Researcher Agent," "Writer Agent," and "Editor Agent" working in a loop.
- Orchestrating a Software Dev Team: An agent that writes code, a second agent that tests it, and a third that fixes bugs.
Case Studies:
- Customer Support: How autonomous agents handle 80% of queries without human intervention.
● Month 2
Domain Coverage: Multimodal Perception, Web-Agents & Memory Integration (30%)
Vision-Language Models (VLM) & Persistent Knowledge
Focus:
Agents shouldn't be blind, nor should they forget. This month introduces Multimodal AI and Long-Term Memory. We use GPT-4o or Claude 3.5 Vision to build agents that navigate web UIs autonomously. Using LlamaIndex and Vector Databases, you will enable these agents to recall past interactions and access massive private knowledge bases via Semantic Search and Knowledge Graphs.
Practical Labs:
- Building a Web-Navigation Agent that can book a flight or buy a product by "seeing" the screen.
- Creating an agent that can extract structured data from complex, unreadable PDF charts and images.
- Building a Personal Executive Assistant that remembers user preferences across different sessions.
- Integrating Knowledge Graphs to help agents understand complex relationships between data points.
Case Studies:
- E-Commerce: Automating competitive price monitoring by having agents "browse" rival websites.
● Month 3
Domain Coverage: Production Deployment, Guardrails & Agent Evaluation (30%)
Refactoring & OptimizationEnterprise Tool-Kits, Production Scaling & Reliability
Focus:
This month focuses on "Actionability," security, and testing. Students learn to deploy agents into production environments while enforcing strict budget, safety, and performance guardrails. We focus on implementing Human-in-the-Loop (HITL) workflows, Cost Caps to prevent infinite loops, and formal Agent Evaluation frameworks to measure trajectory accuracy before live deployment.
Practical Labs:
- Implementing Human-in-the-Loop (HITL): Designing workflows where an agent pauses for human approval before taking a critical action.
- Agent Evaluation & Benchmarking: Using TruLens / LangSmith to trace agent reasoning, calculate hallucination metrics, and log API costs.
- Deploying an Agentic API using FastAPI, Docker, and custom Guardrails (NeMo Guardrails) to block rogue or harmful commands.
- Building a Cloud-Management Agent that safely executes AWS/Azure CLI commands within restricted permissions.
Case Studies:
- FinTech: Using "Agentic Guardrails" and rigorous evaluation to ensure AI-driven trading stays within legal compliance.
● Month 4
Domain Coverage: The Agentic Master Capstone
The Autonomous "Agent-Startup" Capstone
Focus:
The final month is a deep-dive into the Master Capstone. Students will build a fully autonomous, multimodal, multi-agent system that operates independently to solve a significant industry problem. A heavy emphasis is placed on building Self-Correcting mechanisms so agents can automatically diagnose and repair their own runtime errors.
Practical Labs:
- Developing a "Self-Correcting" agent that learns from its own execution failures by storing and parsing error logs in a memory layer.
- Capstone Project: Developing an "Autonomous Market Researcher" that browses the web, analyzes competitors, runs evaluation metrics on its own data, creates a slide deck, and emails it to a stakeholder.
- Portfolio Finalization: Documenting agent logs and "reasoning traces" to show recruiters how your agents think.
- Placement Prep: Focused interview training on Agentic Architecture patterns, cost optimization, and "Agent-Debug" scenarios.
Case Readiness:
- Mastery of the LangGraph and CrewAI ecosystem.
- Certification as an Agentic Systems Architect.
Real Roles. Real Results.
Explore Your Post Course Career
After completing the course, learners can unlock high impact roles such as:
- Agentic AI Systems Architect: Designs enterprise scale agentic workflows and ensures autonomous systems align with business goals.
- Multi Agent Orchestration Engineer: Builds and optimizes agent chains to solve complex, non linear tasks across domains.
- Generative AI Workflow Developer: Implements RAG pipelines, prompt engineering strategies, and embedding optimizations for adaptive apps.
- AI Governance & Safety Specialist: Establishes guardrails, compliance checks, and human in the loop frameworks for secure deployments.
- Cognitive Automation Consultant : Advises enterprises on ROI, replacing manual workflows with agentic automation solutions.
- Enterprise AI Product Lead: Bridges technical teams and executives, leading the lifecycle of agentic AI products.
- Autonomous Operations Strategist: Designs “Zero Touch” operations where agents manage customer service, logistics, and compliance.
- AI Observability Analyst: Monitors agent telemetry, token usage, and drift metrics to ensure efficiency and reliability.
Salary Benchmark
Agentic AI & Multi-Agent Systems
- India: Entry-level specialists in Agentic AI start at ₹20–28 LPA. Senior Architects in this niche are currently among the highest-paid in tech, ranging from ₹50–85 LPA.
- United States: The median salary for Agentic AI Engineers is $175,000/year, with top-tier roles in AI Research Labs exceeding $250,000/year.
- Global Outlook: Due to the "Agentic Revolution," remote roles for skilled Multi-Agent architects are seeing unprecedented demand, with total compensation packages reaching up to $425k.
Would you like me to continue this series for all major AWS certifications (SysOps, DevOps Pro, Solutions Architect Pro, Security Specialty, ML Specialty) so you have a complete brochure set
Frequently Asked Questions
Agentic AI & Multi-Agent Systems
Standard GenAI focuses on prompting for text/images. Agentic AI focuses on Action. In this course, you learn to build systems that use tools and work in teams to complete complex tasks autonomously.
You will gain deep expertise in LangGraph, CrewAI, Microsoft AutoGen, and LlamaIndex. These are the industry standards for building stateful, multi-agent systems.
While some local testing is possible, we primarily focus on using professional API-based models (like GPT-4o and Claude 3.5) and cloud simulators, ensuring you can build and test agents on a standard laptop.
It means the agent can "see." For example, a multimodal agent can take a screenshot of a website, understand where the "Checkout" button is, and click it to complete a task.
The demand is massive and worldwide. Silicon Valley startups, European research labs, and global tech giants are aggressively hiring for Agentic AI Architects. Because these roles are highly specialized, they often offer remote-first opportunities with international pay scales ranging from $150k to $425k. Adian Solutions provides global resume scrubbing and connections to our network of 800+ international recruiters to ensure you are ready for the world stage.
Our Clients
- Client Testimonials
What Our Clients Say
Blogs and Insights
Hello world 3
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
Hello world 2
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
Hello world!
Welcome to WordPress. This is your first post. Edit or delete it, then start writing!
Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators