instructor led courses 

🎯 Introduction to AgentS​

2 Lectures + Practical Exercises

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This two-session, instructor-led course introduces professionals to AI agents and Agentic AI, with a practical focus on designing agents that can reason, use knowledge and tools, and perform multi-step business tasks.

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📌 What You'll Learn:

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✔ AI Agent Fundamentals – Understand how agents differ from traditional

    Generative AI and chatbots.
✔ Agent Design Building Blocks – Define roles, goals, instructions, skills,

   success criteria, metrics, memory, and guardrails.
✔ Knowledge & Tools – Learn how agents use RAG, APIs, web search, databases, and external tools.
✔ Hands-On Agent Design – Build and test business-focused agents using the AAII Agent Sandbox.

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🗓️ Schedule: Tuesday and Thursday ⏰ Time: 3:30 – 5:00 PM

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📍 Virtual via Zoom

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👥 Ideal For: Business leaders, IT/data professionals, AI practitioners, and analysts interested in applying Agentic AI.

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Build a practical foundation for moving from Generative AI to Agentic AI and applying intelligent agents to real-world business problems. Upon successful completion, students earn Foundation level certificate and are eligible to join the Build Agentic Solution Course.​​

 

🚀 Build & Deploy Agentic AI Solutions
Agentic AI + Forward Deployed Engineering (FDE)

4 Lectures + 4 Homework Support Sessions +  Certificate

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This four-week, hands-on course teaches our four-phase Knowledge Engineering framework — Acquisition, Consolidation, Agentic Consumption, and Learning & Governance — using our zero-code Agentic Sandbox and code-first approaches. Learn to act as a Forward Deployed Engineer (FDE) and take a real-world Agentic AI use case through the lifecycle:

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Discover → Design → Build → Integrate → Deploy → Adopt → Improve

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🛠️ Key Topics:

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✔ L1 – Knowledge Acquisition: Agent Foundations & Customer Discovery
Define agent roles, skills, tools, memory, success criteria, and performance metrics. Practice automated extraction from documents and images. Learn the FDE role and translate customer needs into Agentic AI use cases.

✔ L2 – Knowledge Consolidation: TAG, RAG & Enterprise Integration
Build taxonomies and RAG retrieval, resolve entities and conflicting facts, and apply confidence thresholds and business rules. Discover customer data, systems, APIs, and integration requirements.

✔ L3 – Agentic Consumption: Multi-Agent Orchestration & Deployment.  Configure Web Search, GraphRAG, MCP, A2A, and multi-agent workflows grounded in enterprise knowledge. Move from prototype → integration → deployment.

✔ L4 – Learning & Governance: Production Readiness & Adoption
Build guardrails, human-in-the-loop review, auditability, security controls, and performance monitoring. Address production readiness, user adoption, field feedback, and continuous improvement.

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🔹 Hands-On Learning: Build real-world Agentic AI applications using low-code and code-first approaches.

🎓 Final Project: Act as an FDE and take a customer use case from discovery through architecture, prototype, evaluation, and deployment planning.

🏆 Earn an Intermediate-Level Agentic AI Certification upon successful completion.

 

🚀 Full Stack AI Certification 

8 Lectures + 8 Homework Support Sessions + Full Stack AI Certificate

Predictive AI and Generative AI are increasingly used together, yet many teams

struggle to build reliable, end-to-end AI solutions. This course bridges that gap

by teaching how to design and develop full-stack AI systems that integrate

predictive models, large language models (LLMs), and knowledge-driven

frameworks. It emphasizes hands-on learning with real-world applications

and enterprise use cases. Participants will progress from foundational

concepts to advanced architectures, culminating in a capstone project.

The program focuses on building scalable, reliable, and production-ready AI solutions.
 

🛠️ Key Topics:

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• AI foundations and problem framing for business applications

• Working with structured and unstructured data

• Building and evaluating predictive AI models

• Generative AI and prompt engineering techniques

• Knowledge systems using RAG (Retrieval-Augmented Generation)

• Taxonomy-driven AI using TAG (Taxonomy-Augmented Generation)

• Designing agentic and multi-agent AI systems using Agno and LangGraph

• End-to-end system integration and deployment

• Testing, evaluation, and governance of AI systems

• Capstone project for real-world implementation

🔹 Hands-On Learning – Work through your own real-world business applications

🎓 Earn Full Stack Certification upon successful completion of all assignments and the final Capstone project

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