FA-0317Agentic & Generative AI

Mastering Prompts & Agents

Design and deploy powerful AI workflows: prompt engineering, tool integration (MCP), and the age of autonomous agents - 2 days

Introduction

Why this course

AI that just spits out text? That's yesterday's news. Today's AI thinks, plans, and executes—connecting systems, making decisions, and handling complex workflows without human intervention.

Over two intensive days, you'll master the art of getting AI to do exactly what you want. We'll push ChatGPT, Gemini, Perplexity, and Claude to their limits, figure out when free tools beat premium ones, and build intelligent agents that actually solve real problems using MCP frameworks.

This isn't a classroom exercise. Drawing from three decades of solving actual business challenges, every technique you'll learn has been battle-tested in production environments. You'll walk away with skills that work—not just concepts that sound impressive.

Ready to build AI that gets things done? Let's make it happen.

Learning outcomes

By the end, participants will be able to:

Understand AI types and their evolution through ML, DL, LLMs, and agents
Craft effective prompts and compare their efficacy across tools and pricing tiers
Integrate models with external services via MCP
Design, deploy, and troubleshoot agentic AI workflows using Manus, ChatGPT Deep Research, and Operator
Evaluate trade‑offs between autonomy, cost, security, and compliance

Prerequisites

Basic understanding of AI/ML and neural networks
Comfort with prompt‑based interactions
Laptop with internet access and ability to install plugins or APIs

Training outline

8 modules

011. Foundations of AI & Learning Engines4 topics

Types of AI: Reactive systems, limited-memory, theory-of-mind, self-aware; narrow vs general AI
Machine Learning: Supervised, unsupervised, reinforcement learning
Deep Learning Basics: Neural networks, architectures (CNN, RNN, Transformers), backpropagation
From ML to LLMs and Agents: How DL powers language models and forms the basis for agentic behavior

022. Introduction to LLMs & Prompt Engineering3 topics

Core Concepts: Transformer architecture, token prediction, context windows
Prompt Design Variants: Zero-shot, few-shot, chain-of-thought, structured prompting
Live Demos & Tool Comparisons
- Tools: ChatGPT, Gemini, Perplexity, Clause
- Metrics: response quality, latency, context handling
- Free vs Paid tiers: comparing Plus/Pro features (e.g., token limits, model versions, compute time)

033. Model Context Protocol (MCP) Deep Dive4 topics

Overview: Standard JSON‑RPC interface for tool integration
Technical Architecture: Clients, servers, SDKs (Python, TypeScript, Java)
Use Cases: IDE integration, database access, business tools (Slack, GitHub, Postgres, Puppeteer)
Security & Risks: Prompt injection, RCE, unauthorized data access (CVE‑2025‑49596)

044. Agentic AI: Concepts, Frameworks, & Ethics3 topics

Definition: Autonomous systems using ML/DL/RL to act without human intervention
Core Traits: Planning, tool orchestration, monitoring, auditing
Ethical Landscape: Responsibility, governance, misuse, alignment

055. Comparative Agentic AI Platforms1 topics

Manus

Launched March 6, 2025; uses Claude (Anthropic) + Qwen, sandboxed cloud environment
Multi-agent architecture: planning, execution, validation; real‑time audit trail
Strengths: autonomous execution across tasks; Weaknesses: stability, data privacy, access cost

ChatGPT Deep Research

Released Feb 3, 2025; autonomous multi‑step browsing, citation‑aware reporting
Strengths: depth, citations, PDF/spreadsheet parsing; Limitations: hallucinations, TL computing cost, rate limits

OpenAI Operator

Research preview browser-based agent; interacts with real web UIs
Strengths: form-filling, navigation; Weaknesses: shallow outputs, task fragility

066. Hands-On Labs & Comparisons4 topics

Workshop: Prompt engineering with ChatGPT, Gemini, Perplexity, Clause; compare outputs, context sensitivity, cost tiers
Integrate MCP: Connect ChatGPT or Gemini with external tools (e.g., KB, spreadsheets) using MCP server
Agent Design & Execution:
- Task 1: Manus – automation across research and deployment
- Task 2: Deep Research flow – autonomous data collection and summary
- Task 3: Operator – guided browsing and booking via live UI
Performance Analysis: Compare success rates, autonomy level, cost, speed across agents

077. Ethics, Governance & Risk Mitigation3 topics

MCP Vulnerabilities: Secure design, hardened SDKs, least privilege model
Operational Guardrails: Human-in-the-loop checkpoints, audit logs, traceability
Responsible Agent Use: Privacy compliance, bias monitoring, transparency

088. Capstone Group Challenge1 topics

Design a mini end-to-end agentic workflow

A programme built around your team.

Share your training goals and requirements.