AI AGENT DEVELOPMENT

Get a clear view of what this course delivers. Meet your instructor, explore the real-world impact of agentic AI, and lock in your capstone direction so you can start building with intent from day one.

• Instructor story
• Real-world AI agent applications 
• Industry landscape
• AI job market & career pathways
• Course structure, expectations & milestones
• Capstone briefing & success criteria

Build your first working agent and break down the core pieces that make it run. You’ll deconstruct the planner, tools, memory, and outputs—and see exactly where you fit in the agent dev lifecycle.

• Build your first agent
• Core agent components
• Your role in the agent development lifecycle
• Demo: Deconstruct a simple agent and analyze interactions step-by-step

Learn how pros identify agent use cases worth actually building. You’ll evaluate value, impact, and ROI—then workshop, rank, and select high-impact opportunities.

• High-value use cases 
• Workshop: Brainstorm and prioritize agent opportunities
• Estimating ROI and business impact

Assignment #1: Mini-Agent Initiative
Identify one high-impact agent opportunity and outline a plan for development.

Learn how to assess your data like an engineer who’s deploying for real production systems. You’ll run a readiness check, surface risks, and walk through dataset prep for future RAG pipelines.

• Data readiness checklist & risk assessment
• Detecting bias & ensuring data quality
• Demo: Dataset ingestion, cleaning, and validation for future RAG systems

Assignment #2: Outline a Data Strategy for Your Agent Project
Describe the data flow, validation, and risk mitigation plan for your agent.

Design agent architectures that scale without breaking. You’ll map roles, flows, orchestrator patterns, and trade-offs—then build your first architecture diagram with real tooling.

• Modular agent blueprints
• Agent roles, flows, & orchestrator patterns
• Architectural trade-offs 
• Demo: Bootstrap LangChain/AutoGen environment

Level up from single agents to collaborative ones. You’ll explore delegation patterns, communication protocols, and implement a simple multi-agent workflow.

• Collaboration patterns
• Agent communication protocols & delegation logic
• Demo: Implement simple inter-agent delegation

Assignment #3: Inter-Agent Interaction Plan

Upgrade your agents’ intelligence layer. You’ll refine prompts, build a working RAG system, and design memory structures that let agents learn, reference, and reason more effectively.

• Advanced prompt engineering and grounding
• Demo: Retrieval-Augmented Generation (RAG) system implementation
• Agent memory structures 

Assignment #4: Agent Memory Design

Push your agents beyond text. Learn how to add vision, audio, and other modalities, and build a working multimodal prototype with managed context and memory.

• Demo: Adding vision and speech I/O capabilities
• Multimodal tools & libraries
• Prototype development 

Build agents that improve with every interaction. You’ll implement iterative learning loops, supervised retraining, and A/B testing workflows used in production-grade systems.

• Reinforcement-style refinements & continuous learning loops
• Demo: Simple RL workflow and supervised retraining pipeline
• A/B testing 

Tackle the real challenges of deploying agents responsibly. Explore bias, explainability, HITL workflows, and real ethical dilemmas faced in production environments.

• Ethics & bias 
• Explainable AI & Human-in-the-Loop patterns
• Case Study: Ethical dilemmas in agent decision-making

Learn how to defend agents against real attack vectors—prompt injection, adversarial inputs, and data exposure—while designing privacy-safe pipelines and access controls.

• Threat models
• Data anonymization & privacy-preservation
• Access control, authentication, & policy management
• Case Study: Common vulnerabilities in agent systems

Take your agent to production with containerisation, orchestration, CI/CD, and monitoring practices used by modern AI teams. Build your deployment pipeline end-to-end.

• Containerization & deployment
• Workshop: CI/CD pipeline design for AI agents
• Task scheduling, error recovery, & monitoring

Track what matters: reliability, accuracy, and real business impact. Build monitoring dashboards, feedback loops, and retraining pipelines that keep agents improving post-launch.

• Key KPIs
• Demo: Build monitoring dashboards and feedback loops
• Automating retraining and versioning

Present your final build, get expert feedback, and explore what’s next—from agent swarms to embodied intelligence and large-scale enterprise systems. 

• Final project presentations
• Trends

Build your next move. Learn how to position yourself in the agent ecosystem, grow your portfolio, and map a career path in one of tech’s fastest-moving fields.

• Career pathways
• Portfolio & networking 
• Building your professional roadmap