Define AGENTIC AI and differentiate it from traditional AI and automation.

Explain the enterprise, societal, and ethical implications of autonomous systems.

Identify emerging use cases and trends in agentic AI for IT, automation, and business intelligence.

Evolution of AI: From rule-based to generative to agentic systems

Core principles: autonomy, reasoning, perception, adaptability

Agent lifecycle: sensing, thinking, acting, reflecting

Industry trends: AI copilots, self-improving workflows, AI orchestration

Apply Python fundamentals and libraries for agent development.

Write reproducible, modular code for AI experimentation.

Debug and document agent behaviors effectively.

Python refresher: functions, classes, packages, virtual environments

Libraries for AI: requests, langchain, openai, pandas, asyncio

Working with APIs and environment variables

Best practices: reproducibility, version control, code comments

Compare symbolic, LLM-based, BDI, and hybrid agent architectures.

Describe architectural trade-offs and design choices for IT use cases.

Design simple agent blueprints for task-specific environments.

Symbolic AI vs. Neural AI

Cognitive, BDI (Belief–Desire–Intention), and Hybrid Architectures

LLM-powered architectures with reasoning layers

Architectural design templates for enterprise systems

Implement reasoning and planning modules in agents.

Develop context memory for improved decision-making.

Integrate short-term and long-term memory structures.

Chain-of-thought reasoning, reflection, and replanning

Context storage with vector databases

Episodic vs. semantic memory in LLMs

Retrieval-Augmented Generation (RAG) and memory persistence

Describe communication and coordination among multiple agents.

Build collaborative agents to complete distributed tasks.

Evaluate coordination and conflict resolution strategies.

Agent communication protocols (ACP) and message passing

Task division and coordination strategies

Multi-agent orchestration using CrewAI and AutoGen

Real-world case studies: collaborative code review, workflow automation

Design human-in-the-loop systems for hybrid intelligence.

Ensure transparency and explainability in agent outputs.

Integrate feedback mechanisms for oversight and improvement.

Human–AI co-creation frameworks

Designing explainable UIs and dashboards

Trust, accountability, and human override design patterns

Explain reinforcement learning (RL) concepts: states, actions, and rewards.

Create simulated environments for agent learning.

Apply RL policies for goal-oriented decision-making.

RL fundamentals and policy gradient methods

OpenAI Gym environments and agent control loops

Applying RL for adaptive decision-making in automation

Integrate LLMs into agent workflows.

Design prompt strategies for contextual and autonomous behaviors.

Critically evaluate LLM responses for bias, coherence, and reliability.

Prompt chaining, role prompting, and context injection

Function calling and structured output parsing

RAG (Retrieval-Augmented Generation) for dynamic context loading

Build, orchestrate, and monitor agent workflows using modern frameworks.

Evaluate framework capabilities for enterprise-scale use.

Complete guided labs across different frameworks.

LangChain: Chains, Tools, and Agents

CrewAI: Multi-agent orchestration

AutoGen: Collaborative agent conversations

LangGraph: Visual agent pipelines

Deploy, scale, and monitor agentic systems in real environments.

Implement API-based integration with enterprise software.

Troubleshoot performance, latency, and cost issues.

Cloud & container deployment (AWS, Azure, Docker)

CI/CD pipelines for agent updates

Logging, monitoring, and alert systems

Security, access control, and reliability

Recognize safety, risk, and bias challenges in AI systems.

Apply governance models for responsible AI deployment.

Evaluate ethical AI failures and industry case studies.

AI ethics principles (transparency, fairness, accountability)

Governance frameworks (EU AI Act, NIST, ISO 42001)

Responsible AI lifecycle: design, deployment, oversight

Build a fully functional end-to-end agentic solution.

Apply learned frameworks and design principles.

Present, document, and defend your project to mentors.

End-to-end agentic solution development

Testing, documentation, and evaluation metrics

Peer review and mentor feedback