Agentic AI
Agentic AI focuses on building intelligent systems that can plan, decide, and act autonomously to achieve goals. This category covers the concepts, design patterns, and real-world practices behind AI agents — from single-task agents to multi-step, tool-using, and self-improving systems.
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Course content
30 lessons / 1 moduleNot completed
Day 3 – Core Components Of An AI Agent
4 min
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Day 4 – What Makes An Agent “autonomous”?
4 min
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Day 5 – Agentic AI Vs Traditional Automation (rpa)
4 min
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Day 6 – Real-world Agentic AI Use Cases (2026 Snapshot)
4 min
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Day 7 – Popular Agent Frameworks (lang Graph, Crew AI, Auto Gen)
4 min
Not completed
Day 11 – Multi-step Reasoning & Task Decomposition
3 min
Not completed
Day 12 – Reflection & Self-correction In Agents
3 min
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Day 13 – Single-agent Vs Multi-agent Systems
3 min
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Day 14 – When Not To Use Agentic Ai
4 min
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Day 15 – Building Your First Simple Ai Agent
4 min
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Day 16 – Designing Agent Prompts That Actually Work
3 min
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Day 17 – Using Agents For Data Analysis Tasks
3 min
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Day 18 – Agentic AI For Software Development
4 min
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Day 19 – Customer Support Agents (tickets → Resolution)
4 min
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Day 20 – Research Agents (web Search + Summarization)
4 min
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Day 21 – Agent Failure Modes & Debugging Techniques
5 min
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Day 22 – Multi-agent Collaboration (manager–worker Model)
4 min
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Day 23 – Agentic Ai In Product Management
4 min
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Day 24 – Agentic AI In Dev Ops & Mlops
4 min
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Day 25: Security & Guardrails for AI Agents 🔐🛡️
4 min
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Day 26 – Cost Optimization In Agentic Systems
4 min
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Day 27 – Evaluating Agent Performance (metrics That Matter)
4 min
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Day 28 – Agentic AI Vs AI Workflows (2026 Perspective)
3 min
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Day 29 – Future Of Work With Agentic AI
3 min
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Day 30 – How To Start A Career In Agentic AI (roadmap)
3 min