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Agentic AI Engineer
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Agentic AI Engineer

Build advanced AI agents capable of reasoning, decision making, and autonomous task execution using modern AI frameworks.

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Step 1

AI Fundamentals & LLMs

Master the foundational elements of Large Language Models and prompt engineering.

1. Introduction to Large Language Models (LLMs)

Understand how LLMs work, transformer architecture, and base models vs. instruct models.

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2. Prompt Engineering & In-Context Learning

Few-shot prompting, chain-of-thought reasoning, and optimizing context windows.

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3. Embeddings & Vector Databases

Converting text to vectors and performing semantic search.

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Step 2

Building Agentic Systems

Go beyond chat interfaces to create autonomous agents that take action.

4. The Concept of AI Agents (Reasoning & Action)

ReAct pattern, goal-oriented planning, and autonomous execution loops.

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5. Tool Use & API Integration

Function calling, providing agents with APIs, and handling structured JSON output.

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6. Memory & State Management

Implementing short-term buffer memory and long-term semantic memory.

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Step 3

Advanced Architectures

Build complex multi-agent workflows and robust AI applications.

7. Multi-Agent Systems

Orchestrating teams of agents using frameworks like AutoGen and CrewAI.

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8. Retrieval-Augmented Generation (RAG)

Grounding agent responses in custom data, chunking strategies, and advanced retrieval.

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9. Evaluating & Deploying Agents

Measuring agent performance, handling hallucinations, and productionizing AI workflows.

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