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Advanced Level7 Gün

Applied AI Agent Development Training: LLM, RAG, and Autonomous Systems

Learn to design and deploy autonomous AI agents capable of planning and executing tasks without human intervention using LLMs, tool calling, and long-term memory architectures.

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About This Course

The AI ​​Agent Development Training equips participants with the skills to design, develop, and deploy autonomous, task-oriented AI agents using large language models (LLMs), tool use, planning-execution cycles, and long-term memory architectures. The training covers the end-to-end lifecycle, from single-agent scenarios to multi-agent coordination, security and regulatory compliance, CI/CD, and observability practices. Participants gain the expertise to automate real-world workflows and deploy scalable and auditable agent systems in enterprise environments.

Training Methodology

End-to-end lifecycle (Design -> Dev -> CI/CD -> Monitor)

Real-world scenarios and workflow automation

"Tool-augmented" approach for cost optimization

Security and Regulation (GDPR) focused architecture

Who Is This For?

Data Scientists & ML Engineers
Software Developers & Platform Engineers
DevOps / SRE Specialists
Product & Project Managers

Why This Course?

1

To reduce human error by increasing operational efficiency.

2

To minimize hallucination rates with RAG integration.

3

To dynamically adapt to increased load with scalable automation.

4

To gain competitive advantage by rapidly deploying autonomous features.

Learning Outcomes

Developing agents that plan and execute tasks without human intervention.
Competence in LLM-based logic and external tool integration.
Ability to build short and long-term memory architectures.
Expertise in observing, testing, and optimizing agent systems.

Requirements

Basic knowledge of Python programming.
Familiarity with API (REST/GraphQL) and Database concepts.
Basic understanding of LLM and Generative AI terminology.

Course Curriculum

  • Welcome and Course Objectives
    • Program structure, modules, timeline
    • Participant profiles and prerequisites
    • Success indicators, evaluation criteria
  • AI Agent Definition, Motivation, and History
    • Rule-based → data-driven → LLM-backed evolution
    • AutoGPT, BabyAGI, CrewAI, OpenAI Function Calling timeline
    • "Tool use = the new fine-tuning" paradigm
  • Agent Ecosystem and Core Components
    • Planner, executor, critic layers
    • Memory classes: short-term, long-term, episodic, semantic
    • Toolbox, action-observation loop
    • Orchestration patterns: single/multi-agent, synchronous/asynchronous

Instructor

Şükrü Yusuf Kaya

Şükrü Yusuf Kaya

AI Consultant & Instructor

Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading the integration of artificial intelligence technologies into the global business landscape. With operations spanning 6 different countries, he bridges the gap between the theoretical boundaries of technology and practical business needs, overseeing end-to-end AI projects in data-critical sectors such as banking, e-commerce, retail, and logistics. Deepening his technical expertise particularly in Generative AI and Large Language Models (LLMs), KAYA ensures that organizations build architectures that shape the future rather than relying on short-term solutions. His visionary approach to transforming complex algorithms and advanced systems into tangible business value aligned with corporate growth targets has positioned him as a sought-after solution partner in the industry. Distinguished by his role as an instructor alongside his consulting and project management career, Şükrü Yusuf KAYA is driven by the motto of "Making AI accessible and applicable for everyone." Through comprehensive training programs designed for a wide spectrum of professionals—from technical teams to C-level executives—he prioritizes increasing organizational AI literacy and establishing a sustainable culture of technological transformation.

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