Information Package / Course Catalogue
Artificial Intelligence Agents
Course Code: MCS514
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

This course aims to equip students with a foundational understanding of AI agents that perceive their environment reason, plan, and take actions to achieve goals. Students will progress from core concepts to hands-on code, exploring the design patterns, frameworks, and ethical considerations essential for building reliable and trustworthy AI agents using modern large language model (LLM) infrastructure.

Course Content

This course introduces students to the theory and practice of building AI agents. It begins with an introduction to AI agents, their use cases, and the agentic paradigm. Students then explore major agentic frameworks and learn to identify and apply common agentic design patterns, including tool use, planning, multi-agent coordination, and metacognition. The course covers Agentic Retrieval-Augmented Generation (RAG) and techniques for building trustworthy, safe agents.

Name of Lecturer(s)
Learning Outcomes
1.Explain the concept of AI agents, their components, and common use cases.
2.Implement tool use, planning, and RAG patterns using Python and agentic frameworks.
3.Design and build a functional AI agent system.
4.Apply multi-agent design patterns to coordinate specialized agents for complex tasks.
5.Evaluate AI agent performance using systematic benchmarking and tracing methodologies.
6.Assess ethical, safety, and trust considerations in AI agent design.
Recommended or Required Reading
1.Wang, L., Ma, C., et al. (2024). A Survey on Large Language Model based Autonomous Agents.Frontiers of Computer Science, 18(6), 186345..
2.Xi, Z., et al. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. arXiv:2309.07864.Artificial Intelligence: A Modern Approach.
3.Weng, L. (2023). LLM-powered Autonomous Agents. Lilian Weng’s Blog. Retrieved from lilianweng.github.io/posts/2023-06-23-agent/. .
4.Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023.
5.Schick, T., et al. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. NeurIPS 2023.
6.Chase, H. (2023). LangChain Documentation. https://python.langchain.com
7.HuggingFace Datasets (tool-use, agent benchmarks)
8.AgentBench: A Comprehensive Benchmark for LLM-based Agents
9.ToolBench: Tool-use evaluation datasets (ToolLLM)
10.Python (autogen, langchain, langgraph)
11.GitHub
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to AI Agents and Use Cases Definition of AI agents, agent components (perception, reasoning, action), key use cases and motivation for agentic AI systems.
Week 2 - Theoretical
Exploring AI Agentic Frameworks
Week 3 - Theoretical
Understanding Agentic Design Patterns Core agentic patterns: ReAct, reflection, and chain-of-thought; comparing patterns for different task types and use cases.
Week 4 - Theoretical
Tool Use Design Pattern Enabling agents to call external tools and APIs; function calling, tool selection strategies, and practical code examples.
Week 5 - Theoretical
Agentic RAG (Retrieval-Augmented Generation) Combining agents with retrieval systems; agentic RAG architectures, indexing strategies, and iterative retrieval loops. + Team Projects
Week 6 - Theoretical
Building Trustworthy AI Agents Safety, reliability, and transparency in agent design; human-in-the-loop strategies, guardrails, and evaluation methods. + Team Projects
Week 7 - Theoretical
Planning Design Pattern Task decomposition, goal-directed planning, and plan execution strategies for LLM-based agents. + Team Projects + Team Projects
Week 8 - Theoretical
Multi-Agent Design Pattern Architectures for agent teams; communication protocols, orchestration, and delegation among multiple specialized agents. + Team Projects
Week 9 - Theoretical
Metacognition Design Pattern Enabling agents to monitor and critique their own reasoning; self-reflection loops, self-correction, and chain-of-verification. + Team Projects
Week 10 - Theoretical
AI Agents in Production Deploying agents to production on a platform; observability, scalability, cost management, and production best practices. + Team Projects
Week 11 - Theoretical
Agent Memory and Context Management Real-time integration of IoT, edge computing, and situational awareness for responsive environments + Team Projects
Week 12 - Theoretical
Agent Evaluation and Testing Systematic evaluation of AI agent performance; benchmarking, tracing, red-teaming, and automated test harnesses. + Team Projects
Week 13 - Theoretical
Ethics, Safety, and Responsible AI Agents Ethics, Privacy, and Bias in AI Agent Systems Responsible agent design; bias mitigation, privacy-preserving agents, regulatory considerations, and societal impact. + Group Project Presentations and Knowledge Exchange Students present agent-based AI solutions, demonstrating functionality, design rationale, and evaluation results.
Week 14 - Theoretical
Group Project Presentations and Course Wrap-Up Final project presentations; peer review, Q&A, and course retrospective on key lessons in AI agent development.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%10
Midterm Examination1%15
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Project220040
Presentation 212432
Report26012
TOTAL WORKLOAD (hours)154
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
3
3
3
3
5
3
5
3
3
OÇ-2
4
4
4
3
5
4
3
4
3
OÇ-3
3
3
5
5
5
4
3
5
5
OÇ-4
5
4
5
4
4
4
5
5
4
OÇ-5
3
3
3
3
4
3
4
3
3
OÇ-6
1
2
3
3
2
2
3
3
2
Adnan Menderes University - Information Package / Course Catalogue
2026