By the end of this course, you will be able to:

1. Understand the layered architecture of Agentic AI systems.
2. Implement memory, planning, and reasoning modules.
3. Build autonomous task-oriented agents using LLMs and APIs.
4. Design communication and coordination among multiple agents.
5. Integrate external tools, vector databases, and APIs for enhanced capability.
6. Apply safety, governance, and evaluation methods for reliable deployment.
7. Deploy agentic systems using Docker, Kubernetes, or cloud services.
8. Monitor, scale, and iterate agent systems for performance optimization.

Course Syllabus

Module 1: Introduction to Agentic AI
Foundations of agentic systems, definitions, and historical evolution.
Key differences between traditional AI, LLMs, and Agentic AI.

Module 2: Core Components of Agentic Architecture
Cognition, memory, planning, reasoning, and feedback loops.

Module 3: Understanding LLMs in Agentic Systems
Prompting vs. orchestration; role of transformer-based models.

Module 4: Reasoning and Decision-Making Loops
Types of reasoning (chain-of-thought, tree-of-thought, reflective reasoning).

Module 5: Agent Memory Systems
Short-term vs. long-term memory; vector databases and retrieval mechanisms.

Module 6: Tool Use and External API Integration
Designing tool interfaces; action planning through APIs.

Module 7: Multi-Agent Collaboration
Communication protocols, coordination strategies, emergent behavior.

Module 8: Environment and Context Management
Perception, context windows, and state tracking in agentic systems.

Module 9: Planning and Goal Management
Hierarchical task decomposition and planning frameworks.

Module 10: Frameworks for Agentic Systems
LangChain, CrewAI, AutoGen, Semantic Kernel, and custom pipelines.

Module 11: Designing Cognitive Architecture
Layered approach: perception → reasoning → action → evaluation.

Module 12: Simulation and Sandbox Testing
Creating safe environments for agentic experiments.

Module 13: Orchestrating Multi-Agent Workflows
Message routing, coordination layers, and control hierarchies.

Module 14: Human-in-the-Loop Systems
Combining autonomous and guided interaction strategies.

Module 15: Memory-Enhanced Retrieval Systems
Integrating RAG (Retrieval-Augmented Generation) pipelines with agents.

Module 16: Ethical, Safe, and Explainable Agentic Systems
Designing trustworthy agents with transparency and compliance.

Module 17: Evaluation and Metrics
Measuring performance, coherence, reliability, and success rates.

Module 18: Deployment and Monitoring
Using Docker, cloud services, and observability frameworks.

Module 19: Enterprise Use Cases and Patterns
Automation, business intelligence, workflow orchestration, and research.

Module 20: Capstone Project – Multi-Agent Ecosystem
Design and deploy a complex multi-agent system demonstrating reasoning, collaboration, and real-world integration.

Module 21: Agentic AI Interview Questions & Answers
Top interview Q&A covering architecture, frameworks, and design principles.

Upon completion, learners receive an industry-recognized Certificate of Mastery in Agentic AI System Architecture from Uplatz.
This certification validates your expertise in designing, orchestrating, and deploying intelligent, autonomous AI systems. It adds strong credibility for AI architect, ML engineer, and automation strategist roles.

Agentic AI is rapidly becoming the foundation of next-generation software. Expertise in this area qualifies you for high-demand roles such as:

• AI Architect
• Autonomous Systems Engineer
• Multi-Agent Systems Developer
• AI Workflow Orchestrator
• Cognitive Automation Specialist
• Research Engineer in Agentic Systems

Professionals skilled in agentic design are in demand across tech enterprises, R&D labs, startups, and automation-driven industries.

1. What is an Agentic AI System and how does it differ from traditional AI?
   An Agentic AI system is designed to act autonomously—reasoning, planning, and executing tasks—unlike traditional AI that responds to static inputs.
2. What are the key components of Agentic AI architecture?
   Core components include reasoning engine, memory, planning, action, environment interface, and feedback loop.
3. How do agents communicate in multi-agent systems?
   Through message passing, shared memory, or blackboard architectures that enable coordination and task distribution.
4. What is the role of LLMs in agentic systems?
   LLMs act as the cognitive layer for reasoning, context understanding, and language-based interaction.
5. What frameworks can be used to build agentic systems?
   LangChain, AutoGen, CrewAI, and Semantic Kernel are popular open-source options.
6. How is memory implemented in agentic architectures?
   Using vector databases like FAISS, Pinecone, or Chroma for context retrieval and long-term knowledge storage.
7. What is tool use in agentic AI?
   It refers to an agent’s ability to invoke external APIs, databases, or software tools to achieve goals.
8. How can safety and reliability be ensured in autonomous agents?
   By applying human-in-the-loop feedback, rule-based constraints, and ethical governance protocols.
9. What are key evaluation metrics for agentic systems?
   Task success rate, coherence, autonomy, efficiency, and interpretability.
10. How is a multi-agent ecosystem deployed at scale?
    By using containerized orchestration (Docker/Kubernetes) with monitoring, version control, and feedback optimization loops.