Learn Agentic AI by Building Real AI Agents with AWS Bedrock – Autonomy, Actions, LLM, Knowledge Base, Hands-On Training
β±οΈ Length: 5.8 total hours
π₯ 183 students
π November 2025 update
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- Course Overview:
- Dive into Agentic AI: build intelligent, autonomous systems capable of reasoning and action, from foundational concepts to practical implementation.
- Understand Agentic AI as the next AI frontier, empowering applications with true autonomy, complex decision-making, and dynamic environmental interaction.
- Master building, deploying, and scaling sophisticated AI agents using AWS Bedrock, gaining industry-standard, enterprise-grade skills.
- Shift your perspective from basic Large Language Model (LLM) queries to orchestrating goal-driven AI architectures, where LLMs power agents to achieve adaptive objectives.
- Explore essential architectural patterns and design principles for creating resilient and effective AI agents, covering their lifecycle from perception to action and learning.
- Uncover Agentic AI’s immense potential across various domains: automate business processes, enhance customer service, power intelligent data analysis, and create novel interactive experiences.
- Experience a balanced curriculum, combining theoretical grounding with intensive practical application, reinforcing every concept via tangible, working agent implementations.
- Requirements / Prerequisites:
- Foundational programming concepts: Familiarity with Python basics (syntax, data structures, functions) will significantly enhance your practical learning experience.
- Conceptual LLM understanding: Basic knowledge of what Large Language Models are and how they broadly function; prior prompting experience is beneficial, though not essential.
- Basic cloud computing familiarity: An understanding of cloud concepts and resource management is advantageous, but no prior AWS-specific expertise is strictly required.
- AWS account access: A free-tier eligible AWS account is necessary to fully engage with the hands-on cloud labs and deploy your developed agents effectively.
- Proactive learning mindset: An eagerness to experiment, troubleshoot, and engage actively with code is crucial for success in this highly practical course.
- Skills Covered / Tools Used:
- Agent Design Patterns: Master fundamental agent design patterns, including reflective, planning, and tools-based agents, to select optimal architectures for diverse problem statements.
- Advanced Prompt Engineering: Techniques for instructing LLMs to reason, plan, and self-correct within complex, multi-step agent workflows, extending beyond simple, single-turn interactions.
- API Integration & Tool Development: Develop proficiency in defining custom functions and services, empowering agents to securely interact with external systems, databases, and web services.
- Cloud-Native Agent Deployment: Gain practical experience with best practices for infrastructure setup, permission management, and ensuring agent availability and scalability using AWS Bedrock.
- Debugging & Monitoring: Acquire essential skills to diagnose issues, track agent execution paths, and optimize performance in live autonomous environments.
- Ethical AI Agent Development: An introduction to fostering awareness of potential biases, security implications, and responsible deployment practices for autonomous systems.
- AWS Service Application: Hands-on application of specific AWS services, including AWS Bedrock (core orchestration), Amazon S3 (knowledge bases), and AWS Lambda (custom agent tools).
- Agent Frameworks Exposure: Learn to utilize popular agent frameworks (like components inspired by LangChain or similar libraries) to simplify complex LLM agent construction.
- Contextual Reasoning & Retrieval: Develop robust strategies for agents to effectively leverage dynamic and historical data for more informed decision-making processes.
- Benefits / Outcomes:
- Career Advancement: Launch or upskill significantly in the rapidly accelerating field of Agentic AI, positioning yourself at the forefront of AI innovation with highly sought-after expertise.
- Independent Agent Development: Develop the ability to independently prototype, build, and deploy sophisticated AI agents that autonomously perform tasks and solve complex problems.
- Deep Ecosystem Understanding: Gain a profound and actionable understanding of the entire Agentic AI ecosystem, from foundational components like LLMs and knowledge bases to advanced action groups.
- AWS Bedrock Proficiency: Achieve confidence in leveraging AWS’s cutting-edge Generative AI services, particularly AWS Bedrock, making you proficient in cloud-native AI development.
- Practical Portfolio: Construct a compelling portfolio of working AI agent projects, demonstrating your real-world capabilities to potential employers or for personal ventures.
- Foundation for Advanced AI: Establish a strong foundation for exploring more advanced topics in AI, machine learning, and autonomous systems development, serving as a springboard for specialization.
- Problem-Solving Mindset: Cultivate a problem-solving mindset, enabling you to identify opportunities where autonomous agents can drive efficiency and innovation.
- Future-Proof Skills: Master technologies that are defining the next generation of intelligent applications and enterprise solutions, ensuring your skill set remains relevant.
- PROS of this course:
- Highly Practical: Emphasizes building functional agents, providing tangible results and deep understanding through hands-on experience.
- Industry-Leading Platform: Utilizes AWS Bedrock, ensuring relevant, scalable skills applicable in professional cloud environments.
- High-Demand Skill Set: Agentic AI is an emerging and critical area, making acquired skills extremely valuable and career-enhancing.
- Beginner-Friendly: Carefully designed to guide learners from fundamental concepts to advanced implementation without prior deep AI expertise.
- Immediate Applicability: Skills directly apply to real-world problems, enabling learners to contribute to innovative AI solutions.
- Future-Oriented: Covers foundational concepts and modern tools shaping the future of autonomous systems and intelligent automation.
- CONS of this course:
- The 5.8-hour duration, while comprehensive for an introductory course, may offer less time for exhaustive deep dives into every advanced configuration or complex edge-case debugging scenario, potentially requiring additional self-study for highly niche applications.
Learning Tracks: English,Business,Other Business
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