Practical Agentic AI: RAG, Planning & Vector Search

• Transitioning from Chatbots to Autonomous Agents: Move beyond basic conversational interfaces to develop autonomous systems capable of executing multi-step logic. This course bridges the gap between text generation and proactive problem-solving, teaching you to build software that independently acts on goals.
• The Mechanics of Reasoning: Gain a deep understanding of how Large Language Models are guided through complex reasoning chains using the ReAct framework. You will learn to structure prompts that force the model to verbalize its thought process, leading to more transparent execution of tasks.
• Integrating Real-Time Data: Explore the integration of Retrieval-Augmented Generation within an agentic loop. Learn how agents decide when they need more information and perform secondary searches to fill gaps in their knowledge dynamically, rather than relying on a search result.
• Architectural Best Practices: Study design patterns essential for building reliable AI agents, including the separation of reasoning from execution. This ensures your agentic systems are maintainable and capable of operating in high-stakes production environments without manual intervention.

Requirements / Prerequisites

• Intermediate Python Proficiency: A solid grasp of Python is essential, particularly regarding asynchronous programming and object-oriented principles used in modern AI frameworks to manage the complex internal states of autonomous agents.
• Large Language Model Basics: Foundational understanding of how LLMs function, including context windows and tokenization, which impact how an agent perceives and processes information during a multi-step workflow.
• API Integration Skills: Familiarity with handling RESTful APIs and environment variables is required, as the course relies on connecting models to external data sources and tools for real-world functionality.
• Development Environment: Ability to set up virtual environments and use IDEs for debugging multi-file architectures, which is necessary for managing the dependencies required by the agentic ecosystem.

Skills Covered / Tools Used

• Orchestration with LangChain and LangGraph: Master the tools for building stateful AI applications. Use LangGraph to create cyclical graphs that represent an agent’s logic, allowing for complex branching and multi-agent collaboration strategies.
• Vector Search and Pinecone: Gain hands-on experience with vector search engines. Learn how to perform semantic similarity searches and implement metadata filtering to provide your agents with precise context from massive datasets.
• Pydantic and Structured Output: Use Pydantic to enforce strict data schemas on model outputs. This ensures that agentic thoughts are reliably parsed into code that your applications can execute without unexpected failures.
• Function Calling and Tool Use: Empower agents to interact with the world by defining custom tools. Learn the protocols for allowing an LLM to call your own Python functions or external APIs to complete tasks.
• Memory Management: Implement multi-layered memory systems, including short-term buffers and long-term semantic storage, enabling agents to maintain context over long interactions and remember historical user data.

Benefits / Outcomes

• Building Proactive Systems: Transition from a developer who builds reactive apps to one who designs proactive agents. Your systems will monitor data and take initiative, performing corrective actions whenever specific business conditions are met.
• Scalable AI Architecture: Walk away with a blueprint for scaling agentic systems. Understand how to handle concurrency, optimize token usage to reduce costs, and swap underlying models as newer, more efficient versions emerge.
• Career Advancement: As the industry moves from basic LLM integration to full-scale AI agents, these skills will place you at the forefront of the next wave of AI development in the 2025 technology market.
• Robust Error Handling: Master the art of graceful failure. Build agents that do not just stop at roadblocks but analyze the error, adjust their strategy, and attempt a secondary path to find solutions.

PROS

• Current 2025 Content: The curriculum is updated with the latest library versions and industry trends, ensuring you are not learning outdated methodologies or deprecated code.
• Practical Project Focus: Every concept is reinforced with a hands-on implementation, moving quickly from theory to code that can be immediately adapted for commercial use cases.
• High-Level Reasoning: Unlike many courses that focus only on simple RAG, this course emphasizes the planning and reasoning phases which are the true heart of advanced Agentic AI.

CONS

• High Technical Density: The fast-paced nature of the course requires significant prior coding experience, as it bypasses introductory programming to focus on advanced AI orchestration and complex agentic logic.