Learn how to Design, Code, Deploy and Manage AI Agents

Introduction to Multi Agent systems

Offers a broad understanding of how multiple intelligent agents collaborate, setting the stage for an ecosystem where diverse AI components work together seamlessly.

Introduction to LLMs and their limitations

Highlights the boundaries of large language models, helping you understand why relying on a single model often falls short in complex applications.

Introduction to RAG: Bridging the Gap

Explains how retrieval-augmented generation (RAG) fills the knowledge gaps of LLMs by pulling in external, contextually relevant data, ensuring more accurate and timely responses.

Defining Key Components of a RAG System

Breaks down the essential building blocks—retrievers, vector databases, embeddings, and LLMs — So you know precisely which pieces must fit together.

Defining Product Scope: Identifying Use Cases

Identifying use cases: guides you to pinpoint where multi-agent RAG solutions can add real value, ensuring that you focus on meaningful, outcome-driven scenarios.

Choosing a Platform and Tools

Helps you select the right frameworks, libraries, and infrastructure that can support robust RAG implementations and multi-agent workflows.

Configuring the Development Environment

Ensures your tools, dependencies, and environments are properly aligned, creating a stable foundation for iterative development and testing.

Introduction to Cloud Platforms for Deployment

Reviews the pros and cons of different cloud providers, ensuring you pick a scalable and secure environment for hosting agents.

Containerization with Docker

Introduces containerization for consistent deployment, making it easier to replicate development conditions in production.

Designing the Data Model for RAG

Aligns your data structures with retrieval needs, ensuring fast, accurate lookups that inform your agents.

Building the Retrieval Component

Focuses on implementing a retrieval layer that quickly surfaces relevant documents guiding the LLM in generating more accurate answers.

Integrating the LLM for Generation

Explains how to seamlessly merge your chosen language model with retrieved data so the agents responses are both context-rich and authoritative.

Building APIs for Data Access and Interaction

Exposes the underlying capabilities through APIs, enabling other services and agents to easily request and use data.

Understanding Event-Driven Architectures

Introduces the concept of events as triggers for agent actions, ensuring that your system responds dynamically to changing conditions.

Building Data Pipelines for Real-Time Updates

Sets up flows to keep your agents informed with up-to-the-minute data, preventing stale responses.

Designing an Intuitive User Interface

Emphasizes user-centric design so that human operators can interact smoothly with the multi-agent systems capabilities.

Building Interactive Components

Details how to create dynamic UI elements that let users guide the multi-agent process and view results in real time.

Developing a Robust API for Front-End Integration

Ensures front-end tools can seamlessly request data, trigger events, or call AI functions, promoting a flexible and responsive UX layer.

Understanding User Behavior and Preferences

Encourages analyzing user patterns to tailor the experience, helping agents provide more relevant information.

Personalizing the User Experience (Learning from the User in Real Time)

Shows how the system can adapt based on user feedback, making results more personalized And efficient.

Implementing Adaptive UI Elements

Discusses dynamically altering the interface based on what users need, ensuring that the right tools and data are always at their fingertips.

Deploying to Production

Guides you through launching your multi-agent RAG solution with minimal downtime and maximum readiness.

Monitoring and Logging

Stresses the importance of real-time performance data and logs to quickly identify issues and maintain quality.

Scaling for Increased Traffic

Prepares your system to handle growing user demands, ensuring that retrievals and generations remain fast and accurate.

Security Best Practices

Protects user data and the integrity of your agents, essential for maintaining trust and compliance.