Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Agentic RAG - Building Autonomous AI Agents training are: Mastery of Python, knowledge of LLMs, embeddings, and vector databases. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Agentic RAG - Building Autonomous AI Agents training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Agentic RAG - Building Autonomous AI… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Agentic RAG - Building Autonomous AI Agents training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Agentic RAG - Building Autonomous AI Agents training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Agentic RAG - Building Autonomous AI Agents training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Master the principles of Agentic RAG for professional certifying AI systems

Develop autonomous agents integrating advanced retrieval and generation

Design agentic workflows with tools like LangGraph and CrewAI

Implement multi-agent RAG for complex enterprise use cases

Optimize performance and reliability of AI agents in production

Deploy scalable and secure Agentic RAG applications

Training program

Module 1Agentic RAG Fundamentals: Architecture and Intelligent Retrieval (LangChain, Embeddings)

Immersion in the basics of Agentic RAG via installation of Python environments with LangChain and LlamaIndex, exploration of embeddings for precise semantic search on enterprise datasets, setting up classic RAG pipelines evolving to simple agents, practical exercises on real cases like automated Q&A, creation of a first reactive agent with routing tools, production of a functional prototype tested live to validate professional skills.

Module 2Agent Design: Workflows and Collaborative Tools (LangGraph, CrewAI)

Deep dive into modeling autonomous agents using LangGraph for dynamic execution graphs, configuration of CrewAI to orchestrate multi-agents specialized in research and synthesis, practical workshops on business scenarios like legal document analysis, integration of vector stores like Pinecone for hybrid retrieval, development of a red thread agent capable of decomposing complex tasks, real-time debugging and optimization with trainer feedback for certifying mastery.

Module 3Advanced Agentic RAG: Multi-Agents and API Integrations (OpenAI, Vector DB)

Deployment of interconnected multi-agent systems via OpenAI and Anthropic APIs, exploration of reranking and self-reflection techniques to reduce hallucinations, practical cases on customer support automation with real-time retrieval from Weaviate, collaborative exercises to simulate critical enterprise workflows, implementation of ethical guardrails and performance monitoring, completion of the red thread project with concrete evaluation metrics, directly enhancing enterprise skills.

Module 4Optimization and Deployment of Agentic RAG: Scaling and Production (Docker, Monitoring)

Focus on cost and latency optimization with advanced caching and intelligent batching, containerization via Docker for cloud-ready deployment on AWS or Vercel, setup of monitoring with LangSmith to trace agentic decisions, robustness testing under high loads and failure scenarios, workshops to produce a live-deployed MVP accessible to participants, peer code review and maintenance plan, ensuring a smooth transition to scalable professional certifying uses.