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 2026 - Mastering Autonomous AI Agents training are: Python basics, notions of embeddings and LLMs like GPT. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Agentic RAG 2026 - Mastering Autonomous AI Agents training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Agentic RAG 2026 - Mastering Autonomous… | 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 2026 - Mastering 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 2026 - Mastering 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 2026 - Mastering 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 objectives

Master the fundamentals of Agentic RAG for professional AI agents

Develop autonomous retrieval-augmented workflows for enterprise use

Implement intelligent agents using tools like LangChain and LlamaIndex

Design scalable and certifiable agentic reasoning chains

Optimize RAG performance for high-performing 2026 applications

Integrate Agentic RAG into real projects with vector databases

Acquire certifiable AI agent skills to boost your career

Training program

Module 1Agentic RAG 2026 Fundamentals: Environment and First Autonomous Agents (LangChain, Pinecone)

Quick installation of the Agentic RAG environment with Python and key libraries like LangChain and LlamaIndex, hands-on with OpenAI embeddings and vector stores like Pinecone or FAISS, creation of your first simple agent that retrieves and generates contextual responses, practical exercises on enterprise datasets, tests of chaining for autonomous tasks, production of a basic prototype with complete documentation to validate immediate learning.

Module 2Agentic RAG 2026 Architectures: Advanced Workflows and Collaborative Tools (CrewAI, Multi-Agent Tools)

Exploration of multi-agent agentic architectures for RAG 2026, configuration of workflows with CrewAI and AutoGen, integration of external tools like search engines and business APIs, development of agents that plan, delegate, and iterate on complex queries, real enterprise cases like internal document analysis, collaborative pair exercises to simulate real scenarios, prompt optimization for reliable reasoning, delivery of a functional agentic RAG system ready to scale.

Module 3Agentic RAG 2026 Deployment: Optimization and Production (Docker, Monitoring, Evaluation)

Production deployment of Agentic RAG agents with Docker and Streamlit for user interfaces, setup of monitoring with LangSmith and performance evaluation via RAGAS metrics, cost and latency optimization for 2026 environments, exercises on hybrid retriever tuning and reranking, business use cases like intelligent enterprise chatbots, finalization of the red-thread project with end-to-end tests, delivery of optimized source code and maintenance plan for immediate team adoption.