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 - Developing Autonomous AI Agents training are: Python proficiency, LLM basics, basic RAG and vector database APIs. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Agentic RAG - Developing 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 - Developing 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 - Developing 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 - Developing 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 Agentic RAG to create professional autonomous AI agents

Develop certified multi-agent architectures for enterprise

Implement intelligent workflows with LangChain and LlamaIndex tools

Optimize response accuracy via dynamic adaptive retrieval

Design scalable Agentic RAG systems for 2026 production

Evaluate and deploy secure AI agents in business contexts

Training program

Module 1Agentic RAG Fundamentals: Concepts and Environment (LangChain, vector stores)

Dive into Agentic RAG principles to boost LLMs with decision-making agents, install the environment via Poetry and Docker, configure embeddings with HuggingFace and Pinecone, perform your first augmented retrievals, test simple agents on real enterprise cases like intelligent Q&A, produce a functional prototype with initial accuracy metrics.

Module 2Agentic RAG Architecture: Multi-Agent Tools and Reasoning (LlamaIndex, CrewAI)

Design hybrid Agentic RAG architectures for complex tasks, integrate LlamaIndex for advanced indexing and CrewAI for multi-agent orchestration, develop custom routing and fallback tools, apply to large business datasets, generate autonomous workflows that decide retrieval or generation actions, validate with practical exercises and deliverable diagrams.

Module 3Agentic RAG Implementation: Advanced Workflows and Tools (ReAct, hierarchical planning)

Implement Agentic RAG agents with ReAct patterns and multi-step planning, code dynamic tool-calling to external APIs and vector databases, optimize prompts for chain-of-thought reasoning, handle concrete cases like enterprise document analysis or semantic search, debug live with LangSmith observability, deliver a fully functional tested agent.

Module 4Agentic RAG Optimization: Evaluation and Tuning (RAGAS, adaptive fine-tuning)

Evaluate Agentic RAG performance via RAGAS frameworks and custom benchmarks, measure faithfulness and answer relevance on your projects, apply hybrid reranking and intelligent query rewriting, fine-tune retrievers with LoRA on enterprise data, reduce hallucinations by 50% in practice, produce optimization reports and production-ready automated pipelines.

Module 5Agentic RAG Deployment 2026: Scalability and Security (FastAPI, Kubernetes, monitoring)

Deploy Agentic RAG systems to production via FastAPI and Docker Compose, scale with Kubernetes and Ray Serve for massive loads, integrate RAG security with guards and rate-limiting, monitor in real-time via Prometheus and Grafana, test end-to-end on critical business scenarios, finalize your red thread project with cloud deployment and skills certificate.