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 - Creating High-Performing Autonomous AIs training are: Proficiency in Python, basic knowledge of RAG and LLMs such as LangChain or LlamaIndex. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Agentic RAG 2026 - Creating… | 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 - Creating High-Performing Autonomous AIs 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 - Creating High-Performing Autonomous AIs 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 - Creating High-Performing Autonomous AIs 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 Agentic RAG architectures for professional AI systems

Develop intelligent autonomous agents in a business context

Implement agentic RAG workflows with 2026 tools

Optimize retrieval and generation for precise responses

Design scalable, certifiable Agentic RAG applications

Deploy RAG agents in production with advanced monitoring

Training program

Module 1Agentic RAG 2026 Fundamentals: Principles and Architecture (LangGraph Frameworks, Vector Tools)

Dive into the advanced basics of Agentic RAG 2026 by installing a complete environment with LangGraph and Pinecone, build your first autonomous agent capable of reasoning over external data, explore routing and tool-calling patterns for intelligent decisions, perform practical exercises on real business cases such as internal document analysis, and produce a functional prototype with precision evaluation by the end of the day.

Module 2Agentic RAG 2026 Implementation: Multi-Agent Workflows (Orchestration, Persistent Memory)

Build complex Agentic RAG 2026 workflows by integrating vector memory and collaborative agents via CrewAI, simulate professional scenarios like augmented search in customer support, code custom tools for hybrid RAG retrieval, test robustness in real-time against ambiguous queries, generate concrete deliverables such as performance dashboards, and optimize feedback loops for increased agent autonomy.

Module 3Agentic RAG 2026 Optimization: Fine-Tuning and Evaluation (Metrics, 2026 LLMs)

Advance to Agentic RAG 2026 optimization with fine-tuning on models like GPT-4o or Llama 3.1, measure precision using RAGAS and custom metrics, apply reranking and query rewriting techniques on enterprise datasets, conduct practical A/B tests to compare performances, produce detailed evaluation reports, and integrate ethical safeguards for secure production deployments.

Module 4Agentic RAG 2026 Deployment: Scaling and Monitoring (Kubernetes, Observability)

Master production deployment of Agentic RAG 2026 on Kubernetes with FastAPI, configure horizontal scalability and observability via Prometheus/Grafana, simulate high loads on critical business cases like HR automation, implement CI/CD for smooth updates, generate a complete ready-to-use pipeline, and conclude with a certifying code review and evolution plan for your professional projects.