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 - Orchestrating Autonomous AI Agents training are: Advanced Python proficiency, LLMs, classical RAG, and frameworks like LangChain or LlamaIndex. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Agentic RAG - Orchestrating 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 - Orchestrating 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 - Orchestrating 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 - Orchestrating 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 Agentic RAG architectures for professional AI systems

Develop intelligent autonomous agents in enterprise settings

Implement multi-agent workflows with optimized retrieval

Optimize response accuracy via tools and advanced reasoning

Design scalable and certifiable Agentic RAG pipelines

Deploy Agentic RAG solutions in secure production environments

Training program

Module 1Fundamentals of Agentic RAG and Autonomous Agents

Definition of Agentic RAG. Differences with classical RAG. Principles of AI agents. Installation of LangGraph and LlamaIndex environments. Creation of first simple agent. Tool and memory management. Exercises on concrete retrieval cases. Setup of enterprise capstone project. Implementation of basic workflows. Analysis of common errors. Professional skills in generative AI. (92 words)

Module 2Advanced Implementation of Multi-Agent Agentic RAG

Design of agent graphs. Intelligent request routing. Integration of vector embeddings. Custom tools for agents. Reasoning chains and ReAct pattern. Professional multi-agent exercises. Hybrid retrieval optimization. Capstone project: enterprise search agent. Implementation of real data science cases. Unit tests for autonomous agents. Development of scalable AI skills. Persistent state management. (98 words)

Module 3Optimization and Evaluation of Agentic RAG

Agent fine-tuning techniques. RAG performance monitoring. Precision and latency metrics. Advanced hallucination management. Agent security and guardrails. Optimization exercises for enterprise projects. Integration of dynamic knowledge bases. Capstone project: refinement of complex agent. Implementation of AI benchmarks. LangSmith observability tools. Certifiable deployment skills. Inference cost analysis. (102 words)

Module 4Production Deployment of Agentic RAG

Agent containerization with Docker. Cloud deployment on AWS/GCP. Scalable FastAPI APIs. CI/CD pipelines with GitHub Actions. Production monitoring with Prometheus. Deployment exercises for enterprise cases. Finalized and deployed capstone project. Defense of acquired skills. Best practices for AI security. Post-training support resources. Implementation of integration with existing systems. Business cost optimization. Professional AI certifications. (96 words)