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 AutoGen - Orchestrating High-Performance Multi-Agent AI Systems training are: Mastery of Python, LLM APIs (OpenAI, Hugging Face), and AI frameworks like LangChain. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training AutoGen - Orchestrating High-Performance Multi-Agent AI Systems training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training AutoGen - Orchestrating High-Performance… | 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 AutoGen - Orchestrating High-Performance Multi-Agent AI Systems 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 AutoGen - Orchestrating High-Performance Multi-Agent AI Systems 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 AutoGen - Orchestrating High-Performance Multi-Agent AI Systems 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 AutoGen to develop professional multi-agent workflows

Design advanced GroupChats and orchestrate complex interactions

Implement custom tools and API integrations in AutoGen

Optimize AI agent performance for enterprise deployments

Deploy scalable and secure AutoGen applications in production

Develop certifiable skills in autonomous conversational agents

Training program

Module 1AutoGen Architecture: Configuring Advanced Agents and Environments (LLMs, Python Tools)

Installation of AutoGen with advanced dependencies, creation of specialized agents connected to LLMs like GPT-4 or Llama, definition of roles and capabilities via custom Python code, exercises on real business scenarios like collaborative data analysis, production of a first functional multi-agent workflow with integrated logging and monitoring for professional traceability.

Module 2AutoGen GroupChat: Orchestrating Dynamic Multi-Agent Conversations (Roles, Moderation)

Dive into GroupChatManager to manage intelligent agent debates, implementation of moderation rules and adaptive speaker selection, practical workshops on complex cases like business problem-solving in AI teams, development of scripts for smooth human-agent interactions, delivery of a tested conversational prototype with performance metrics to evaluate effectiveness in professional contexts.

Module 3AutoGen Tools and Integrations: Coding Custom Functions and External APIs

Development of custom tools via RegisterTool for tasks like web scraping or ML calculations, connecting AutoGen agents to REST APIs and SQL/NoSQL databases, guided exercises on enterprise workflow automation such as analytical report generation, real-time debugging with detailed traces, creation of reusable deliverables like an agent toolkit to accelerate internal AI projects.

Module 4AutoGen Optimization: Performance, Scalability, and Advanced Debugging (Caching, Parallelism)

LLM call optimization techniques with caching and batching in AutoGen, setting up parallelism for concurrent agents, workshops on profiling and latency reduction in high-load scenarios, robust error handling and retry mechanisms, production of an optimized application with quantified benchmarks demonstrating 50% speed gains for critical enterprise deployments.

Module 5AutoGen Deployment: Secure Production and Red-Thread Projects (Docker, Cloud)

Containerization of AutoGen workflows with Docker and deployment on AWS or Azure, securing API keys and secret management, finalization of the red-thread project on a business case like a multi-task virtual assistant, collective code review and end-to-end tests, delivery of a production-ready package with complete documentation and maintenance plan for immediate team integration.