Training
in agentic AI.
Two complementary learning paths: AI adoption strategy for leaders and AI agent development for technical teams.
From strategic vision to technical implementation
Agentic AI systems can plan steps and use tools to work towards an objective within defined limits. Bennen Technologies offers two learning paths tailored to decision-makers and technical teams.
Our teaching approach draws on field experience in regulated environments. Each module combines theory, case studies and practical workshops. Participants leave with tools, decision frameworks and skills they can apply.
The two paths can be taken independently or together, depending on participants’ roles and goals. We also adapt the content to each client’s industry and organizational context.
Typical deliverables
Training materials, slides and guides
AI use-case evaluation framework
Personalized adoption roadmap
Code examples and reference architectures
Access to practice environments
Bennen Technologies certificate of participation
Post-training follow-up (30 days)
Business strategy for agentic AI
For executives, managers and decision-makers. This path covers agentic AI fundamentals, strategic issues, organizational adoption and the factors that support lasting transformation.
Fundamentals & strategic issues
Understand agentic AI, its capabilities, limitations and implications for business models and organizational processes.
Adoption & change management
Build a culture that supports AI adoption, address organizational resistance, define responsibilities and track progress with appropriate indicators.
AI leadership & governance
Develop a clear AI vision, align stakeholders, manage ethical and regulatory risks, and establish agent governance suited to your industry.
Selecting valuable use cases
A structured method for identifying, evaluating and prioritizing agentic AI use cases based on business value, technical feasibility and risk.
AI agent programming & design
For developers, architects and technical teams. This path covers AI agent fundamentals, development frameworks, multi-agent architectures and integration with existing systems.
Technical foundations of AI agents
Agent architecture, reasoning loops, memory, tools and orchestration. Understand the fundamental patterns before writing code.
Frameworks & implementation
Use LangChain, LangGraph, AutoGen and CrewAI to design agents and multi-agent systems. Integration with Azure OpenAI, Claude and Gemini.
Multi-agent architecture & orchestration
Multi-agent architecture patterns: supervisor and specialist agents, communication, state management and production error handling.
Practical case workshops
Design and implement AI agents for concrete tasks: research, process automation and integration with enterprise APIs and databases.
Technologies & platforms covered
Related services
MLOps, LLMOps & intelligent automation ↗
AI model production readiness and deployment of generative solutions.
Data, BI and AI consulting & architecture ↗
Robust target architectures to organize, govern and make better use of data.
Business applications compliant by design ↗
Custom platforms and applications built for security and compliance.
Ready to develop your agentic AI skills?
Contact us to discuss the right format for your team — in person, online or hybrid, tailored to your industry.
