AI Agent Developer for Autonomous Workflows

Course overview

AI agents autonomously execute multi-step tasks: they plan work steps, utilize tools and data sources, and deliver verified results. An AI agent developer for autonomous workflows designs such processes, builds them, and responsibly integrates them into business processes. This training provides the complete craft from task analysis to controlled operation. From Simple AI Application to Agent An agent fundamentally differs from a chat dialogue: it pursues goals over multiple steps. - Functional principle of AI agents: goal setting, planning, tool usage, self-monitoring - Distinction from assistance functions and classical process automation - Typical application areas from research to data maintenance to report generation - Assessing maturity levels of agent systems and their requirements Designing Agents Before building, a clean breakdown of the task is essential. - Decomposing business tasks into agent-capable sub-steps - Defining roles, goals, and action boundaries of an agent - Establishing inputs, outputs, and handover points to humans - Determining data accesses and required tools for each task Building and Orchestrating Agents - Designing system instructions, tool connections, and data accesses - Integrating interfaces and APIs for agent actions - Incorporating automation platforms and agent frameworks into existing systems - Connecting and coordinating multiple agents into multi-agent systems - Human-in-the-loop: establishing approval and control points at the right places Quality, Control, and Risks Autonomy requires control - an agent without supervision is a business risk. - Systematically checking results and narrowing down sources of error - Safeguarding against hallucinations, infinite loops, and unwanted actions - Logging and traceability of agent decisions - Developing test scenarios for agent processes Integrating Agents into Business Operations The transition from a functioning prototype to reliable operation determines the actual benefit. - Prioritizing use cases based on benefit and risk - Evaluating the economic viability of autonomous processes - Organizing the operation, maintenance, and further development of agents - Shaping collaboration between departments and IT in agent deployment Legal and Organizational Frameworks - Data protection and confidentiality in automated data accesses - Classifying responsibility and liability issues in autonomous systems - Co-developing internal guidelines for agent deployment Upon completion, participants will be able to develop autonomous AI workflows from the idea to controlled operation while balancing benefits, quality, and risks.

Upcoming dates

• 2026-10-13T22:00:00.000Z · 2026-12-15T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-27T23:00:00.000Z · 2027-06-27T22:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-11-03T23:00:00.000Z · 2027-07-03T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-11-10T23:00:00.000Z · 2027-01-12T23:00:00.000Z · Frankfurt am Main · Hybrid Learning