What is Developing in Agentic AI Systems?

This module introduces the principles of agentic AI and how AI agents can reason, use tools, maintain state, and perform tasks within modern software development environments.

What will I learn about agent architecture?

You will learn how to design agent architectures and integrate AI agents into software development life cycles, including planning, development, testing, deployment, and monitoring.

What are MCP and agent execution environments?

You will explore Model Context Protocol (MCP), tools, and execution environments that allow AI agents to securely interact with external systems, APIs, data sources, and development tools.

Will the training cover multi-agent systems?

Yes. You will learn how multiple specialized AI agents can collaborate, communicate, and be orchestrated to complete complex tasks.

How will agents be evaluated and governed?

The module covers agent memory and state management, evaluation techniques, security guardrails, governance, monitoring, and operational practices for building reliable and responsible agentic AI systems.

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Agentic AI Developer IntermediateFace to Face / Online

Developing in Agentic AI Systems

Name: SBEEH Software Academy
Address: Amman, JO
Telephone: +962-77-984-6564

Developing in Agentic AI Systems is a intermediate Agentic AI Developer course delivered in person and online by SBEEH Software Academy in Amman, Jordan.

1 Day (8 Hours)

Last updated September 21, 2026

This course is designed to build practical skills in developing, deploying, and managing agentic AI systems within GitHub-based software development workflows. The course explores how to integrate AI agents into the software development lifecycle (SDLC), including designing agent architectures, configuring tools and environments, and managing agent memory, state, and execution. Students will learn how to evaluate and optimize agent performance, implement governance and guardrails, and coordinate multi-agent systems to ensure safe, reliable, and efficient outcomes. Through hands-on learning, participants will gain the skills needed to operate, supervise, and govern AI agents in production environments using GitHub as the control plane.