Developing in Agentic AI Systems
GH-600(1 day)
Developing in Agentic AI Systems
6.000 DKK
(excl. VAT)
Course calendar
Description
AI agents can perform an increasingly large share of a development task, but autonomy places new demands on architecture, control, and documentation. In this course, you learn to integrate agentic AI systems into the software development lifecycle (SDLC), so agents work from clear goals, scoped permissions, and controllable processes. GitHub serves as the shared point of record and control for code, workflows, reviews, and the artifacts that document the agents' work.
You design agent architectures in which inputs, outputs, and success criteria are precisely defined. You learn to separate planning from execution, validate the agent's plans, and choose an appropriate level of autonomy. This also covers configuring tools and execution environments, managing tool permissions, and integrating MCP servers and registries. The goal is for the agent to have access to the resources it needs without broader permissions than the task requires.
The course goes beyond configuring an agent. You learn to manage memory, state, and long-running execution, so work can resume without repeating steps or deviating from earlier decisions. You also work with evaluation and failure analysis using logs, traces, scans, plans, and workflow artifacts, among other sources. This gives you a basis for finding the causes of failures and adjusting instructions, workflows, constraints, and the use of memory.
When multiple agents work in parallel, their tasks, context, and changes must be coordinated. You learn to apply orchestration patterns, isolate agent execution, and handle conflicts such as overlapping code changes, duplicated work, and conflicting outputs. You also get a firm grasp of guardrails, least-privilege access, approval mechanisms, and audit-ready artifacts. The course is for you if you already work with GitHub-based software development and are responsible for developing, integrating, or providing technical oversight of AI agents in production environments.
After the course, you can:
- Design agent architectures with clear inputs, outputs, and success criteria
- Configure tools, MCP servers, permissions, and execution environments
- Manage agent memory, state, and long-running execution
- Evaluate agent outputs and analyze failures using scans and artifacts
- Adjust instructions, workflows, and constraints based on evaluations
- Coordinate multiple agents and resolve conflicts between their results
- Implement guardrails, least-privilege access, and relevant approvals
- Use GitHub to maintain control over agents' work throughout the SDLC
Related exam
Prerequisites
To get the most out of the course, we recommend experience or knowledge equivalent to:
Module overview
Learn how to design, deploy, and manage agentic AI systems within the software development lifecycle by integrating, orchestrating, and governing autonomous agents using GitHub to ensure safe, reliable, and scalable delivery.
Lessons:
- Foundations of Agentic AI in GitHub
- Designing Agent Architecture and SDLC Integration
- Tooling, MCP, and Agent Execution Environments
This is part two of two part learning series to support our new certification, Developing in Agentic AI Systems. In this learning path you will learn how to design, deploy, and manage agentic AI systems within the software development lifecycle by integrating, orchestrating, and governing autonomous agents using GitHub to ensure safe, reliable, and scalable delivery.
Lessons:
- Multi-Agent systems and orchestration
- Memory, State, and Evaluation
- Governance, guardrails, and operations
Free course advice
We care a lot about helping you find the course path that creates the most value for you and your workplace. Get in touch with our course advisors — they're ready to help you, free of charge and with no obligation.
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