{"product_id":"developing-in-agentic-ai-systems-gh-600","title":"Developing in Agentic AI Systems (GH-600)","description":"\u003cdiv\u003e\n\u003cp\u003eThis course teaches learners how to develop, deploy, and manage agentic AI systems within GitHub-based software development workflows. Participants learn how AI agents can be integrated into the software development lifecycle, including agent architecture design, tool and environment configuration, memory and state management, execution, evaluation, and governance.\u003c\/p\u003e\r\n\u003cp\u003eLearners explore how to operate agent workflows inside the SDLC, supervise autonomous behavior using GitHub controls, evaluate and tune agent outputs, configure custom agents, and coordinate multi-agent execution safely.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eDeveloping in Agentic AI Systems (GH-600) Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuilds practical skills for developing and managing AI agents in GitHub workflows\u003c\/li\u003e\n\u003cli\u003eHelps development teams integrate agents safely into the SDLC\u003c\/li\u003e\n\u003cli\u003eCovers agent architecture, tooling, MCP servers, execution environments, memory, state, and evaluation\u003c\/li\u003e\n\u003cli\u003eSupports responsible use of AI agents through governance, guardrails, and human oversight\u003c\/li\u003e\n\u003cli\u003eHelps teams supervise autonomous agent behavior while maintaining traceability and control\u003c\/li\u003e\n\u003cli\u003ePrepares learners for the GitHub Certified: Agentic AI Developer certification\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eLearners should have experience with the software development lifecycle, GitHub workflows and controls, code quality practices, security practices, and code review processes. They should also have experience with coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eExam Information\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003ca href=\"https:\/\/learn.microsoft.com\/en-us\/credentials\/certifications\/agentic-ai-developer\/?practice-assessment-type=certification\"\u003eGitHub Certified: Agentic AI Developer\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAgentic AI Development in GitHub Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eFoundations of Agentic AI in GitHub\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners explore how AI coding agents are changing software development by planning, acting, and improving within GitHub workflows.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eDefine agentic AI in the SDLC\u003c\/li\u003e\n\u003cli\u003eExplain the agent lifecycle: plan, act, evaluate\u003c\/li\u003e\n\u003cli\u003eDescribe GitHub as the system of record and control plane\u003c\/li\u003e\n\u003cli\u003eIdentify responsibilities, risks, anti-patterns, and traceability needs\u003c\/li\u003e\n\u003cli\u003eApply the contributor model to agent-generated work\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eAgent Architecture and SDLC Integration\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners design agentic systems that use GitHub workflows to build software safely, reliably, and with appropriate controls.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eMap agent responsibilities to the SDLC\u003c\/li\u003e\n\u003cli\u003eDefine inputs, outputs, and success criteria\u003c\/li\u003e\n\u003cli\u003eSeparate planning, reasoning, and execution\u003c\/li\u003e\n\u003cli\u003eApply pull request governance using templates, checks, CODEOWNERS, rules, and environment gates\u003c\/li\u003e\n\u003cli\u003eBuild reliable workflows with outputs, contexts, triggers, and cross-job handoffs\u003c\/li\u003e\n\u003cli\u003eControl and operate agents using observability, tools, MCP, secrets, hooks, and reliability practices\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eTooling, MCP, and Agent Execution Environments\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners configure the tools, execution environments, and boundaries agents need to perform tasks safely within GitHub workflows.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eExplain how agents interact with GitHub APIs and workflows\u003c\/li\u003e\n\u003cli\u003eUse Model Context Protocol servers, registries, and allow lists\u003c\/li\u003e\n\u003cli\u003eDefine execution context and boundaries\u003c\/li\u003e\n\u003cli\u003eApply agent execution limits and protections\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eMulti-Agent Systems and Orchestration\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners design reliable multi-agent systems in GitHub using observable workflows, coordinated artifacts, and safe recovery mechanisms.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eDesign multi-agent workflows for coordinated development tasks\u003c\/li\u003e\n\u003cli\u003eDefine agent roles, responsibilities, and handoff points\u003c\/li\u003e\n\u003cli\u003eCoordinate artifacts across agents and workflows\u003c\/li\u003e\n\u003cli\u003eMonitor multi-agent activity for reliability and traceability\u003c\/li\u003e\n\u003cli\u003eApply recovery mechanisms when agent workflows fail or require intervention\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eMemory, State, and Evaluation\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners manage agent memory and state, persist progress across environments, and evaluate agent behavior using clear success signals.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eManage agent memory and state across tasks\u003c\/li\u003e\n\u003cli\u003ePersist progress across development environments\u003c\/li\u003e\n\u003cli\u003eDefine evaluation criteria for agent outputs\u003c\/li\u003e\n\u003cli\u003eUse scans, artifacts, and signals to assess quality\u003c\/li\u003e\n\u003cli\u003eTune agent behavior based on evaluation results and error analysis\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003eGovernance, Guardrails, and Operations\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003eIn this section, learners design secure and compliant agent governance using GitHub-native controls, human approvals, least-privilege access, and operational safeguards.\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eImplement governance and guardrails for agentic workflows\u003c\/li\u003e\n\u003cli\u003eConfigure human-in-the-loop approvals\u003c\/li\u003e\n\u003cli\u003eApply least-privilege access controls\u003c\/li\u003e\n\u003cli\u003eImprove accountability through logging, review, and traceability\u003c\/li\u003e\n\u003cli\u003eUse operational safeguards to support reliability, recovery, and safe production use\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Microsoft","offers":[{"title":"26AD14US \/ 2026-10-20T09:00:00 \/ Online","offer_id":42918732267600,"sku":"US-8784-IL","price":488.0,"currency_code":"USD","in_stock":true},{"title":"26CC50US \/ 2026-12-01T09:00:00 \/ Online","offer_id":42918732333136,"sku":"US-8784-IL","price":488.0,"currency_code":"USD","in_stock":true},{"title":"271D05US \/ 2027-01-20T09:00:00 \/ Online","offer_id":42918732365904,"sku":"US-8784-IL","price":488.0,"currency_code":"USD","in_stock":true},{"title":"273C51US \/ 2027-03-02T09:00:00 \/ 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