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Master agentic AI development on GitHub — SDLC integration, MCP servers, tool permissions, memory management, multi-agent orchestration, evaluation, and guardrails for production AI agents.
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Detailed notes covering agent architecture, tool use, memory management, evaluation, multi-agent coordination, and guardrails.
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120 minutes
~60 questions
70%
GH-600
GitHub
Defining agent inputs, outputs, and success criteria, Integrating agents into pull request and CI workflows, Separating agent planning phase from execution phase
Selecting and configuring agent tools with minimum required permissions, Configuring MCP servers and the MCP allow list, Configuring GitHub remote MCP servers
Short-term vs. long-term vs. external memory, Scoping memory to task-relevant information, Memory expiration, pruning, and reset rules
Specifying quantitative and qualitative evaluation signals, Automated scanning tools for evaluation signal generation, Root cause categories: reasoning errors, tool misuse, context/environment issues
Pipeline, fan-out/fan-in, orchestrator-worker, and consensus patterns, Configuring agent isolation for parallel execution, Detecting and resolving overlapping changes and contradictory outputs
Classifying actions by risk: reversibility, impact scope, compliance sensitivity, Assigning autonomy levels to maximize delivery speed within compliance, Implementing guardrails and human-in-the-loop workflows
Understand the core concepts of machine learning, from supervised to unsupervised learning, and when to use each approach.
Understand how neural networks work, from perceptrons to deep learning architectures like CNNs and transformers.
Learn how machines understand and process human language, from tokenization to modern LLMs.
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