Course program
Four core courses that build up: each one rests on the ones before it. The first two are general — development with AI, and the context the agent sees at the moment it answers; the third and fourth show the same discipline inside a regulated ecosystem and inside a closed perimeter. Beside the ladder sit two optional courses: the fifth, for a deeper understanding of approaches to organizing agentic software development, and the sixth, on agent memory management, recommended after the second. Every lesson ends with three tests and explanations; the courses themselves are taken in the personal cabinet.
Course 1 · AI-driven PDLC
Why you need this course. You already write code with an agent, but the result depends on luck: sometimes it works the first time, sometimes much of it — or all of it — has to be rewritten. The course turns that work into a result you can repeat.
What you will learn. Framing a task and assembling its context, the specification as the first artifact, basic checking of the result, the analysis of risks and guard rails. Then the concept of the lifecycle: from Waterfall to working from intent, the roles of an AI team, agentic patterns. The third chapter is a workshop on two instruments, Claude Code and Cursor: subagents, skills, MCP, the links and the boundaries of the agent’s loop.
What you gain. You stop treating the agent as a clever autocomplete and start seeing exactly where the work needs your gate.
How it is built. Three chapters, 19 lessons, 1 practical assignment and 57 test items. Prerequisites — development experience, Git and the command line.
Course 2 · Context management for AI agents in team development
Why you need this course. The agent answers not your question but everything the context window holds. The course is about what fills that window and who is answerable for filling it.
What you will learn. The five layers of context — permanent, project, task, session and tool: each with its own lifetime, its own owner and its own way of reaching the window. Why the permanent layer is not remembered for good but weakens as the session goes on, and why the window is a capacity the layers compete for. Then the development team’s context system: rules, README files and the decision log, the artifact standard and its owner, the normative layer, implicit sources, quality metrics and the symptoms of degradation. The third chapter is work on a task: planning mode, working from a specification and folding the context, review of the context package, test design, research and the coordinator’s eleven-stage workflow.
What you gain. You stop fixing the agent’s answers and start fixing its input.
How it is built. Three chapters, 25 lessons, 25 practical assignments and 75 test items. Prerequisite — the first course.
Course 3 · Executable knowledge as the subject of development
Why you need this course. In a regulated environment the subject of development is not code but knowledge, which is turned into executable code by stated rules. Responsibility for the result is different here, and the habits of an ordinary project carry over at too high a price.
What you will learn. Development in a regulated environment: the normative corpus and global conventions, repositories and the boundaries of authority, developing components with agents, the platform MCP servers, gathering knowledge. Extending an existing domain: a program in the ASL language, the authorization loop, domain research and the research document, the provenance of values, calibrating thresholds, a new consumption surface and the analysis of a real divergence. Standing up a new domain: the boundary between the invariant core and the changeable dialect, a seven-step route and a list of prerequisites that no amount of declaration-writing will create.
What you gain. You understand where an authoritative value comes from, and why the question “who decided this” must have an answer.
How it is built. Three chapters, 20 lessons, 3 practical assignments and 60 test items. Prerequisites — the first two courses.
Course 4 · The HealthOS development environment
Why you need this course. The preceding courses teach you to work with agents that have you: your terminal, your browser, your prompt at any moment. Here you have only a web interface in a personal cabinet, and the fleet of agents has your package of assignments.
What you will learn. Lessons 1–20 form the general program: environment configuration, the foundations of a run, boundaries and traces. Lessons 21–30 deepen the medical specialization: protocol, search, deposit, publication screening, extraction, critical appraisal, synthesis, a proposed graph change, authorization and stewardship of a continuously updated evidence base.
How it is built. Four chapters, 30 lessons, 15 practical assignments and 90 test items. Prerequisites — the three preceding courses. The course describes the target state of the environment and is published together with it.
Course 5 · optional · Comparative analysis of agentic development methodologies
Why you need this course. Several methods call themselves agentic development and use the same words — “specification”, “intent”, “engineering” — while meaning different things by them. Merging methods by a shared word is a false economy: a change in the very subject of development is easily mistaken for one more maturity level. The course compares seven approaches and does not fuse them.
What you will learn. A comparison frame and a vocabulary of such homonyms. Vera Vishnyakova’s maturity pyramid of agentic engineering and the code-path methodologies: Software Engineering 3.0, Vibe and Deep Coding, industrial vibe coding, the AI-driven development life cycle. Where executable knowledge engineering stands beside the pyramid, and where the closed execution contour sits on the overall map. Trust engineering and the activity operating system, illustrated by the intensive care unit — together with the regulatory frame for medical software and the EU AI Act.
What it gives you. You can put one diagnostic question to any approach — where is it written why this value is here: in the source code, or in separated, authorized knowledge — and from the answer tell what a method really changes and what it merely renames.
How it is built. Four chapters, 17 lessons, 0 practical assignments and 51 test items. Prerequisite — the course “AI-driven PDLC”. The course is aimed at the advanced part of the audience: it is a map of the field, not a programming textbook.
Course 6 · optional · Agent memory management
Why you need this course. The context of a window lives for one model call. An agent’s memory is everything that outlives the session and enters the window again — rules, logs, summaries, reference books — or is built into the model’s weights. The course examines what kinds of such memory exist, who has the right to write to it, how it fails, and how to verify that it actually works rather than being merely declared.
What you will learn. The first chapter introduces the language: the four memory usage classes, the three forms of its storage and their intersection. The second applies it to work: who writes to long-term memory, how the window behaves, how memory fails, how development and operation differ, how to measure memory and for whom it should be built at all. The third assembles all of this into two complete scenarios — agentic development and the ECG pipeline.
What it gives you. You can tell memory that works from memory that is merely declared, and you know who has the right to write to it.
How it is built. Three chapters, 13 lessons, 16 practical assignments and 39 test items. An optional course; its chapters sit at levels 4.0, 5.0 and 6.0. It is recommended after the course “Context management for AI agents in team development” — the concepts of context and the context window are taken from there; this is a recommendation, not a condition of access.