Avanzado
Vibe Coding: Multi-Agent Engineering
Stop working with one AI assistant and start leading a team of them. Written for founders who have shipped a production product and now want to build at the pace of a real engineering team. You will learn to create specialized AI agents, a product manager, an architect, a developer, a reviewer, a security specialist, a DevOps engineer, give each a clear role, and coordinate them the way a modern software team ships. You direct the team. The agents do the work. By the end you run an entire software project with a full AI engineering team instead of a single assistant.
12 lecciones5.3h práctico11 idiomas
Empieza a aprender gratisVibe Coding: Multi-Agent Engineering curso12 lecciones, en orden
- 120 min
Thinking Like an Engineering Leader
An engineering workflow is the repeatable path work takes: plan, build, review, shipA team splits ownership so no single owner holds everythingA leader invests in a plan before any code existsA non-technical leader makes technical calls by weighing the tradeoffs the AI lays out - 220 min
Creating Specialized AI Agents
An AI agent is the same AI given one focused role, its own instructions, and its own slice of the workA team of specialists beats one generalist doing everythingThe seven roles of an AI engineering team and what each one ownsA role instruction is the short system-style prompt that defines an agent's job - 320 min
Giving Every Agent a Role
Responsibilities: naming what an agent owns and, just as important, what it does not ownContext: the goal, constraints, and prior agents' decisions that must travel with a taskRole instruction: the standing description that makes an agent behave the same way every timeHandoffs: passing work between agents without losing informationTurning a vague role into a precise one - 430 min
Managing AI Collaboration
Shared context: the one common understanding all agents work from (the goal, the plan, what is done)Planning: agreeing the approach before anyone starts buildingCommunication: relaying one agent's output to the next as clean inputReviews: one agent checking another's work before it counts as doneIteration: looping build, review, refine instead of one-shotting a featureDriving one small feature through planner, developer, and reviewer as the coordinator - 530 min
AI Code Reviews
The review process: how a change goes from proposed to reviewed to merged, and why nothing merges unreviewedWhy the reviewer must be a different agent from the author, so it reads with fresh eyesQuality checks: whether the work meets the requirement, is clear, and has nothing missing or brokenRefactoring: improving the structure of working code without changing what it doesSecurity reviews: flagging unsafe patterns such as trusting user input without checking itActing on findings as the leader, deciding what to fix without reading the code line by line - 620 min
AI Project Management
The leader runs the project, not just the individual agentsA sprint is a short fixed period with a committed set of work; sprint planning is choosing that setTask management breaks a plan into tracked tasks with one owner and a checkable doneDocumentation is a written record of decisions and how things work so the team stays alignedPriorities decide what matters most now and are revisited as things changeUsing a PM agent to draft a sprint plan, a task list, and priorities - 730 min
AI DevOps
Handing the delivery and running of the product to a DevOps agent so shipping is automatic and repeatableCI/CD as an automatic pipeline that tests and ships every changeDeployments as releasing new versions safely and repeatably, including the ability to roll backInfrastructure described as config the team can review, not servers set up by handMonitoring where the DevOps agent wires up health checks and alerts so problems surface fastThe founder directs the DevOps agent and reviews what it produces - 830 min
AI Security Teams
A dedicated security agent whose only job is to find ways the product could be attackedA security review as a structured pass that reports weaknesses ranked by severityActing on findings by severity, worst first, instead of fixing everything at onceA vulnerability as a specific weakness an attacker could useA vulnerability scan as systematically hunting for known weaknesses, including in dependenciesA threat analysis as thinking like an attacker: who would target this, what they want, and where they would push+1 - 930 min
AI Operations
The operations agent as a focused role that keeps the live product healthyIncident response as the calm, ordered way to detect, diagnose, fix, and communicate when something breaks in productionMonitoring as watching signals so incidents are caught early, building on the monitoring you set up in course 2Production support as handling the steady stream of real-world issues and user reportsMaintenance as the ongoing upkeep that prevents incidents: updates, cleanup, and keeping things healthyThe blameless postmortem as a write-up after an incident of what happened and how to prevent it - 1020 min
Building Your Own AI Workflow
An AI software factory is a repeatable path an idea travels from head to shippedWorkflow design is fixing the sequence of agent roles every feature passes through, in the same order each timeAutomation is handing repeated steps to agents or scripts so the leader stops doing them by handDocumentation is writing the workflow itself down so it is repeatable and shareable, not held in memoryProductivity is how fast good work flows through the line, improved by removing friction rather than by cutting reviewsAn end-to-end example: idea, PM, architect, build, review, security, deploy - 1120 min
Scaling AI Teams
A large project outgrows a single agent's context and must be split into parts with clear boundariesA boundary defines what a part owns and the one well-defined way other parts interact with itTeam coordination keeps many agents on the same goal without conflicting or duplicating workA shared source of truth holds docs and decisions outside any single conversation so context does not vanishLong-term development means onboarding new agents, guarding against drift, and keeping the plan currentCoordination and shared knowledge, not raw building speed, are the real limits at scale - 1245 min
Becoming an AI Engineering Leader
Assembling the full AI engineering team and giving each agent its roleThe seven roles: product manager, architect, developer, reviewer, security, DevOps, and operationsRunning a project through the whole workflow: plan, build, review, secure, ship, operateHandoffs that carry decisions and context, not just the next instructionLeading the team rather than doing the work, and holding the quality standardCarrying the whole approach into the founder's own real projects
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