Course Outline
Session 1: Reproducible methodology and tool map (2h)
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Module 1: From personal flow to reproducible methodology
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Explicit team contracts: Sustaining SDD flow at a coordinated collective scale.
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Specification lifecycle: Refinement, validation, and traceability under high pressure.
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Reproducibility guarantee with AI: Structural consistency (same input → same output) in multi-agent environments.
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Module 2: Multi-tool operational map
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Local context control: Configuration of environments and guide files (
CLAUDE.mdvs.copilot-instructions.mdvs.OpenCode). -
Instructional guidance structure: Equivalencies and discrepancies between Rules, AI Contexts, system instructions, and memory.
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Multi-agent synchronization: Maintaining a single functional source of truth with heterogeneous tools.
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Session 2: Artifact precision and calibration (2h)
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Module 3: Artifact precision: The differences that matter
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Specification boundaries: Technical differences and agent behavior regarding Functional Specs vs. Technical Specs.
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Task granularity and sizing: Impact of massive tasks (code breakage) vs. minuscule tasks (loss of context).
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Comparative response analysis: Course adjustments in Copilot, Claude, and OpenCode facing the same specification.
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Production antipatterns: Live demonstration of recurrent failures and their impact on generated code.
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Guided Practice: Work on real specifications (30 min)
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Active analysis and real-time diagnosis of specifications provided by the team to detect ambiguities.
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Session 3: Full lifecycle and change management (2h)
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Module 4: Refinement, validation, and mid-flight changes
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Iterative refinement techniques: Transitioning from diffuse needs to validated specifications without writing base code.
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Operational viability checklist before delegating tasks to an agent.
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Mid-flight modifications: Managing technical scope variations halfway through the cycle without corrupting previous tasks.
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Structural deviation control: Procedures to regain control when the agent diverges from the objective.
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Module 5: Multi-agent patterns and MCPs in a corporate environment
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Limits of the agentized paradigm: Formal criteria to avoid excessive structural complexity without value return.
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Advanced development patterns: Hierarchical orchestration, parallel agents, and human control gates.
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MCP Ecosystem (Model Context Protocol): Corporate implementation with Filesystem, Git, and Jira (Direct demonstration).
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Session 4: Quality, security, and real case resolution (2h)
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Module 6: Agentized TDD and quality pipeline
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Specification as a test contract: Transforming acceptance criteria into automated tests by AI.
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Automation in PR and CI/CD: What to automate and which decisions to retain under strict human review.
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Continuous integration differences: Coupling methodologies for GitHub Copilot, Claude, and OpenCode.
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Module 7: Security, intellectual property, and real limits
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Data security policies: Safeguarding sensitive information and the advantages of corporate plans (Enterprise/Team).
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Intellectual property: Regulatory scenario, authorship, and responsibilities in AI-assisted development.
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Organizational guardrails: Ethical and internal frameworks to enhance delivery without jeopardizing technological assets.
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Practical Resolution: Real use cases from the team (30 min)
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Solution in the team's code: Direct work on the repository and problems provided by the students.
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Requirements
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Daily, established practice in Software-Driven Development (SDD - Software-Driven Development).
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Prior experience and active use in your workflow of at least one of the following tools: GitHub Copilot, Claude, or OpenCode.
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Solid knowledge in software architecture, specification management (functional and technical), and continuous integration flows (CI/CD).
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Essential requirement: Willingness to provide real use cases and code examples from the team before the start of the training.
Target Audience:
- A consolidated team of 20 professionals with daily SDD practice (Senior Developers, Tech Leads, and Software Architects who already use AI in their day-to-day work and seek to optimize, standardize, and scale their workflow).
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Essential requirement: Willingness to provide the instructor with real use cases and code examples from the team before the start of the training for a prior expectation management meeting.
Testimonials (4)
What appears most useful is the fact that both theory and practical demonstrations are shared, which helps ground the knowledge and reveals its real-world utility.
Noelia Iglesias Martinez - Aplicaciones y Tratamientos de Sistemas S.A (knowmad mood)
Course - IA Generativa y SDD Avanzado
Machine Translated
the MCPs
Gustavo Lopez Garcia - Aplicaciones y Tratamientos de Sistemas S.A (knowmad mood)
Course - IA Generativa y SDD Avanzado
Machine Translated
the automation flow of the agents
Antonio Perez Caballero - Aplicaciones y Tratamientos de Sistemas S.A (knowmad mood)
Course - IA Generativa y SDD Avanzado
Machine Translated
AGENTS.MD concepts
Eduardo Palomares Medina - Aplicaciones y Tratamientos de Sistemas S.A (knowmad mood)
Course - IA Generativa y SDD Avanzado
Machine Translated
