Semantic Engineering

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27. Engagement phases

A continuous SDLC engagement runs in four phases: Advise, Launch, Scale and Optimize. Advise takes two to four weeks, assesses how the team uses AI today and where it meets friction, and produces a scoped roadmap and a platform recommendation. Launch takes about twelve weeks, builds the four-layer graph for the highest-priority product, and puts the validation gate in place on every pull request. Scale extends the methodology across the portfolio over quarters to years, with agents gaining autonomy on routine, repeatable work. Optimize is continuous operation, with governance audits every quarter and regular clean-up of the graphs. In every phase, the client's four custodians own the four layers of the knowledge graph. During an engagement, Accion Labs customizes the platform, sets up the initial graph, and provides managed support in one of three tiers. For the end of an engagement, Accion Labs makes four commitments. The graph can be exported, the agents can be reproduced, the governance framework is documented, and the roles Accion Labs filled can be handed to the client's own staff.

On the site: The Four Phases · Advise · Launch

28. Two engagements

Two anonymized engagements show the methodology at work on continuous SDLC. The first archetype is a live code base of more than two million lines, with five to six scrum teams. Extracting the graph from the code built all four layers in two to three weeks. On the extracted graph, impact analysis replaced three to five days of investigation by senior engineers. The second archetype is a new user-interface workstream that grew in complexity, and started with the design layer of the graph. In its first sprint under Semantic Engineering, the workstream reused 53 percent of its components from the existing design system. In both engagements, structured knowledge was added exactly where the specification alone was not enough.

On the site: The Archetypes · Brownfield Enterprise Modernization · What Changed in How the Team Works

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The full video in six parts and an appendix, one part at a time.

Act 3Knowledge graphs and agents in the AI-driven SDLC, with Breeze.AIPlay →
  1. 10When are specifications not enough?
  2. 11Where is the state of the work kept?
  3. 12Who prepares a change before coding starts?
  4. 13What does impact analysis tell you, and when does it run?
  5. 14How do coding agents use the graph?
  6. 15How is each change checked, and how does the graph stay current?
  7. 16Who decides which work agents do?
  8. 17How do managers see what people and agents are doing?
  9. 18What happens across many products?
  10. 19What about existing systems and technical debt?
  11. 20What changes for the team?
  12. 21What results have teams seen?