We deploy our project utilizing the configuration we prepared earlier and we move into the evolutionary cycle. Phase 7 results in a deployed, observable system, operational runbooks as AI orchestration scripts, continuous compliance, technical debt tracking and management, evolution artifacts and continuously updated principle scorecard. The SDD cycle does not stop at deployment — it continues driving the system’s evolution, with the same discipline, utilizing the same principles and expanding the tool sets. Thanks to our earlier work it is far easier to iterate through over and over as features are added, performance is tuned, security threats evolve, compliance requirements shift, infrastructure is upgraded, and technical debt is addressed.

We audit our project with a skeptical view and an attackers mindset. In Phase 6 we apply independent scrutiny to try to game our system and measure using different test strategies from different perspectives using a different AI Tool set. The results of this phase are a Comprehensive Test Results, a Security Audit Report, a Performance Validation Report, a Documentation Audit, and a Final Principle Scorecard. Failure to meet expectations set earlier mean cycling back through phases 5-7 with the aim of fixing bugs and mitigating security findings before deployment or signing off on acceptable risks to be corrected in a future release.

We take our specifications and other architectural documents from Phase 4 (see Part 6: Architecture & Modular Design) and coded, or have our agents code, with proper boundaries and controls meant to maximize the abilities of AI-centric Software Development utilizing our Specification Driven Development cycle. This results in working module implementations, test suites, monitoring and observability implementations, infrastructure-as-code, continuous scoring data, and an updated principle scorecard.

We apply our artifacts from Phase 3 (see Part 5: Design & Technical Analysis) and committed to the blueprints that we will use to build our project. This results in seven critical artifacts (collections) including Module Specification Documents, API Specifications, Security Architecture, Data Architecture, Documentation Strategy, Governance Model, and an Updated Principle Scorecard.

In Part 6 we have the AI systematically apply our four artifacts and our benchmark framework from Phase 2 (see Part 4: Analysis & Stack Selection) resulting in a Design Direction Document, a Principle Scorecard, and a Technical Feasibility Assessment. Additionally, Phase 3 identifies two critical forward-looking concerns, monitoring and observability hooks, and compliance design constraints.

We explore the second phase of any software project and applied our Core Principles to help understand how we should build out our toolkit for this Phase 2. This phase results in four artifacts, a Business Analysis, a Cost Model, a Stack Decision with Rationale, and an Initial Threat Model. Additionally, Phase 2 establishes a benchmark framework.

In Part 3 we begin our exploration into the first of seven phases of building software with AI. Phase 1 of any project is the gathering of all available information on the subject. Using our Tool Set for this Phase results in a structured Informational Report.

In Part 2 we define the means to act on our Core Principles and define the methodology that puts them into practice. This includes Tool Sets made up of Building Blocks (i.e. Prompts, Agent Instructions, Skill Documents, MCP Servers, Custom AIs, etc) for each Phase of the Specification Driven Development (SDD) process that should have each phase producing structured artifacts which act as input for the following phases.

First things first, we establish the six core principles that act as the compass by which we navigate each aspect of our project — Security, Maintainability, Economics, Operations, Scoring & Metrics, and Correctness Verification. These principles define what you care about.

It is my belief that within the next 12 to 24 month the leading edge of professional software development will look like this: a team of one to five people, capable across the entire process from conception through deployment, orchestrating AI tools that perform specialist-level work across every discipline. These teams will deliver what fifty-person organizations once required — not by working harder, but by working through a fundamentally different interface.