MACH-1

Adaptive Delivery Lifecycle

Architecture as machine-readable context. AI agents that know your constraints. Quality gates backed by formal evidence.

The Problem

Enterprise software delivery faces a persistent speed-accuracy trade-off. Traditional SDLC optimizes for safety through long planning cycles, extensive code reviews, and manual validation. This creates predictability but sacrifices speed.

The constraint is not engineering — it is knowledge transfer. Each developer must understand the system's architecture, integration points, business rules, existing patterns, and ADRs. When this knowledge is scattered, code review becomes a bottleneck. When it's concentrated, key developers become single points of failure. Neither scales.

The Economic Thesis

The initiative is self-funded by savings on non-hiring. With 200 developers and +30% annual growth in delivery volume:

ScenarioHires per yearAnnual cost
Without ADLC +60 developers $900K–$1.5M
With ADLC (1.5x multiplier) +20 developers Savings: $600K–$1M/year

Cost of ADLC: near zero (MIT license tools, existing CI/CD, minimal API cost). ROI: effectively infinite.

Hidden bonuses: fewer architectural errors, faster onboarding (days not weeks), knowledge preserved after developer departures, compliance by default.

What is ADLC?

ADLC is not a replacement for SDLC. It is SDLC amplified by machine-readable architecture and AI agents.

AaC is not documentation. It is a context layer for AI. When your architecture is machine-readable, agents stop generating generic code and start generating code that is correct for your organization by default.
MetricSDLCADLC (target)Advantage
Days task → production5–101–25–10x faster
Devs per new system4–62–333% smaller teams
Onboarding (new dev)3–4 weeks3–5 days10x faster
Arch issues per MR30–40%<5%Fewer review rounds

The Four Layers: M-A-C-H

M — Managed: Governance
Evidence-based gates. Every phase transition requires proof: architectural compliance, fitness metrics, compliance rules, downstream approval. Managers review evidence, not hunches.
A — Acceleration: ADLC Process
8-phase Adaptive Delivery Lifecycle where each phase has defined agents, gate criteria, and workflows. Architect → Design → Develop → Review → Test → Deploy → Monitor → Retrospect.
C — Corporate: Architecture as Code
YAML-based AaC: systems, domains, ADRs, catalog. When a CodeGen Agent reads your system spec and domain rules, it generates code that's already correct for your organization.
H — Infrastructure: Implementation
MCP servers expose the framework as APIs: LikeC4 MCP (C4 model), YAML MCP (corporate layer), GitLab MCP (CI/CD). Fitness functions in CI/CD enforce constraints every commit.

The Eight Phases

  1. 01 — Architect Describe the change. Architecture Agent validates feasibility against the existing landscape. Output: C4 model sketch, identified systems.
  2. 02 — Design Generate API contracts. Design Agent reads domain rules and produces OpenAPI specs, error codes, idempotency headers, event schemas.
  3. 03 — Develop CodeGen Agent pulls full architectural context and generates controllers, services, repositories, tests, and migrations — all architecture-aware.
  4. 04 — Review Review Agent checks architecture compliance, computes impact, validates compliance. Tech Lead reviews evidence, not raw diffs.
  5. 05 — Test Test Agent verifies coverage, runs contract tests, checks NFRs: latency, memory, query performance.
  6. 06 — Deploy Deploy Agent updates C4 model, notifies downstream stakeholders, triggers CI/CD, verifies health checks.
  7. 07 — Monitor Monitor Agent compares declared architecture vs actual runtime, detects drift, measures SLA compliance, generates staleness reports.
  8. 08 — Retrospect Team updates domain specs, writes ADRs, feeds patterns back to agent guidance. No gate — learning and improvement.

The Six Core Agents

AgentPhasesKey Skills
Architecture Agent Architect, Monitor Analyze landscape, detect drift, suggest placement
Design Agent Design Generate API contracts, check compatibility
CodeGen Agent Develop Generate code, apply patterns, write tests, create migrations
Review Agent Review Analyze diff, check fitness, compute impact, check compliance
Deploy Agent Deploy Update C4 model, notify stakeholders, enforce governance
Monitor Agent Monitor Detect drift, measure SLA, generate staleness reports

The Mach Number Scale

LevelStateWhat it means
Mach 0.3 Subsonic Architecture as Code exists. Agents not connected. Current state.
Mach 0.5 Transonic MCP live. Agents read the model. Generation is manual. Target: Q2 2026.
Mach 0.7 Approaching ADLC cycle running. Gates partially automated. Target: Q3 2026.
Mach 1.0 Barrier broken Full cycle end-to-end. Task → production in 1–2 days. Target: Q4 2026.
Mach 2.0+ Supersonic Proactive agents, predictive analysis, federation.

What's Next

  1. Assess your Mach number — check which level your organization is at and what's blocking the next step.
    Open Maturity Model →
  2. Follow the Roadmap — milestones from Mach 0.3 to Mach 1.0 with concrete tasks and blockers.
    Open Roadmap →
  3. Read the Glossary — definitions for ADLC, AaC, MCP, quality gates, evidence, and more.
    Open Glossary →