Agentic SDLC · Apptension SDLC
Turn AI coding into a governed SDLC.
From scattered assistants to a client-owned harness: standards, gates, and evidence leaders can defend.
For CTOs, VPs of Engineering, and platform leaders accountable for quality, security, and delivery economics.
2 weeks
Implementation
6 months
Managed adoption
100%
Client-owned harness
_> Delivery arc
>> From informal AI usage to a governed harness
Baseline first. Two weeks install the factory. Six months prove economics and stop drift.
Baseline the system
Map ticket to production. Capture flow, review load, quality, and AI spend before any uplift claim.
Install in two weeks
Shared context, skills, verification, PR evidence, and autonomy boundaries on a real repository.
Adopt and measure
Six-month loop: coach, harden, expand proven workflows, report cost per verified outcome.
Trusted by leaders
>>Licences are easy. Change is hard.
_> Most organizations hit the same three-layer gap after the first wave of enthusiasm.
01
Individual acceleration
Developers prompt faster and automate isolated tasks. This layer is visible and feels productive.
02
Team friction
Inconsistent rules, duplicated prompts, larger PRs, uneven review quality, and hidden rework.
03
Management blind spot
No reliable link between spend, adoption, flow, quality, security, and business outcomes.
>>A client-owned Apptension SDLC harness
_> Shared context, skills, verification, PR gates, autonomy boundaries, coaching, and economics. Models change. The harness stays yours.
01
Controlled delegation
Risk-adjusted autonomy with human gates for architecture, security, and release. Not unbounded agent freedom.
02
Evidence over activity
Baseline first. Measure accepted production outcomes, human leverage, quality guardrails, and cost per verified unit.
03
Operating change, not tool training
We install and operate the delivery system around AI coding agents. No prompt workshops without a pilot repository.
Who this is for
_> Outcomes for consulting leaders, CTOs, and founders
CTOs
Turn scattered AI coding into a governed SDLC
Tools are already in use. You need measurable adoption, security controls, and a path that scales across squads.
Outcomes
- Baseline and pilot report in two weeks
- Harness installed in a real repository
- Six-month evidence loop on flow, quality, and economics
Consulting Engineering Leaders
Standardize agentic delivery across programs
Multiple engagements need shared rules, review quality, and auditable handoffs without reinventing each time.
Outcomes
- Reusable skills and verification patterns
- Autonomy boundaries sponsors can defend
- Economics visibility for programme leads
Early-Stage Entrepreneurs
Best when you already have an eng org
If you are still shipping the first product, start with Agentic MVP. Agentic SDLC fits once you have a delivery team and a sponsor.
Outcomes
- Clear fit gate before Implementation spend
- Referral to Agentic MVP when the org is too early
- No workshop theater without a pilot repository
Why this path wins
_> Business outcomes, not tool licenses
Evidence over AI theater
Baselines for flow, quality, control, and economics. Every cycle ends in scale, adjust, or stop.
Client-owned harness
Standards, skills, guardrails, and gates stay in your org. Models change. The delivery system remains.
Operating change that sticks
Two-week install, then managed adoption. Licences alone do not move the org.
>>For teams that need evidence
_> Strongest fit: active software delivery, AI coding already in use, an executive sponsor, and a real repository for the pilot.
Strong fit
- 5–50 engineers for the initial motion (larger orgs start with one team)
- Coding assistants or agents already in use
- Pressure on lead time, review load, quality, or cost
- A CI/testing baseline, or willingness to repair it
- An identifiable pilot repository and backlog
- An accountable engineering sponsor
Clarify or no-go
- One-off prompt workshops or generic AI inspiration
- No sponsor, pilot workflow, or repository access
- Expectation that agents remove architecture and review accountability
- Tool procurement or licence resale as the only approved scope
- Guaranteed headcount reduction with no measurement
>>What the client can prove
_> No vanity metrics. Success is measurable delivery improvement with governance that holds.
Flow
01Lead time, cycle time, review wait, and normalized throughput against agreed baselines.
Quality
02Change failure rate, escaped defects, rework, and rollback frequency beside any throughput claim.
Control
03Autonomy boundaries, critical approvals, policy compliance, and auditable handoffs.
Economics
04AI cost and total cost per verified delivery unit or accepted complexity point.
BUILT WITH APPTENSION SDLC
>>nini.bot: 159 merged PRs in 11 days
_> Production infant-care PWA shipped on the same governed harness we install for client teams.
0
Merged PRs in 11 days
0
Closed issues shipped
0+
Cited knowledge articles
GITHUB PROOF · JUL 31 – AUG 11, 2026
Installable PWA shell with care logging and bottom navigation
QUICK LOG
One-tap timers with recent stats
KNOWLEDGE
Cited, swipeable guidance cards
VISUAL LANGUAGE
In-app illustration system for every screen and guidance card
Delivery outcomes
Peak merge velocity with gates
41 PRs merged on day one, 50 on Aug 1, 2026. Agent branches merged through Vitest, review, and repository rules, not prompt-only output.
Production scope, not a prototype
Rhythm orbit wheel, offline outbox sync, OTP auth, Neon Postgres, PWA install shell, and CMS-seeded knowledge library in one continuous delivery run.
Live product, replicable harness
Installable at nini.bot today. The SDLC loop, context packs, and verification gates behind it are what we deploy for client engineering teams.
SECOND PROOF · PLATFORM MODERNIZATION
>>Teamdeck: 60 PRs of governed legacy change
_> Stage on Temporal, Pulumi, and ECS. Product work never froze. Production cutover still ahead. The story is the gates.
0
PRs in ~5.5 weeks of GitHub history
0h
Median PR open → merge
~0k
Lines of plans, specs, ADRs, runbooks
Machine-readable estate
Bindings table, docs map, and path-scoped rules so agents and humans share one source of truth for branch, CI, and verification.
CI that enforces the process
Twelve workflows and thirteen tested gate scripts. Release-line merges, promotion ancestry, drift detection, and first-pass review on every PR.
Honest mid-program status
Stage is live end to end. Prod cutover is planned, not done. Cost parity waits for measured post-cutover numbers.
>>Assess → Install → Embed → Measure → Improve → Scale
_> A bounded install creates momentum. Managed adoption prevents drift as models, repos, and teams change.
→ Scroll to see all steps
>>Engagement shapes
_> Price against organizational value and complexity. Soft commercial bands validated against scope and risk before engagement.
>>Where the harness lives
_> Where the harness lives across the delivery system.
In-editor
Completion, chat, and inline edits with shared rules.
Terminal
Repository-wide tasks, tools, and tests.
Background
Sandboxed tasks that return branches and PRs.
Workflow
CI, review, issue triage, release, and maintenance.
Organization
Shared skills, controls, metrics, and budgets.
Product portal
Process tables, measurement model, and ROI calculator at /agentic-sdlc.
Also see
Building AI product features for customers rather than installing your delivery system? See Generative AI Solutions.