AI SDLC: Automating the Grind

The SDLC Is a GRIND

After enough years in software, I stopped pretending the Software Development Lifecycle was a clean, rational process.

Instead, I started calling it the GRIND:

A ritual cycle of optimism, ego, compromise, suffering, and damage control.

If you want the classic layman’s breakdown first, read the older Software Methodologies draft. If you want the security model for the same problem space, read Threat Modeling AI as an Engineering Coprocessor. If you want the architectural control argument underneath both, read Software Architecture for Independence.

This post is partly satire, but the operating assumptions come from spending a long time inside enterprise, product, and contract delivery environments where context loss, weak handoffs, and repeated decision churn are normal failure modes.

For me, it looks like this:


1) Requirements — RE: Acquire Mints

Requirements exist because stakeholders don’t want your bad breath anywhere near their problem space.

If you don’t proactively acquire mints (clarity, context, shared language), requirements will:

  • arrive vague
  • be delegated
  • be re-delegated
  • and eventually be blamed on you

This phase ends with someone saying:

“That’s not what we meant.”

It always is.


2) Analysis & Design — Anal-ysis and De-Sign

This is the part where we decide how the system will work — and discover just how much ego can fit in a single meeting.

Design reviews are:

  • deeply political
  • emotionally exhausting
  • 30% architecture
  • 70% removing political signs from the lawn so collaboration can even happen

Everyone wants alignment. No one agrees on what that word means.


3) Implementation — Imp-Lamentation

Now the little development imps get to work.

They:

  • implement decisions they didn’t fully agree with
  • uncover edge cases no one designed for
  • lament loudly about the earlier analysis phase

But it’s too late.

The tickets are assigned. The sprint has started. The political signs are gone — but the resentment remains.


4) Testing — Test-icles

Testing is cold. Testing is precise. Testing involves a tiny hammer.

Its job is to:

  • remove unjustified confidence
  • punish assumptions
  • and separate “it compiles” from “it survives contact with reality”

This is where systems lose their bravado.


5) Deployment & Maintenance — Damage Control

This is the phase where:

  • time runs out
  • weekends evaporate
  • shortcuts quietly appear
  • and everyone promises to “clean it up next sprint”

You ship. You patch. You monitor. You recover just enough energy to do it all again.


The Methodology Doesn’t Matter

No matter the methodology you swear by —

  1. user-obsessed Agile 2-week sprints
  2. product-focused 3-month iterative cycles
  3. Kanban, Lean, Scrum, etc. — it always comes down to the SDLC.

Fixing a bug. Writing a feature. Collaborating across teams.

Same grind. Same loop. Same lifecycle.


Why I’m Still Doing This

Do not get me wrong - I respect process and rely upon it. This cycle isn’t broken because engineers are incompetent. It’s broken because the SDLC has no memory.

Decisions evaporate. Context gets lost. Risk is rediscovered instead of managed.

That is part of why I increasingly see AI tooling as memory infrastructure rather than as a replacement for engineering judgment. If you want the security version of that argument, read Threat Modeling AI as an Engineering Coprocessor. If you want the organizational version of how these failures accumulate in real systems, read Legacy System Insights.

We laugh about it because if we didn’t, we’d scream.


Why Anthesis Exists

Anthesis comes from living inside this grind for a long time and deciding that maybe — just maybe — the lifecycle itself could be smarter.

Not magical. Not autonomous. Just aware.

Aware of:

  • why decisions were made
  • where risk was accepted
  • who approved what
  • and when it’s time to stop and ask a human

Anthesis doesn’t remove the grind.

It tries to remember it, so we don’t have to relearn the same lessons every sprint.


Author Background

  • LinkedIn: Ryan Jennings for the broader professional context behind the process and delivery observations in this piece.