Stage · 01
Planning & Decomposition
Break tickets to the smallest unit and map the blast radius before a line is written.
⊕ AI · ◇ Human AI runs impact & dependency analysis. A human owns scope and priority.
Product · Aegis AI Enterprise Production Suite / Patent-pending
Ship twice a week.
Zero critical defects.
Aegis is the production process we run every single day for a 200+ location enterprise. It is not a black box and it is not magic. It is a disciplined application of a centuries-old engineering principle — divide and conquer — with AI placed at every gate where humans miss things: impact analysis, code review, QC, and live monitoring.
Cadence
2×/week releases
Critical defects
0 in 12 months
Proven at
200+ location enterprise
02 / The problem
Faster releases usually mean more risk.
Most teams accept a trade-off: ship slowly and safely, or ship often and break things. That trade-off exists because large, infrequent releases bundle dozens of changes together — and when something fails, no one can tell which change caused it.
The research is blunt about this. DORA's own guidance is to "make each change as small as possible," and that any batch of code taking longer than a week is too big to reason about safely (DORA, Working in Small Batches). Large pull requests don't just slow reviews down — they hide defects. Detection collapses from 87% on changes under 100 lines to just 28% on changes over 1,000 lines (analysis of 212,687 PRs across 82 projects).
So the bottleneck was never speed. It was batch size and blind spots — and no single team owning the system end to end. Aegis is built to remove both.
03 / The principle
Divide and conquer, applied to releases.
Aegis takes a principle every engineer already trusts — break a hard problem into the smallest pieces you can independently solve — and turns it into an operating discipline for shipping software.
The operating rule
If a change can be split, it must be split. Every ticket is broken down until its blast radius is small enough that a human and an AI can both fully understand it.
This is the move that makes everything else possible. A small change is a knowable change. You can analyze exactly what it touches, review it properly, test it completely, and — if it ever misbehaves in production — point to it instantly. Speed and safety stop being a trade-off, because the unit of work is small enough to be both fast and safe.
01
Decompose. Tickets are broken into the smallest independently shippable units. Decades of peer-reviewed data show small batches are where defects are actually caught.
02
Assess impact first. Before a line is written, the team maps the blast radius — every system, dependency, and team the change can touch — so everyone knows the impact before, not after.
03
Put AI on every gate. AI joins impact analysis, code review, QC, and monitoring — not to replace engineers, but to catch what tired humans miss on the hundredth review of the week.
04 / How Aegis works
Four stages.
One continuous loop.
A loop, not a line. Production data from the last release feeds the planning of the next one. At each stage, a human owns the decision and an AI widens the net.
Stage · 02
Pre-Release Quality
Code review, regression prevention, and test-coverage insight before anything ships.
⊕ AI · ◇ Human AI reviews every diff and runs a QC pass. A human makes the go / no-go call.
Stage · 03
Production Monitoring
Anomaly detection, performance alerts, and root-cause insight on live systems after release.
⊕ AI · ◇ Human AI flags anomalies and runs RCA on live signals. A human triages and responds.
Stage · 04
Continuous Optimization
Experience, accessibility, SEO, and testing feedback turned into the next release plan.
⊕ AI · ◇ Human AI synthesizes signals into the next plan. A human prioritizes the improvements.
↺ Stage 4 feeds back into Stage 1 — production data drives the next plan ● Twice-weekly cadence, sustained
05 / The release engine
An AI checkpoint and a human on every gate.
Every stage pairs an AI checkpoint (⊕) with a human decision (◇). The dashed return path is the loop — last release's production data informs the next plan.
Every stage pairs an AI checkpoint (⊕) with a human decision (◇). Research note: AI generates more review suggestions than humans, but humans still adopt their own at a far higher rate — which is why Aegis keeps a person on every gate (Human-AI Synergy in Agentic Code Review).
06 / A real release week
What twice-weekly actually looks like.
No heroics, no weekend deploys. Two predictable release windows a week, each carrying small, fully-understood changes through the same four gates.
Each release window carries only what passed every gate. Because units are small, a window is measured in days — not the multi-week batches DORA flags as too risky to reason about (DORA).
07 / Why zero defects is credible
Four nets. Nothing slips through.
"Zero critical defects" only sounds like marketing until you see where issues actually get caught. Small changes plus layered gates mean a defect has to pass four independent filters to reach a customer.
Detection rates by change size from analysis of 212,687 pull requests; the 200-LOC review threshold is the peer-reviewed finding of Kemerer & Paulk (IEEE TSE).
08 / The research behind it
We didn't invent this. We operationalized it.
Every part of Aegis maps to decades of published software-engineering research. The novelty — the patent-pending part — is the disciplined process that ties it together and puts AI on every gate.
09 / Before & after
The shift, measured.
What changes when divide-and-conquer becomes the operating rule and AI sits on every gate. Mapped to the four DORA delivery metrics.
Cadence and defect figures are BJ's Restaurants 12-month live data; the direction of every other metric is the consistent finding of DORA small-batch research (DORA).
10 / Proof · BJ's Restaurants
Headline case · 12-month live data
A 200+ location enterprise running at the frontier of release engineering.
BJ's Restaurants operates a demanding production environment where downtime touches customers, revenue, and brand trust directly. For the past twelve months, Aegis has carried that environment to twice-weekly production releases with zero critical defects — not by shipping less, but by shipping smaller, fully-understood changes through every gate. That's a stability profile most pure-play tech companies never reach.
2×/wkRelease cadence sustained
0Critical defects (12 months)
200+Locations supported
11 / Get the overview
See the process
that ships this.
Thirty minutes, no pitch deck. We'll walk you through exactly how we decompose, assess impact, and put AI on every gate — using your own stack as the example. The patent-pending part is the process, and we're happy to show it.
The patent-pending part is the process — how we do the work, not a black box. We'll walk you through it. 60-min session · Under NDA on request.