Show HN: Two Pillars Protocol – a maturity model for AI-era software engineering
Introducing Two Pillars Protocol, a maturity model for AI-era software engineering that measures individual cognitive capacity (Mixer Mode) and organizational meta-software readiness. It claims to cover 17 of 18 characteristics missed by existing models and invites participation via a self-diagnostic.
Two Pillars Protocol
A maturity model for AI-era software engineering
Measuring two capacities that compound when humans build software with AI.
Under evaluation
We're proposing what we believe is the first maturity model for software engineering in the AI era that measures both individual cognitive capacity (Mixer Mode) and organizational meta-software readiness at the same time.
We surveyed the existing literature — CMMI V3.0 (2023), ISO/IEC 33000, Pöppelbuß & Röglinger (2011), plus a dozen newer AI-readiness frameworks — and found none of them measure both pillars at once with operationalized items that hold under AI-mediated work.
We want to be wrong. If you know of prior work that covers this, please tell us. The instrument is live, the data is anonymized, and the model is open to challenge.
Pillar 1 — Mixer Mode (individual)
The cognitive disposition to run multiple agentic threads in parallel, evaluate their outputs simultaneously, and integrate them into coherent work. Like a music producer running stems through a console: not authoring each track from scratch, but coordinating, gating, mixing.
Measured per practitioner. 3 sub-dimensions: Multiplicity, Simultaneity, Integration.
Pillar 2 — Meta-Software (organizational)
Software (and the org around it) that understands, modifies, validates and orchestrates other software at the symbolic level — the capacity AI-era practitioners need to reason about systems whose internals they no longer hand-code line by line.
Measured per organization. 4 D-scores: Cognitive, Meta-Software, Institutional, Exposure.
Why a new model?
CMMI tells you whether processes are repeatable. ISO/IEC 33000 tells you whether quality is measurable. Pöppelbuß & Röglinger (2011) gave us the methodology to evaluate maturity models themselves. None of them speak the language of "this developer just dispatched 4 agentic flows in parallel, gated them with tests, and shipped".
A full crosswalk vs CMMI / ISO 33000 / P&R 2011 is being written up. Highlights: of 18 characteristics that AI-era engineering work exhibits, prior frameworks cover 4–7 each; ours covers 17. The remaining one is honest about gaps. Show us what we missed.
Take the diagnostic yourself
For individual practitioners. ~12–18 minutes. You get a private report with your Mixer Index, D-score perception of your org, quadrant placement, and peer context.
No account, no email login. Token-based access to your private report.
Re-accessible via a private link you save at the end.
Your responses are anonymized and pooled with closed cycles for peer comparison.
D-scores reflect YOUR perception of your org (N=1) — the triangulated org-level view requires a full cycle.
Start the diagnostic →
Run a cycle in your org
For tech leaders running an internal evaluation. We work with you to design the cohort, run the assessment, and surface aggregate patterns — under k-anonymity (n ≥ 3 per cell).
Internal cohort (your respondents only) — not a public benchmark.
Aggregate dashboard for leadership; private individual reports for respondents.
PDF snapshot at cycle close; reproducible methodology.
We onboard you 1:1 to scope the cycle (cohort, timeline, hierarchy).
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Request access for your org
Short brief so we can route your inquiry. We reply within 1–2 business days from [email protected].