Start Here | Living Framework

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For researchers and academics

You are working on adjacent problems — reliability, verification, human-AI interaction, extended cognition, or long-horizon AI collaboration — and you want to understand how this research frames those problems and what it has found. Start with the theoretical grounding, then move to the empirical findings.

01 — Start

Research Overview

The central question, six research themes, and five major findings. Gives you the theoretical landscape before you read individual papers.

→ research/

02 — Foundation

Papers 01 + 02

The reliability architecture and memory governance papers. Together they define the theoretical framework on which everything else builds.

→ papers/

03 — Empirical

Paper 03: Failure Taxonomy

Six named failure modes derived from empirical observation. The first systematic taxonomy of structural AI collaboration failures.

→ papers/

04 — Recent

Papers 07 + 08

Distributed cognition as the right frame for human-AI systems, and the verification layer as the structural gap in current reliability approaches.

→ papers/

05 — Active

Project RITAM

The active reliability architecture research. Open investigation questions and current status — where collaboration might intersect.

→ ritam/

06 — Priority record

Early Observations

What the research documented before those concepts became mainstream — the intellectual priority record.

→ early-observations/

07 — Applied layer

Applied AI Guides

Twelve field-specific guides showing how this research translates into practice — for practitioners in adjacent fields working with AI.

→ Applied AI Guides

For practitioners building or running AI workflows

You are working with AI systems in production — building collaborative pipelines, managing long-horizon AI projects, or dealing with reliability failures you cannot quite name or diagnose. Start with the failure library, then take the assessment to locate your specific gaps.

01 — Diagnose

Failure Library

Six documented failure modes with descriptions, signals, and what they cost. Start here to identify whether a problem you have seen has a name and a structural cause.

→ failure-library/

02 — Assess

Reliability Assessment

Twenty questions across five reliability domains. Identifies which failure modes your workflow is most exposed to and which papers address them.

→ assessment/

03 — Understand

Papers 01 + 02

The structural conditions for reliable AI collaboration — context integrity and memory governance. These are the most directly applicable papers for practitioners.

→ papers/

04 — Apply

Project NIYOM

The execution layer project — how the framework's architectural principles translate into implementable workflow structures.

→ niyom/

05 — Translate

Terminology Translator

Maps the industry vocabulary you already use — context engineering, RAG, grounding, AI memory — to the Living Framework concepts that describe the same phenomena more precisely.

→ terminology/

06 — Self-directed

Applied AI Guides

Twelve guides written for practitioners in your field — managers, lawyers, teachers, HR professionals, recruiters, administrators. Grounded in the same research, built for immediate use.

→ Applied AI Guides

07 — If needed

Work directly with Rishi

If the assessment identifies significant exposure across multiple failure modes, a direct engagement may help you prioritise and address them.

→ advisory/

For leaders and decision-makers

You need to understand the reliability risks in your organisation's AI usage at a level that lets you make informed decisions — about investment, oversight, or where to focus attention. Start with the research overview, then evaluate specific exposure with the assessment.

01 — Orient

About the Research

What this research is, how it has developed, and what makes it different from general AI commentary or enterprise AI product marketing.

→ about/

02 — Understand

Research Overview

The central question and five major findings. Readable without prior technical knowledge — designed to give a clear view of what the research has established.

→ research/

03 — Identify risks

Failure Library

The six structural failure modes and what they cost in practice. Concrete enough to apply to your organisation's AI workflow without technical detail.

→ failure-library/

04 — Locate gaps

Reliability Assessment

Twenty questions across five domains. Even if your team takes this, the domain breakdown reveals where your AI collaboration architecture has structural gaps.

→ assessment/

05 — See direction

Research Roadmap

Where the research is going and what questions remain open. Useful for understanding the frontier and what will likely emerge from this work.

→ roadmap/

06 — Self-directed

Applied AI Guides

Twelve field-specific guides for self-directed application. If direct engagement is not the right fit yet, these give your team an immediate, research-grounded starting point.

→ Applied AI Guides

07 — If appropriate

Advisory Engagement

If the assessment reveals significant structural gaps, a direct engagement can focus the research specifically on your organisation's context.

→ advisory/

Good to know

No account required

All papers are published on Zenodo with open access. The assessment runs entirely in your browser — no data is collected. Everything on this site is freely accessible.

The framework is evolving

This is called a "Living Framework" because it develops as the research does. Papers 01–08 are published. RITAM and NIYOM are active. New findings get incorporated. The roadmap shows what is currently under investigation.

Independent research

There is no institution, no funding body, no corporate sponsor. The research is not affiliated with any employer or company. Findings reflect what the evidence supports, not what any stakeholder would prefer.

Starting from a specific failure?

If you have a concrete failure in mind — context drift, numerical errors, trust breakdowns — go to the Failure Library first and work backwards from there to the relevant papers and assessment domains.