The definitive body of work on operating autonomous AI
A numbered series of evergreen, citable publications. Each one defines a concept, argues why it matters, and stays at the level of principle rather than product. Read as a whole they are the reference an enterprise team returns to when a green dashboard is not enough to trust an agent. Start with the canonical piece, then follow the path in order.
The plain-language walk-throughs. Start with the overview, then follow one run all the way through.
How Provy Works
Trace in, proof out. What goes into Provy, what it does with it, and what comes out, with one diagram. Read this first for the picture before the theory.
How Provy Checks the Work
Follow one run start to finish: the guess Provy makes up front, the real result that comes back, and how lining the two up becomes a trust score. Plain language, one worked example.
Read the path in order
Publications 001 through 007 are the learning path. Each builds on the one before it, from naming the problem to a framework for locating your own organization. This is the same order the Start Here path follows.
Decision Assurance
The discipline itself: the commitments that close the gap, the vocabulary that ties them together, and how it differs from monitoring, evaluation, guardrails, and governance.
The Enterprise Confidence Gap
The distance between what an AI system appears to do and what it actually achieves, why that gap is widening, and what closing it demands.
Why AI Observability Isn't Enough
The tools you already run watch the machine run. Correctness lives in the result. The category error, laid out tool by tool.
Outcome Intelligence
Measuring each decision against the real result it was meant to produce, and the difference between an estimated outcome and a reconciled one.
Runtime Evidence
Confidence you cannot show a third party is not enterprise confidence. Why the record that ties a decision to its outcome must be held at the moment, not reconstructed later.
Continuous Verification
Trust is a property you keep, not a gate you pass once. How to keep checking that a system still does the right thing as the world moves under it.
The Enterprise Confidence Model
The five stages an organization moves through toward trusting autonomous AI, and how to find the one you are in and the next move to make. The end of the path.
The conceptual grounding
The two publications that ground everything else: how autonomous systems actually fail, and the gap that failure opens between activity and outcome.
AI Failure Modes
A taxonomy of how autonomous decisions actually go wrong, beyond hallucination, and which of the six modes a green dashboard will never show you.
The Enterprise Confidence Gap
The problem the whole discipline answers: why enterprises can build autonomous agents faster than they can trust them, and what the distance between the two is made of.
The frameworks that put it to work
Move from principle to practice: a model for locating your organization, the life of a single decision, and the working rules for running the discipline day to day.
The Enterprise Confidence Model
The five stages from hope to assurance, and how to find the one you are in and the next move to make.
The Decision Lifecycle
The life of one autonomous decision across six stages, and where confidence is won or lost at each one.
Enterprise AI Operating Principles
The working rules for running autonomous AI with assurance, for the platform team that has to put the discipline into practice.
Start with these
If you read only three, read these. They carry the core of the argument, from the discipline itself to why current tooling falls short and what to measure instead.
Decision Assurance
The discipline that closes the gap between what an agent did and what it achieved.
Why AI Observability Isn't Enough
Why a green dashboard can sit on top of a real failure, tool by tool.
Outcome Intelligence
Measuring results instead of activity, and the gap between estimated and reconciled.
All publications refreshed, July 2026
Every publication in the library is now at version 2.1. The July 2026 pass added a publication number and named author, an executive summary, in-practice scenarios, and a signature diagram to each paper, on top of the abstract, takeaways, references, footnotes, and citation each already carried. See the change history at the foot of any paper for the detail.
Read the discipline, then see it on your own agents
The publications describe what confidence in autonomous AI takes. Provy is how an enterprise puts it into practice: every agent decision measured against the real outcome, with the evidence to prove it.
Get a demoNew to the topic? The Start Here learning path walks the same numbered series in order, with the concepts and insights that support each step.