Overview
Methodology
How CORTX learns your business in 30–60 days.
C · D · A
Capture, Decode, Activate. Three movements that turn tribal knowledge into a working system.
Read the methodology →
Products
Featured
Atlas + Flow ship together.
A · F
Two products, one operations OS. Add modules as you grow.
Compare products →
Solutions
Case study
Shin-On automated 78% of insurance claims.
78%
A 14-person dental clinic, ₪34K recovered, 47 hours saved monthly.
Read the story →
Company
Hiring
Operators & engineers wanted.
5 / 12
Five open roles across deployment, engineering, and design.
See all roles →
WHITEPAPERS

Deep dives on AI-native ops.

Long-form technical writing on the architecture, methodology, and operational patterns behind CORTX. Each paper is a self-contained framework, not a product brochure.

Memory as Files: Designing Inspectable State in Agentic Systems

A case for treating agent memory as a versioned, diffable, customer-owned asset.

Most agentic systems treat memory as an implementation detail — a vector store, a hosted state service, an opaque embedding cache. This paper argues that the operational consequences of this default are unacceptable for serious deployments. We present an alternative architecture in which agent memory is a structured filesystem, owned by the customer, versioned in the open, and inspectable by humans without specialized tools. We describe the runtime mechanics, the trade-offs against vector-based approaches, and the deployment patterns we have observed in production over the last twelve months.

April 2026 · 28 pages · v1.2 · Engineering

Download PDF

The Validation Loop: A Discipline for Operational AI

Why "task completed" is not the same as "task verified," and what that distinction requires of an agent's runtime.

The difference between a demonstration and a deployment is the validation loop. This paper specifies the loop in detail — Instruct, Execute, Verify — and traces its implications across the agent's runtime, the operator's interface, and the audit trail. We present primitives for validation across API, screen, and database surfaces. We argue that systems lacking an explicit validation discipline cannot be responsibly deployed for operational work, regardless of the underlying model's capability.

March 2026 · 22 pages · v1.0 · Methodology

Download PDF

Local-First AI: Architectural Patterns for Data Sovereignty

How to deploy capable agents without exfiltrating customer data, and what becomes possible when the model lives on the customer's hardware.

The default deployment posture for AI systems — model in the cloud, customer data routed through external services — is incompatible with a meaningful share of the businesses that most need agentic operations. This paper describes a local-first architecture in which the model, the agent runtime, and the operational state all run on customer-owned infrastructure. We detail the hardware envelope, the network posture, the trade-offs against frontier model capability, and the patterns for routing specific tasks to cloud models when the operational benefit justifies it.

February 2026 · 34 pages · v1.1 · Architecture

Download PDF
FORTHCOMING

In progress.

Three additional papers are in active drafting:

  • The MCP Layer Architecture. A reference document for the four-layer context model.
  • Vertical Onboarding: From First Interview to Live Agent. The methodology paper.
  • Operator-Centered AI: A Design Philosophy. The interface argument.

Subscribe to the blog to be notified when each is published.

STAY IN TOUCH

Get notified.

One email when a new whitepaper is published. Nothing else.

Next Back to all resources