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Timely analysis of the developments shaping agentic AI, plus durable guidance for building DataInbox as the deterministic business runtime beneath it.

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Capability Review

From model calls to governed work: the first DataInbox capability review

What our updated Runtime and AI cost explanations mean for teams: reusable capabilities, accountable actions and costs that can be tested in a pilot.

October 8, 2026

5 min read

DataInbox

Architecture & Research

Latest Articles

Proposed DataInbox Companion flow: check state and authority, invoke custom CAP logic, then preserve the decision and result; recheck state after approval.

News analysis

Companion Architecture

SAP CAP opens custom business logic to agents. DataInbox should govern each request.

Reuse custom CAP logic, reduce manual handoffs and keep approvals accountable: the business case for DataInbox's proposed SAP Companion, to test in a pilot.

Companion Architecture

Salesforce is becoming a capability provider. DataInbox should govern the connection.

Salesforce now exposes data, automation and agent capabilities through MCP, APIs and open skills. DataInbox turns that access into a governed agent runtime with company rules, replaceable AI, approvals and auditable evidence.

Your AI model is temporary. Your organisational understanding should not be.

Current business context makes an agent useful now. Durable, model-independent organisational understanding keeps the business operable when models, people and providers change.

a16z's $1.1B Machine Age Fund and the cost of agentic AI

a16z says AI workloads and token intensity are rising by orders of magnitude. Here is why the Machine Age also needs a deterministic business runtime.

Why does software keep getting more expensive?

For fifteen years I've watched businesses accumulate more technology and solve less of the actual business problem. Here is why we built DataInbox around business messages instead of yet another layer, and why our pricing looks the way it does.

Hero Mode vs Architect Mode: Why AI Without Structure Is Just a Demo

Most companies are running AI in Hero Mode - manual prompts, Slack-based approvals, human-in-the-loop chaos. That does not scale. Here is why the shift from AI enthusiasm to AI reliability requires an Architect layer.

Why a Clean Data Layer Matters for AI Agents

AI agents are only as good as the data they can access. Learn why multi-model validated data on intake is the key to reliable AI automation.

From Workflow-First to Data-First Automation

Why leading organizations are shifting from workflow orchestration to data-first automation strategies.

Identity Resolution: The Missing Piece in Business Data

How automated identity resolution transforms fragmented customer data into actionable Golden Profiles.

Nov 28

6 min read

Nicky Romeijn

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