Quando o caos aprende: a verdade indigesta da empresa AI First
Continuous learning can create extraordinary advantage, but it can also turn unclear processes into a system that industrializes chaos.
Read on LinkedInOperating Intelligence
Newsletter archive
Every edition, in chronological order, on AI agents, software architecture, and the controls required to move serious systems into production.
11 published editionsPublished weekly
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- LatestEN4 min read
When Chaos Learns: The Uncomfortable Truth About AI-First Companies
Many companies still do not know how to deploy the AI they already have. Soon, they may feed systems that learn continuously from everything they do.
Read on LinkedIn↗ - EN4 min read
What It Really Means to Be an AI-First Company
Being AI-first requires redesigning decisions, processes, and accountability—not distributing licenses or accumulating proofs of concept.
Read on LinkedIn↗ - PT-BR4 min read
O que realmente significa ser uma empresa AI First
A company does not become AI-first by buying licenses for everyone or creating an isolated lab. Transformation starts with the operating model.
Read on LinkedIn↗ - EN4 min read
Agent Memory Before Claude 5: What I Built, What the Evidence Shows, and What Comes Next
The record of agent-memory released in March 2026, what the evidence supports, and why operational memory became central to serious agents.
Read on LinkedIn↗ - EN3 min read
The Four Controls Behind Codex Behavior
A practical map of the layers that shape how Codex reasons, acts, collaborates, and stays inside operational boundaries.
Read on LinkedIn↗ - EN20 min read
When AI Agents Start to Act, Who Controls the System?
Why autonomy needs identity, observability, policies, and an explicit control plane before it can scale.
Read on LinkedIn↗ - PT-BR19 min read
Quando agentes de IA começam a agir, quem controla o sistema?
Control, governance, and autonomy must be treated as engineering problems when agents begin acting on real systems.
Read on LinkedIn↗ - EN5 min read
The New Engineering Paradox: The More Agents Do, the More Human Judgment Matters
As agents execute more work, engineering responsibility moves outward to framing, verification, and consequence ownership.
Read on LinkedIn↗ - EN9 min read
The Real Differentiator in AI Agents Is Not the Model. It Is Operational Control.
The differentiator is not only the model, but the operating layer that governs identity, context, boundaries, and evidence.
Read on LinkedIn↗ - EN5 min read
Building Reliable Agentic AI Systems: The PRINCE Case from Bayer
The PRINCE case shows that agentic AI reliability depends on system architecture, validation, and controls around the model.
Read on LinkedIn↗