The argument, in long form.
Where we work out the architecture before we ship it. Board-readable on the way in, engineer-honest on the way down — and reproducible at the bottom, via Gaussia.
Proof Before Action: How Alquimia Enforces Agent Authorization with Zero Trust and MCP
A production implementation that pauses consequential tool calls, lets the MCP define what the user is approving, and resumes only the exact action under unchanged terms.
Zero Trust for AI Agents: How We're Rethinking Security at Alquimia
Traditional zero trust was built for servers and networks, not systems that plan, reason, and act on their own. Here's how we're extending it — from non-human identity for every agent to designing as if the attacker is already inside — to secure autonomous AI at Alquimia.
Ledger: knowledge our agents can be audited on
Every layer of an agent stack has to be governable, and knowledge is the weakest one today. We are building Ledger, a module that compiles curated sources into versioned, OKF-conformant knowledge bundles that agents consume through MCP — so what an agent knows becomes an artifact you can check out, diff, and audit.
Running AI agents on Red Hat OpenShift AI: lessons from a sovereign deployment
GPU economics, hardware observability, model explainability — three runtime-layer choices that decide what governance is feasible above the agent layer.
Why governing AI agents end-to-end is now a board-level concern
When AI agents make decisions a person used to be accountable for, governance reaches the boardroom. The six properties every audit committee should ask about.
From a notebook to a fleet: why AI agents in production need a platform layer
The first AI agent is not the hard one. The fourth is. When one agent becomes a fleet, the platform underneath them is what makes it operable.
Run production-grade agents you govern end-to-end.
We work with organizations adopting AI agents under regulated conditions. A short conversation is usually enough to know where to begin.
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