Engineering briefs, architecture deep-dives, and product updates from the team building the trust layer for AI agents.
72% of enterprises already run agentic AI in production. 60% have no formal governance model. The tools you already have don't close the gap — here is what does.
72% of firms already run agentic AI in production. 60% have no formal governance. Here's why the tools you already have don't close the gap — and what does.
An eval dashboard tells you your agent hallucinated — yesterday, after the user already saw it. A runtime gate stops it before the output ships. Here's what 20ms of inline governance actually looks like.
An eval dashboard tells you your agent hallucinated — yesterday. A runtime gate stops it before the output ships. Here's the 4-engine validation pipeline that governs every agent call in under 20ms.
Most teams approve agents on subjective review. Here's the structured scoring framework — 11 measurable dimensions across five families — that turns "looks good" into a governance decision you can defend.
'Looks good' is not an evaluation framework. Here are the 11 dimensions every AI agent should be scored on before production — and how each one is actually measured, not guessed at.
Why 67% of AI pilots stall before production — and the three-phase governance roadmap that gets agents past the pilot purgatory gate
67% of enterprise AI agent pilots never reach production. The blockers are not technical — they are governance: missing audit trail, undefined incident response, no policy enforcement, and no compliance demonstration. Here is the 90-day roadmap that resolves all four.
The #1 agentic AI risk — and the three controls that prevent LLM06 violations without eliminating agent autonomy
OWASP LLM06 is the vulnerability that amplifies every other LLM risk. System prompt instructions do not prevent it — capability-level controls do. Here are the three technical controls: minimum-capability tool scoping, confidence-based escalation thresholds, and human-in-the-loop enforcement for irreversible actions.
Three regulatory regimes — one overlap table — and the four obligations every enterprise deploying AI agents must meet
OSFI E-23, SR 26-2, and the EU AI Act now apply simultaneously to multi-national AI agent deployments. All three converge on four obligations: named accountability, documented risk classification, ongoing monitoring, and human oversight. Here is the complete comparison and the unified governance architecture that satisfies all three.
New engineering briefs and product updates whenever we ship something worth reading.
