AI agents are software that acts autonomously — processing expense reports, handling customer service, managing HR workflows. They have real access to real systems. And right now, almost nothing governs what they do.
Governance must live in the runtime — where it can't be bypassed, ignored, or forgotten.
Finance · HR · Sales
Marketing · Eng · CS
Autonomous software at machine speed
Policy · Trust scoring
DLP · Audit
LLMs · Tools · APIs
Data · Systems
Every API call, every database query, every message sent — checked against your organization's policies and historical behavior patterns before it executes. Unauthorized or out-of-character actions are blocked by design, not discovered in the next audit.
Document what agents do. After the fact.
Run agents. But don't enforce what they're allowed to do.
Enforces policy in real time, before the action executes.
Technical executive and founder who built and led a global services firm to a successful exit before transitioning to enterprise SaaS leadership. Combines hands-on engineering fluency with go-to-market expertise — equally comfortable pairing on production code and presenting to a board.
Senior software engineer with 20+ years across full-stack architecture, DevOps, and AI/ML systems. Technical depth spans C#, JavaScript, Rust, neural networks, and large language models — an engineer who has shipped enterprise production code for two decades.
Your AI policy says your agents cannot access customer data without authorization. Your agents can, in fact, do exactly that. Both statements are true. That is the problem.
April 20, 2026PE firms acquire companies and immediately tighten controls: standardized reporting, mandated tooling, new approval workflows. From the boardroom, it's responsible governance. From the engineering floor, it's the moment top performers update their LinkedIn. Fifty years of research and the DORA data say the same thing: this playbook destroys the value the deal was meant to create. Here's the boundary model that resolves the paradox.
April 14, 2026Most engineering teams already have access to AI coding tools. That is not the hard part. The real problem is that leadership often treats AI adoption like a software procurement exercise instead of an operating model change. Without clear norms, lightweight governance, and meaningful measurement, AI becomes another line item in the budget instead of a real driver of engineering throughput.
April 1, 2026We're working with a small group of design partners. If you're deploying AI agents in a regulated or enterprise environment, we'd like to talk.