Insights on AI agent governance, safety, and building production-grade agent systems.
Most agent governance lives in a slide deck while the running system obeys nothing. This guide covers the controls that actually bind at runtime: per-agent spend caps, approval gates on irreversible actions, and a tamper-evident record, sorted by risk so the gates fire where they matter and stay out of the way where they don't.
Approve an agent action before it fires, without gating every step. How LangGraph's interrupt() handles in-graph decisions, where it stops helping, and how to put a hard approval gate on irreversible tool calls with working code.
We classified over 15,000 public discussions from AI agent builders across 77 platforms. The failures they complain about most are not hallucinations. They are governance failures: runaway permissions, uncapped cost, missing audit trails, and approval gaps. Here is the data.
Billing alerts fire after the money is gone. The only reliable fix is a hard dollar cap enforced before each LLM call executes. Here is how to set one up, with working code.
AI agents are making real decisions in production: calling APIs, moving money, sending emails. Here's why an AI agent governance framework isn't optional, and what runtime guardrails actually look like.
A practical walkthrough of adding policy-based governance and AI guardrails to any MCP server using Tenet's proxy pattern. No code changes to your existing server required.
Cloud providers invented shared responsibility for infrastructure security. AI agents need the same AI governance framework. Here's what it looks like when humans and autonomous agents share accountability.