Facio Blog

Practical notes on human-reviewed AI agents.

Payload-powered product notes, security writing, HITL patterns, and operational guidance from the Facio runtime: long sessions, Placet approvals, audit trails, memory, providers, channels, tools, and Docker-first operations.

Showing 116-120 of 235 articles.

Product

Facio's Incident Response Playbook: How AI Agents Detect, Triage, and Mitigate Production Issues Autonomously

Jun 22, 2026Product

Facio's Incident Response Playbook: How AI Agents Detect, Triage, and Mitigate Production Issues Autonomously

Production AI agents need an incident response playbook — a structured way to detect issues, triage severity, mitigate damage, and escalate intelligently when human judgment is required. Facio's runtime provides the building blocks: heartbeat-driven monitoring, structured error responses, log queries, HITL escalation, and checkmarked state recovery. Combined, they let agents handle routine incidents autonomously and bring humans into the loop at exactly the right moment. Here's the playbook.

Human-in-the-loop

Reviewer Context: The State That Makes HITL Decisions Good Across Sessions and Handoffs

Jun 22, 2026Human-in-the-loop

Reviewer Context: The State That Makes HITL Decisions Good Across Sessions and Handoffs

Most HITL articles focus on individual decisions. This one focuses on the decision-maker. The reviewer's context is more important than the reviewer's credentials — and most HITL systems destroy context by design. Here's the design pattern for building reviewer context that survives across sessions, escalations, and handoffs.

Engineering

MCP Spotlight: GitHub MCP Server — The Official 100+ Tool Bridge With Toolsets, OAuth, Dynamic Tool Discovery & Code Security Built In

Jun 21, 2026Engineering

MCP Spotlight: GitHub MCP Server — The Official 100+ Tool Bridge With Toolsets, OAuth, Dynamic Tool Discovery & Code Security Built In

The official GitHub MCP Server — 100+ tools organized into dynamic toolsets (repos, issues, pull_requests, actions, code_security, secret_protection, dependabot, code_scanning), local stdio + remote OAuth, full tool annotations, maintained by GitHub itself.

Human-in-the-loop

Right-Sized HITL: The Action Classification Engine That Decides What Actually Needs a Human

Jun 21, 2026Human-in-the-loop

Right-Sized HITL: The Action Classification Engine That Decides What Actually Needs a Human

The HITL industry is converging on a wrong assumption: that every AI action should have a human in the loop. The right architecture isn't 100% HITL — it's right-sized HITL. Most actions should be autonomous. Some should be sampled. A few need synchronous review. The challenge is mapping the right oversight to the right action.