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 131-135 of 235 articles.

Product

Facio's Self-Healing Agents: How Error Detection, Diagnosis, and Recovery Run as a Continuous Loop

Jun 17, 2026Product

Facio's Self-Healing Agents: How Error Detection, Diagnosis, and Recovery Run as a Continuous Loop

AI agents that fail in production are a fact of life. The question isn't whether your agent will encounter an error — it's what happens after. Facio's self-healing architecture turns failures into recovery actions through a continuous detection-diagnosis-recovery loop. The agent detects, classifies, and resolves issues without human intervention for routine cases, and escalates intelligently when it can't. Here's how the loop works.

Security

CVE-2026-2256: The First Agent-Runtime CVE That Should Change How You Deploy AI Agents

Jun 17, 2026Security

CVE-2026-2256: The First Agent-Runtime CVE That Should Change How You Deploy AI Agents

ModelScope's MS-Agent framework shipped a critical command injection vulnerability (CVSS 9.8) that turns any document, ticket, or email into a remote shell for attackers. No patch. Three months after disclosure. The procurement agent that approved $5M in fraudulent orders wasn't using a vulnerable framework — it was using any framework, the same way every agent does.

Human-in-the-loop

HITL for Code Generation Agents: Why 'Looks Good' Approvals Are Creating Production Incidents

Jun 17, 2026Human-in-the-loop

HITL for Code Generation Agents: Why 'Looks Good' Approvals Are Creating Production Incidents

Code generation agents are the fastest-growing HITL use case — and the worst-implemented. Reviewers approve diffs they don't fully understand, miss subtle security flaws, and let prompt injection land in production. The 10× velocity claim isn't real if 30% of approvals are rubber-stamps.

Product

Facio's Two-Layer Memory: How Passive Context and Active Recall Give AI Agents Institutional Knowledge

Jun 16, 2026Product

Facio's Two-Layer Memory: How Passive Context and Active Recall Give AI Agents Institutional Knowledge

AI agents with no memory are amnesiacs — they start every session knowing nothing about the user, the project, or the prior work. Facio's two-layer memory architecture combines passive context (always loaded) with active recall (query-based search) to give agents institutional knowledge that grows over time. Here's how the layers work, what each is good for, and why the combination beats either approach alone.