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 11-15 of 281 articles.

Product

Facio's Feature Flag Discipline: How AI Agents Roll Out New Capabilities Safely Without Risking the Whole System

Aug 3, 2026Product

Facio's Feature Flag Discipline: How AI Agents Roll Out New Capabilities Safely Without Risking the Whole System

AI agents evolve. New capabilities ship. The naive approach enables a new capability for everyone at once: one bug, every customer affected. Facio's feature flag discipline gives agents structured mechanisms to roll out new capabilities safely: flag definition and configuration, per-request evaluation with tenant allowlists and percentage rollouts, gradual rollout strategies from internal testing through canary customers through staged percentages, rollback and kill switch for incident response, and flag lifecycle management with cleanup. The team ships often without risking the whole system.

Security

Runtime Policy Engine Architecture for AI Agents: Why 7 Policy Layers Are the Minimum Stack That Survives the Multi-Vector Threat Model

Aug 3, 2026Security

Runtime Policy Engine Architecture for AI Agents: Why 7 Policy Layers Are the Minimum Stack That Survives the Multi-Vector Threat Model

The Maxim AI 2026 implementation guide confirmed: AI guardrails are runtime controls that validate inputs to and outputs from an LLM against safety, security, and compliance policies — and the operational reality is that single-vector guardrails (NeMo, Llama Guard, Lakera Guard) miss the multi-vector attacks that the 2026 incident data documents. The Context Studios analysis named the architectural commitment: production agents need runtime policy, structural command parsing, sandboxing, provenance-tagged memory writes, identity, traces, evals, compliance exports, and incident-to-test regression loops. The seven-layer architecture that satisfies the commitment.

Human-in-the-loop

HITL and the Asymmetric Cost of False Positives vs False Negatives: Why the Reviewer's Mental Math Almost Always Gets It Wrong

Aug 3, 2026Human-in-the-loop

HITL and the Asymmetric Cost of False Positives vs False Negatives: Why the Reviewer's Mental Math Almost Always Gets It Wrong

Every HITL decision is a tradeoff between false positives (approving a bad action) and false negatives (rejecting a good action). Most reviewers default to optimizing for the false positive — rejecting the bad action is visible, accepting the bad action is catastrophic. The asymmetric worry produces over-rejection. Here is why the asymmetric mental math is the most common collapse in HITL, and how to design systems that give the math the right shape.

Engineering

MCP Spotlight: Stripe MCP Server — The Official Payments Bridge With Restricted-Key Enforcement, OAuth + DCR, and the Commerce-Default Reference for Agents

Aug 2, 2026Engineering

MCP Spotlight: Stripe MCP Server — The Official Payments Bridge With Restricted-Key Enforcement, OAuth + DCR, and the Commerce-Default Reference for Agents

The official Stripe MCP Server by Stripe — ~25 focused tools (customers, subscriptions, invoices, payments, refunds, products, payment links, disputes). Restricted API Key enforcement at startup (sk_ keys rejected). OAuth 2.0 + Dynamic Client Registration for hosted (mcp.stripe.com). Idempotency keys required for all writes. Apache 2.0-licensed.

Product

Facio's Graceful Degradation Discipline: How AI Agents Keep Working When Tools, Models, and Dependencies Fail — Without Losing the Customer's Trust

Aug 2, 2026Product

Facio's Graceful Degradation Discipline: How AI Agents Keep Working When Tools, Models, and Dependencies Fail — Without Losing the Customer's Trust

AI agents depend on tools. The tools depend on external services. The services go down. The naive approach assumes everything works — when a tool returns 503, the agent crashes; when the rate limit hits, the agent gives up. Facio's graceful degradation discipline gives agents structured mechanisms to keep working when dependencies fail: retry with backoff, circuit breakers to prevent cascading failures, fallback strategies for alternative paths, timeouts and cancellation, and proactive customer communication. The agent keeps the customer experience even when the technical foundation cracks.