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 81-85 of 236 articles.

Product

Facio's Secret Hygiene: Why AI Agents Never See Raw Credentials and Never Should

Jul 2, 2026Product

Facio's Secret Hygiene: Why AI Agents Never See Raw Credentials and Never Should

An AI agent that sees raw credentials is a security incident waiting to happen. Config files, environment dumps, tool outputs, error messages, and reasoning traces all become leak vectors when the agent's context window is the exposure surface. Production AI agents never see raw credentials — they reference credentials by identifier, the runtime resolves and uses them, and the agent's context stays clean. Facio's secret hygiene is the structural discipline that makes this consistent. Here's how the discipline works.

Security

Your Agent's Reasoning Is the Audit Trail You Cannot Reconstruct: Why Decision Tracing Is the Missing Primitive in 2026 AI Observability

Jul 2, 2026Security

Your Agent's Reasoning Is the Audit Trail You Cannot Reconstruct: Why Decision Tracing Is the Missing Primitive in 2026 AI Observability

Your agent made 47 tool calls in the last hour. You can see what tools it called, what arguments it passed, what responses it received. You cannot see why. The reasoning that produced the call — the model's interpretation, the alternatives considered, the selection rationale — is invisible. Decision tracing makes it visible.

Human-in-the-loop

The HITL Rollback Pattern: When Human Review Should Reverse an Agent Action

Jul 2, 2026Human-in-the-loop

The HITL Rollback Pattern: When Human Review Should Reverse an Agent Action

Most HITL designs treat approval as a one-way gate. The action executes, the audit trail records, and that's the end. But real systems need the ability to roll back — when a reviewer realizes they approved the wrong action, when an incident reveals a pattern, when a customer requests reversal. Here's how to design HITL rollback as a first-class pattern.

Engineering

MCP Spotlight: Sentry MCP Server — The Error Tracking Bridge With Seer AI Autofix, Issue Sync to GitHub/Linear/Jira, and the Production-Observability Default for Agents

Jul 1, 2026Engineering

MCP Spotlight: Sentry MCP Server — The Error Tracking Bridge With Seer AI Autofix, Issue Sync to GitHub/Linear/Jira, and the Production-Observability Default for Agents

The official Sentry MCP Server — ~22 tools (issues, events, releases, performance, replays, source maps, Seer AI root-cause analysis, issue sync to Linear/Jira/GitHub). MIT-licensed, GA, official Sentry-maintained. The production-observability default for AI agents in 2026.

Product

Facio's Structured Output Discipline: How AI Agents Produce Outputs Other Systems Can Actually Trust

Jul 1, 2026Product

Facio's Structured Output Discipline: How AI Agents Produce Outputs Other Systems Can Actually Trust

An AI agent's free-text response is unusable by other systems. Downstream pipelines can't parse it reliably; databases can't store it; APIs can't consume it. Production agents produce structured outputs — JSON, Markdown, schema-validated payloads — that other systems can ingest, validate, and trust. Facio's structured output discipline gives agents the patterns for reliable, machine-readable outputs: explicit schemas, validation before return, type safety, and clear error contracts. Here's how the discipline works.