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 21-25 of 62 articles in Product.

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.

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.

Product

Facio's Quota and Rate Limit Awareness: How AI Agents Stay Under the Caps Without Throttling the Workflow

Jun 30, 2026Product

Facio's Quota and Rate Limit Awareness: How AI Agents Stay Under the Caps Without Throttling the Workflow

Every external system an AI agent touches has limits. APIs have rate limits per second. Models have token quotas per minute. Storage services have request budgets per hour. Email services have daily send caps. The naive agent hits these limits and fails. The disciplined agent tracks consumption in real time, paces its requests to stay under the caps, batches where possible, and shifts work to alternative windows when quotas are tight. Facio's quota awareness turns a brittle integration into a predictable operation. Here's how the discipline works.

Product

Facio's Graceful Degradation: How AI Agents Keep Working When the Stack Around Them Breaks

Jun 29, 2026Product

Facio's Graceful Degradation: How AI Agents Keep Working When the Stack Around Them Breaks

Production AI agents operate in hostile environments. MCP servers go offline, APIs return 500s, databases time out, networks drop mid-call. The agent that fails on the first hiccup is a prototype. The agent that degrades gracefully — falling back to alternatives, retrying intelligently, escalating when needed, and continuing with what it has — is a production system. Facio's graceful degradation patterns are the structural discipline that keeps agents working when the world around them isn't. Here's how the patterns work.

Product

Facio's Context Window Discipline: How AI Agents Stay Sharp When Conversations Run for Hours

Jun 28, 2026Product

Facio's Context Window Discipline: How AI Agents Stay Sharp When Conversations Run for Hours

An AI agent's context window is its working memory — and it's finite. Long sessions accumulate context: every tool call result, every message, every retrieved fact stays in the window until the session ends. The agent gets slower, more expensive, and eventually confused as the context fills. Facio's context window discipline gives agents the structural patterns to stay sharp across hours of work: selective loading, aggressive summarization, strategic forgetting, and checkpoint-driven compaction. Here's how the discipline works.