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.

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Showing 6-10 of 62 articles in Product.

Product

Facio's Idempotency Discipline: How AI Agents Avoid Double-Charging Customers When Retries Happen

Jul 17, 2026Product

Facio's Idempotency Discipline: How AI Agents Avoid Double-Charging Customers When Retries Happen

AI agents fail and retry. The model returns 500, the agent retries the tool call. The network drops mid-flight, the agent retries. The tool returns a timeout but the operation actually succeeded, the agent retries. Without idempotency, retries cause duplicate side effects: double-charged customers, duplicate emails, duplicate records, double-deployed code. Facio's idempotency discipline gives every side-effecting operation a unique key, makes operations safe to retry, and detects duplicate executions before they cause damage.

Product

Facio's Per-Tool Credentials: How AI Agents Authenticate Without Becoming a Credential Vault

Jul 16, 2026Product

Facio's Per-Tool Credentials: How AI Agents Authenticate Without Becoming a Credential Vault

An AI agent that calls a hundred APIs needs credentials for each one. The naive approach — give the agent one master credential with broad access — is a security catastrophe waiting to happen. The slightly less naive approach — let the agent store credentials in environment variables or config files — is also bad. The discipline Facio applies is per-tool credentials: each tool has its own narrowly-scoped credential, never seen by the model, replaced on rotation, and audited per call. The agent gets access; the security team gets controls.

Product

Facio's Normalization Layer: How AI Agents Handle the Messy Real-World Inputs That Never Match the Schema

Jul 15, 2026Product

Facio's Normalization Layer: How AI Agents Handle the Messy Real-World Inputs That Never Match the Schema

Production AI agents receive inputs from dozens of sources: web forms, APIs, webhooks, email, chat, file uploads, copy-paste from spreadsheets. Every source has its own format, conventions, and quirks. Every source produces messy data: missing fields, wrong types, locale variations, encoding issues, ambiguous values. Naive agents assume clean inputs and fail on real-world data. Facio's normalization layer sits between the world and the agent's reasoning — cleaning, standardizing, and validating every input before the agent sees it. The agent reasons over clean data; the team reasons about fewer failures.

Product

Facio's Token Discipline: How AI Agents Spend Their Context Budget Like Engineers Spend Cloud Credits

Jul 14, 2026Product

Facio's Token Discipline: How AI Agents Spend Their Context Budget Like Engineers Spend Cloud Credits

An AI agent's context window is its working memory — and like cloud credits, it has a budget. A team that ignores the budget wakes up to a $50k monthly bill; an agent that ignores the context budget forgets, hallucinates, and produces degraded output by mid-session. The naive approach is to give the agent whatever context it asks for and hope for the best. Facio's token discipline treats the context window as a managed resource: tracked, allocated, optimized, and accounted for. The agent stays sharp; the team stays within budget.

Product

Facio's Versioned Prompts: How AI Agent Behavior Stays Reproducible Through Model and Code Changes

Jul 13, 2026Product

Facio's Versioned Prompts: How AI Agent Behavior Stays Reproducible Through Model and Code Changes

An AI agent's behavior comes from the prompt, the model, and the code that interprets both. Change any of them and the behavior changes. The team upgrades the model — the agent's responses shift subtly. The team tweaks the prompt to fix a bug — the fix breaks three other things. The team refactors the agent code — the agent stops working entirely. Reproducibility collapses. Facio's versioned prompts discipline makes agent behavior reproducible: every prompt version is pinned, every change is tracked, every comparison is traceable.