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 41-45 of 78 articles in Product.

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.

Product

Facio's Operational Metrics: The KPIs That Tell You Whether Your AI Agent Is Actually Working

Jun 27, 2026Product

Facio's Operational Metrics: The KPIs That Tell You Whether Your AI Agent Is Actually Working

Most teams running AI agents have no idea whether the agent is working. They count sessions and tokens, mistake volume for value, and have no metrics for agent quality, user satisfaction, or business outcome. Facio's operational metrics give you the structured KPIs that distinguish an agent that genuinely works from one that just runs. Here's the framework, the metrics that matter, and how to use them to improve agent performance over time.

Product

Why Facio Agents Get Smarter Every Session: The Compounding Returns of Institutional Memory

Jun 26, 2026Product

Why Facio Agents Get Smarter Every Session: The Compounding Returns of Institutional Memory

A stateless AI agent starts every conversation from zero — no knowledge of the user, no awareness of past work, no institutional context. The user re-explains, the agent re-investigates, the workflow repeats from scratch. A Facio agent with institutional memory starts every session with accumulated knowledge: the user's preferences, the project's history, the lessons learned, the patterns that work. The result is compounding returns — each session is faster, more accurate, and more personalized than the last. Here's how the compounding happens.

Product

Facio's Decision Provenance: How to Explain an AI Agent's Reasoning After the Fact

Jun 25, 2026Product

Facio's Decision Provenance: How to Explain an AI Agent's Reasoning After the Fact

"Why did the agent do that?" is the question every AI operator eventually has to answer. The answer is rarely obvious from looking at the agent's outputs. Production agents take hundreds of tool calls across complex contexts; reconstructing the reasoning requires structured provenance — what the agent saw, what it knew, what it decided, and why. Facio's decision provenance features turn post-hoc explanation from guesswork into query work. Here's how to make AI agent reasoning auditable after the fact.

Product

Why Your First AI Agent Shouldn't Be Your Most Ambitious: The Facio Approach to Graduated Deployment

Jun 24, 2026Product

Why Your First AI Agent Shouldn't Be Your Most Ambitious: The Facio Approach to Graduated Deployment

The first AI agent workflow you ship determines whether your team ever ships a second one. Most teams make the same mistake: they pick their most ambitious, highest-impact use case for the pilot — and when it fails or underperforms, the team concludes "AI agents don't work for us." Facio's approach is the opposite. Start small, ship the boring workflow, build trust, then expand. Here's the graduated deployment methodology and why the choice of first workflow is the most important product decision you'll make.