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.

Clear

Showing 11-15 of 78 articles in Product.

Product

Facio's Output Validation Discipline: How AI Agents Catch Their Own Mistakes Before They Reach the Customer

Jul 28, 2026Product

Facio's Output Validation Discipline: How AI Agents Catch Their Own Mistakes Before They Reach the Customer

AI agents produce output. The output reaches customers. The naive approach trusts the output: the agent generates text, the text is sent; the agent generates an answer, the answer is delivered. Facio's output validation discipline gives agents structured mechanisms to validate their own output before it reaches customers: schema validation for structural correctness, factual validation against source context, policy validation for compliance, consistency validation for internal coherence, and safety validation for dangerous output prevention. Mistakes are caught before they reach.

Product

Facio's Backpressure Discipline: How AI Agents Adjust to Capacity Without Drowning the System

Jul 27, 2026Product

Facio's Backpressure Discipline: How AI Agents Adjust to Capacity Without Drowning the System

AI agents produce work. The downstream — databases, APIs, humans, dependent agents — has capacity limits. The naive approach floods downstream regardless, causing cascading errors, latency spikes, and human review burnout. Facio's backpressure discipline gives agents mechanisms to sense downstream capacity and adjust production rate: real-time capacity sensing, adaptive throttling, queue management with overflow strategies, flow control signals, and backpressure-aware retries. The agent flows at the pace the system can absorb.

Product

Facio's Rollback Discipline: How AI Agents Undo Their Own Mistakes Without Making Things Worse

Jul 26, 2026Product

Facio's Rollback Discipline: How AI Agents Undo Their Own Mistakes Without Making Things Worse

AI agents take actions. Some actions are wrong: a customer is charged twice, an email goes to the wrong person, a deployment breaks production. The naive approach accepts the bad state and moves on. Facio's rollback discipline gives AI agents structured mechanisms to undo their own actions: reversible action design, automatic rollback triggers, compensating transactions for multi-step workflows, confirmation and verification, and learning loops that improve behavior. Bad state is reversed quickly; the system recovers cleanly.

Product

Facio's Tool Surface Discipline: How AI Agents Get Exactly the Capabilities They Need Without Exposing the Entire System

Jul 25, 2026Product

Facio's Tool Surface Discipline: How AI Agents Get Exactly the Capabilities They Need Without Exposing the Entire System

AI agents gain power through tools. The naive approach exposes every tool to every agent. The risk: an agent that's supposed to handle customer support questions can also trigger a payment refund; an agent compromised via prompt injection has the keys to the kingdom. Facio's tool surface discipline gives every agent a controlled capability set: per-agent allowlisting, argument scoping, context-aware dynamic loading, complete invocation logging, and anomaly detection. The agent is powerful enough to do its work; constrained enough to not be a liability.

Product

Facio's Memory Hierarchy Discipline: How AI Agents Decide What to Remember, What to Forget, and What to Surface at the Right Moment

Jul 24, 2026Product

Facio's Memory Hierarchy Discipline: How AI Agents Decide What to Remember, What to Forget, and What to Surface at the Right Moment

AI agents accumulate state: conversation history, tool results, intermediate reasoning, user preferences, learned facts. The naive approach stores everything and hopes the model sorts it out. The disciplined approach treats memory as a hierarchy with explicit retention policies, decay functions, relevance scoring, and surfacing mechanisms. Facio's memory hierarchy discipline gives AI agents the structural framework to remember what matters, forget what doesn't, and surface the right information at the right moment without overwhelming the context window.