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 26-30 of 78 articles in Product.

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.

Product

Facio's Backpressure Discipline: How AI Agent Systems Slow Down Before They Break

Jul 12, 2026Product

Facio's Backpressure Discipline: How AI Agent Systems Slow Down Before They Break

Production AI agent systems face the same problem as any distributed system: capacity limits. The agents can process N tasks per minute; the upstream systems can accept M requests per second; downstream services can handle K concurrent calls. When input exceeds capacity, naive systems keep accepting work and pile it up. The pile grows until memory exhausts, queues overflow, or the system crashes. Facio's backpressure discipline gives systems the structural answer: detect overload, signal backpressure, shed load gracefully, recover when capacity returns.

Product

Facio's Cascading Timeouts: How AI Agents Bound Latency at Every Layer of the Stack

Jul 11, 2026Product

Facio's Cascading Timeouts: How AI Agents Bound Latency at Every Layer of the Stack

An AI agent's response time is the sum of many operations: model inference, tool calls, database queries, API requests, HITL pauses. Each operation has its own latency. Each operation can fail by being slow. Without discipline, a single slow operation makes the whole agent slow. Facio's cascading timeouts give teams the structural discipline to bound latency at every layer of the stack. The agent has time budget per operation; the operations chain into a budget per session; the session fails fast rather than hanging.

Product

Facio's Evaluation Harness: How AI Agent Quality Stays Measurable Instead of Vibes

Jul 10, 2026Product

Facio's Evaluation Harness: How AI Agent Quality Stays Measurable Instead of Vibes

"The agent feels slower today." "The agent seems to be giving worse answers." "The agent used to handle this edge case." These are real signals, but they're not actionable. Without measurement, the team can't quantify the regression, can't identify the cause, can't validate the fix, can't prevent recurrence. Facio's evaluation harness turns agent quality from vibes into metrics. The team has golden test sets, automated regression detection, prompt-version comparison, and quality dashboards. Here's how the discipline works.

Product

Facio's Distributed Tracing Discipline: How AI Agents Stay Debuggable When Every Step Crosses a System Boundary

Jul 9, 2026Product

Facio's Distributed Tracing Discipline: How AI Agents Stay Debuggable When Every Step Crosses a System Boundary

An AI agent's execution is a chain of model calls, tool invocations, database queries, API requests, and human-in-the-loop pauses. When something goes wrong — the agent makes a wrong decision, the workflow takes too long, the output is wrong — the team needs to trace the failure to its root cause across every system involved. Facio's distributed tracing discipline gives teams the structural observability to debug AI agents the way they debug microservices: trace IDs propagated through every step, spans for every tool call, and correlated logs across all systems.