Fail-Safe Path
A predefined fallback route that keeps workflows in a safe state when a dependency or model misbehaves.
A fail-safe path is a planned fallback when automations cannot proceed safely—rerouting to manual review, pausing writes, or sending minimal notifications. It prevents bad or partial actions.
In business flows, it handles dependency outages, bad data, or low-confidence AI outputs. Instead of failing silently, it moves work to a safe queue or human.
Fail-safe paths are embedded in workflows as branches with clear triggers and owners. They limit blast radius, preserve data integrity, and keep customers informed during incidents.
Frequently Asked Questions
When should a workflow take the fail-safe path?
On repeated downstream failures, validation errors, low-confidence outputs, or breach of time/cost budgets. Define explicit conditions.
What should a fail-safe path do?
Stop risky writes, log context, notify owners, and queue items for manual handling. Provide enough data to resume later.
Who owns the fail-safe queue?
Assign a clear team/role to triage, fix, and reprocess. Ownership prevents backlog and dropped items.
How do I test fail-safe paths?
Simulate dependency failures, schema mismatches, and low-confidence outputs in staging. Verify logging, alerts, and recovery steps.
Should customers be notified?
If impact is user-facing, send concise status updates and expected timelines. For internal flows, alert operators and stakeholders.
How do fail-safes affect SLAs?
They may pause or slow delivery. Track SLA breaches separately and include fail-safe time in incident reporting.
Can fail-safe paths be automated?
Yes—auto-queue for review, trigger backups, or switch to read-only modes. Keep a manual override to stop automation entirely.
What metrics matter?
Fail-safe activation rate, time to resolution, reprocessing success, and downstream incidents prevented.
How do I return from fail-safe to normal?
Define recovery criteria: dependency health restored, validation fixed, backlog cleared. Run a controlled re-entry with close monitoring.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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