Part 1 The problemWhy teams need this
01 · THE PROBLEM
Automation is only as good as what happens when it breaks. Most teams find out from an angry customer, then spend an hour reading logs. This makes failure boring: the obvious ones fix themselves, and the rest reach the right person with the diagnosis attached.
Part 2 How it worksWhat it does, step by step
02 · WHAT IT DOES
Every other workflow points here when it fails. The failure is checked against known incidents so one outage never becomes fifty alerts. A new failure has its execution data pulled and an AI classifies it as transient, a data problem or a real bug. Transient ones are retried automatically after a pause; everything else gets an alert that already names the cause.
03 · HOW IT RUNS
Step by step, as built.
Catch every failure
An error trigger receives the failure from any workflow that points to it.
Don't alert twice
The incident is checked against known ones, so repeats bump a counter instead of paging you again.
Diagnose
Execution data is fetched and Claude classifies the error against a strict schema.
Retry what's safe
Transient failures wait 60 seconds and are retried automatically.
Escalate the rest
Everything else raises an enriched alert; anything that keeps failing escalates to the owner.
04 · TOOLS AND APPS
Built around the systems already in the process.
05 · WHEN SOMETHING BREAKS
Failure is designed in.
- 9nodes retry automatically when an external API fails.
- 3decision points (IF or Switch) check the data before it moves on.
- ✓Anything unhandled triggers our central error workflow, so a crash gets reported instead of failing silently.
Standard on every build
- Schema validation before downstream writes
- Retry and error routes for external API failures
- Duplicate-safe processing and idempotent updates
- Human approval where the action carries business risk
- Execution logging for support and audit review
Part 3 The impactWhat it's worth, and how we'd build yours
06 · PROJECTED IMPACT
What it should change in the business.
Projections for a typical deployment. The calculation below shows the math, and you can put in your own numbers.
07 · ROI CALCULATION
How it pays back in your business.
The starting numbers are a hypothetical deployment sized to the projections above. Change any of them to your own volumes and costs, and the math updates underneath.
- Full-time equivalent freed
- 0.1 people
- Gross value
- $8,640
- Running cost
- −$240
- Return per $1 of running cost
- $36
The math: 60 workflow failures × 30 min × 12 months × 60% ÷ 60 = 216 hours a year × $40/hour = $8,640. Net value = gross value − $240 running cost a year.
08 · HOW WE'D BUILD YOURS
How we'd build yours.
- Discover: map the current process, systems, volumes, owners and exceptions.
- Design: define the canonical data model, approvals, retries and system boundaries.
- Build: implement credentials, nodes, validation and observable error routes.
- Prove: run controlled data through success, duplicate and failure scenarios.
- Operate: publish runbooks, ownership and measurable service levels.
