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Retries without idempotency create duplicates.
A webhook fires twice. An agent retries a write. Your CRM now has two contacts and two follow-ups. The dashboard still says green.
Before you add another AI step, lock three basics.
Use an idempotency key so the same event cannot create the same record twice. Store it with the write, not only in the request log.
Set explicit retry rules with backoff and a hard ceiling. Infinite loops hide the real failure and burn rate limits.
Give failed events a dead-letter path a human can actually see. Quiet failures are more expensive than loud ones.
I treat reliability as part of the product, not a polish pass after launch. The workflow should stay clean when traffic spikes and tools hiccup.
Message me if you want a workflow that holds up under real traffic, not only in a happy-path demo.
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I built LumaFlow for a fictional Chicago cleaning business, LumaClean.
The problem was simple: too much back-and-forth just to turn a customer inquiry into a confirmed booking.
So I built a workflow that handles the journey:
→ Instant quote
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→ $40 deposit
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→ Rescheduling
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The goal wasn't to build another pretty booking page.
It was to give the owner less work to do.
Vert nice
Before adding AI to your SaaS, write the failure contract.Most AI feature briefs describe what should happen when the model works.Production problems begin when it doesn’t.Before adding AI to an existing SaaS product, I think the team should define: 1. LatencyHow long can the user wait before the product falls back, continues asynchronously, or offers another path?2. UncertaintyWhat happens when the output is incomplete or confidence is too low? Does the product ask for clarification, request human review, or refuse the action?3. PermissionsWhich records can the model access for this user, role, and organization? AI should inherit the product’s access rules rather than bypass them.4. Action boundariesCan the AI recommend, draft, or execute? Sending an email, changing a payment state, and updating a customer record should not share the same approval rules.5. CostWhat is the usage limit per user or tenant? Which model or workflow becomes the fallback when a request is too expensive?6. RecoveryCan the team see which model, prompt, data, and tool call produced the result? Can a failed workflow be retried without duplicating the action?Connecting an API is usually the easy part. The real product work is deciding how the feature behaves when reality does not match the demo.If you are building AI into a product, which part is hardest to define: latency, permissions, human approval, cost, or recovery?
Recovery is the one I find hardest to define. If an email goes out and the next step fails, I want the retry to know it was sent so the customer does not get it twice.
I conceptualized and engineered this interactive manuscript recovery interface for Palimpsest Lab .
The build bridges the gap between archival scholarship and technical UI, pairing classical book typography with real-time multispectral simulation. Crafting a live wavelength scrubber that sweeps from 365 nm ultraviolet fluorescence to 940 nm infrared; letting visitors uncover an erased fifth-century Greek geometry treatise hidden beneath a medieval Latin prayer book; turned scientific telemetry into a hands-on story where every control has a clear, functional purpose.
