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Everyone is shipping AI agents right now. Almost no one is testing them — and that is the whole problem.
A demo agent and a production agent are different animals. The demo works because you walked it down the happy path. Production breaks on the paths you never imagined.
A real example from my own work: a support assistant correctly handed a conversation to a human when someone wrote "I want to talk to a person" in Spanish — but not in English, because one phrase was missing from a rule. Every automated test passed. It only surfaced when I ran real conversations through it.
That is the gap the "agents will replace everything" wave keeps skipping. The hard part was never making an agent act. It is making it:
• say "I don't know" instead of guessing,
• hand control back to a human at the right moment,
• and prove, with tests, that it still behaves when the input gets strange.
Autonomy is cheap. Trustworthy autonomy is the real engineering.
Before you let an AI agent near real users or real data — what is the first thing you check?
Great work 👏🏻
The Spanish vs English handoff bug is such a good example. Every test passes until a real user shows up.
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Built an AI system that reads supplier invoices and posts them straight into a dealer management system. No one retypes anything anymore.
The problem: a group of car dealerships was getting hundreds of supplier invoices by email and WhatsApp photos, and staff were keying every line into their DMS by hand.
What I built:
• Email and WhatsApp intake, auto-matched to the right dealership
• AI and vision extraction of vendor, tax details, and line items grouped by vehicle
• Verification and duplicate detection before anything gets posted
• A review dashboard for the few cases that need a human
• Browser automation that posts approved invoices into the DMS
• WhatsApp auto-replies that chase suppliers for missing details
Result: 280+ invoices processed so far, with zero exceptions.
Stack: Python, LLM and vision models, React, Selenium
If your team is still copying data from documents into another system, that's usually very automatable. Happy to take a look at yours.
#AI #Automation #AIAgents
The WhatsApp auto-replies that chase suppliers for missing details are the piece I like most, since that closes the loop instead of just flagging a gap. How do you decide when a missing field gets an automatic chase versus going to the review dashboard for a person?
Pulen is a take-a-number app for a roti mochi bakery in Bandung. The bakery is fictional, the problem is real: the line starts at 6:40, people wait 45 minutes, and the cheese one sells out before nine.
You fill a box from home and pay, and your number only exists once the card clears, so nobody can hold buns with a fake order. Your paper ticket follows the oven and tells you when to start walking. In the kitchen, every tray of 36 is planned from paid boxes first. Unpaid holds, missed boxes, sold-out flavours and the owner's closing summary with an AI suggestion all run on their own.
If Bandung is asleep when you visit, press "Play the morning" to watch a whole morning in 70 seconds. To try ordering, use the Stripe test card 4242 4242 4242 4242.
Built in Base44 from 12 plain-English prompts.
I'd test the last cheese bun with two payments completing at once. Does Pulen reserve stock during checkout and release it on expiry, or allocate only after payment with a refund path if that flavour has sold out?
Recent Ai & Saas hero section designs
Ye le Jani:
Fire collection! That "Nothing moves unseen" hero with the Vigil figure and red atmosphere is so cinematic - love the serif + script mix. And the other two "Wake up to work that's already done" and AI calendar concepts nail that moody SaaS vibe. Super polished hero directions!