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Notes on product decisions, engineering tradeoffs, and what we're learning from running AI Agents in production.
A step-by-step walkthrough of wiring an AI Agent into a Discord server so it can answer questions without a human on call.
Retrieval alone wasn't enough. Here's the reranking layer we built to stop the model from confidently citing the wrong document.
We looked at what separated our best-performing deployments from the average ones. It wasn't the model.
We traced a single automated refund from the customer's message to the API call that actually issued it.
Traceability isn't a nice-to-have once a team is running an AI Agent in production — it's what makes production possible at all.
How a handful of support teams cover a dozen languages with the same headcount they started with.
The practical differences between cross-encoder rerankers, LLM-as-judge, and hybrid scoring — and when each one is worth the latency.
We asked the person who reviews the most AI conversations on the platform what still trips agents up.
Catalog sync, order lookups, and a working storefront widget — all before the end of your workday.
Most teams don't know what their AI Agent can't answer until a customer hits the gap. Here's how we surface it earlier.
Confidence scores aren't enough. Here's the three-tier fallback we run so an uncertain Agent never guesses out loud.
A ticket can be 'resolved' and still leave the customer annoyed. Here's the second metric we added to catch that.
Same product, three very different setups. What separated the team that trusted their Agent with refunds from the one that still reviews every reply.
Traffic on our busiest customers' stores goes up 8x overnight. Here's how we make sure the Agent doesn't fall over when it matters most.
Going from one channel and five agents watching closely to company-wide coverage doesn't happen by flipping a switch. Here's the checklist we actually followed.
Full traceability and customer privacy pull in opposite directions. Here's where we drew the line.
Model choice barely moved the number that mattered. What did was taking certain decisions away from the model entirely.
One sentence from a customer, five systems underneath. Here's what actually has to happen before that sentence turns into a real resolution.
We borrowed a classic marketing exercise — segment, target, position — to figure out how to actually divide an agent's responsibilities, instead of just giving it more personas.
Nobody asks whether an AI agent is secure until after the incident. We walked our own agent through reach, action, conversion, and ongoing engagement to find out before that happened.
None of the changes below show up in an "AI agent" pitch. Tracked as a real customer journey — see, think, do, care — they explain almost everything about whether people trust what they're talking to.
Pricing by the message measures the wrong unit. We worked through it the way you'd price any product — product, price, place, promotion — and ended up somewhere more honest.
We used the classic segment, target, position exercise to figure out where this market actually gets won — and it isn't on who has the smartest model.
The build itself was one thing. Talking about it in public — owned posts, shared reactions, the occasional earned mention — turned out to teach us almost as much as the engineering did.
