Ideas, engineering, and lessons from the field.
Practical thinking on AI, software, infrastructure, and building technology that works in the real world.
What an AI Agent Costs to Run and How to Keep It Predictable
Agent costs are driven by steps, context size and model choice, not by the number of users. Each can be measured and limited.
How to Test an AI Agent Before It Touches Real Customers
A demo proves an agent can work once. A test set built from real cases shows how often it works, and where it does not.
AI Agent Guardrails: What to Restrict and What to Approve
An agent that can act needs limits that do not depend on the model behaving. A practical way to sort actions into free, approved and forbidden.
When a Chatbot Should Hand Over to a Person
The handover is the part of a chatbot customers remember. Clear triggers and a handover that carries the conversation make the difference.
How to Prepare Your Documentation for an AI Chatbot
A chatbot can only be as good as the content behind it. A short content audit before the build prevents most quality problems later.
Why AI Chatbots Give Wrong Answers and How to Fix Retrieval
When a chatbot answers wrongly from your own documents, the model is rarely at fault. The search step in front of it usually is.
Measuring Chatbot Quality: What to Track After Launch
Launch is the start of the work on a chatbot. A small set of measures and a weekly review keep quality from drifting.
No-Code Automation or Custom Build: How to Decide
No-code tools are the right choice more often than developers admit, and the wrong one more often than their marketing suggests.
Document Processing with AI: Invoices, Forms and Email
AI can read documents that older tools could not. What makes it dependable is the checking around the model, not the model alone.
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