Lab notes
Lab notes
Shorter pieces: a concept explained, a distinction made, a comparison drawn. Where an engineering article argues a design, a lab note explains the thing the design is about.
- 5 min
Designing human-in-the-loop AI systems
Adding a human is easy. Adding one whose attention is actually worth something is the design problem.
- 5 min
How AI agents will interact with business software
There are four routes into a business system, and they are strictly ordered by preference. Most disappointment comes from taking the wrong one.
- 5 min
MCP and the future of AI tool integration
MCP standardises how a tool is described and invoked. It does not standardise who may invoke it - and in a multi-tenant product that is most of the work.
- 5 min
Shared context across voice, chat and email
Synchronous channels differ by seconds. An email thread differs by weeks, and almost every assumption about context changes with it.
- 4 min
Why AI agents need observability
An agent can return 200 on every request and fail every conversation. Nothing in a standard dashboard will show you that.
- 6 min
From prototype to production AI agent
The demo is roughly ten per cent of the work, and it is not the ten per cent that determines whether the thing survives contact with customers.
- 5 min
The architecture of an omnichannel AI agent
Draw the line between adapter and agent in the wrong place and you have not built one agent - you have built four with a shared logo.
- 5 min
How computer use agents work
The agent sees a page or a screen, proposes an action, and checks what changed. The hard part is everything around that.
- 5 min
Voice AI and traditional IVR
An IVR is a decision tree the caller walks. A voice agent is a loop that walks itself. Both properties have consequences.
- 5 min
How AI agents manage context
The context window is assembled fresh every turn. What goes in it is a design decision, not a side effect.
- 5 min
Building AI agents that can use tools
Tool calling looks like a model capability. In practice it is an interface design problem with a model at one end.
- 5 min
From chatbots to autonomous agents
Each generation moved one decision from the author to the system. That is the whole story, and it explains what breaks next.
- 5 min
Why latency matters in voice agents
In text, a slow answer is a slow answer. On a phone call, it is a signal - and callers read it as confusion or a dropped line.
- 6 min
How realtime voice AI works
Six components, one hard constraint: none of them may wait for the previous one to finish.
- 5 min
What makes an AI agent an agent?
The word has been stretched to cover almost anything with a model in it. Here is the distinction we find load-bearing.