Agent Lab

How we think about AI agents — and the working demos we built to test it.

The lab pairs two papers on agent design and evaluation with two artifact-backed demonstrations. Each demonstration shows the task, the agent decision, the trace, and the evidence used to evaluate it.

Our perspective

How we think about agents

Two questions sit under every project below: when an agent is actually worth building, and how you know it's done. These are our answers.

When Do AI Agents Actually Pay Off?

The question every AI pilot asks — "can the model do the task?" — is the wrong one. What you actually buy is verified useful output at an acceptable risk, and that turns on the cost of checking the work, not on raw capability. A framework, and a worked example where the same model pays off on one task and loses money on another.

Read the full piece

What Counts as Done?

A leaderboard score is a capability screen, not a readiness certificate. It measures the model under conditions that flatter it — full information, single-shot, outcome-only grading — while deployment readiness turns on parameters the score never estimates. How we actually measure whether an agent is ready to ship.

Read the full piece

Weighing whether an agent is worth building for a real task? Talk to us about scoping it →

Working demos

The agents in the lab

Each one takes a concrete task, builds an agent for it, and shows the work — the pattern, the reasoning, and where it's checked against a result or a simpler baseline.

Working prototype

Coverage Auditor

Hand it a dense insurance benefits document and ask a question. The agent answers with the exact clause and figure it relied on, and a separate deterministic layer re-checks every number — so the answer is grounded in the document, not the model's memory.

Review the evidence audit
Every answer pinned to its clause and page.

Replay demo

Customer Support Agent

Given a customer's support conversation, the agent works out what to do — pull up the order, check a policy, then resolve or escalate to a person. We put it head-to-head with a simpler keyword baseline on hand-scored conversations, and published every case — including the ones it got wrong.

Inspect the evaluation
Every call checked against a human-scored case.

36 cases · scored vs gold

Resolved Resolved Resolved Resolved Resolved Escalated Resolved Escalated Escalated Resolved Escalated Resolved

Coloured by action · ✓/✗ against human-verified gold