Research

How do you control the way an AI system approaches a problem?

Our research is connected directly to how we build. Each finding answers three questions: What did we learn? Why does it matter for AI systems? What changes in practice?

TopicsWhat we study

What we study.

Reasoning proceduresDecomposition and recompositionBehavioral controlExpert workflowsSynthetic personasMulti-agent systemsModel policy versus capabilityEvaluation methodologyHuman–AI cognitive systemsPsychological safetyExplainabilityFailure modes
FindingsMeasured, not claimed

What changes the work — and what only changes the wording.

Generic / demographic persona 80–96% hedge
“maybe”
With psychological structure 26–46% hedge · 1.8–4.1× spread
books demo objects walks negotiates
Structure vs. labels

What we learned. Generic and demographic-only personas sit on the fence 80–96% of the time. With explicit psychological structure, hedging drops to 26–46%, and the spread of distinct decisions widens 1.8×–4.1×.

Why it matters. A role label changes the wording more than the decision. Systems built on labels produce cautious, interchangeable output.

What changes in practice. We specify the procedures behind a perspective and keep only the parts that measurably change the decision, not just the words.

Depth vs. naturalness

What we learned. Adding structure did not make output more synthetic: telltale AI artifacts fell from 12.9 to 3.3 per response, and flat “no real tension” answers dropped from 45% to 4%.

Why it matters. The common worry is that engineered behavior reads as artificial. Measured, the opposite held.

What changes in practice. We measure both task behavior and naturalness, instead of trading one for the other.

Several perspectives vs. one model

What we learned. Checked against a real advertising regulator’s own decisions, a committee of structured perspectives caught about 9 in 10 of the claims the regulator flagged — more than the strongest single model, especially on implied claims.

Why it matters. One pass from one perspective reads right past what a differently-oriented reviewer would catch.

What changes in practice. For review work, we compose several explicit review procedures rather than relying on one generalist pass.

Behavior is measured across Claude, GPT, Gemini, Grok and Llama model families. Each result is graded by confidence and checked against the published literature, with its limits stated alongside it.

Public evidenceLong-running indexes

Public, repeatable evidence.

WritingStudies & case notes

Studies, case notes and methods.

10
Expertise changes what AI notices
We reconstructed how experienced human coders decide and gave that to a low-cost model. It reached Cohen's κ 0.64–0.68 against their labels. Frontier models given the coding manual reached 0.50–0.59 on the same sentences. Changing the way of thinking moved agreement by +0.17–0.18 κ; changing to a stronger model moved it by +0.01–0.05 κ.
Oct 9, 2026
18 min read
AI evaluationannotation
09
AI is getting better. Just not evenly.
We followed 353 AI models over three years of human votes. Every model gets better at everything. But work with a checkable answer, like code and math, pulls ahead, while writing and other judgement work falls further behind. Public benchmarks can't show this, because they mostly measure what is easy to check.
Oct 7, 2026
13 min read
AI evaluationmodel selection
08
Workflow is a contract. Orchestration is control.
Notes on managing multi-agent work: define the workflow and its acceptance criteria first, then give an orchestrator authority to dispatch, inspect, reject, and redirect specialist agents and personas.
Aug 24, 2026
7 min read
OrchestrationWorkflow design
07
A behavioural safety audit of a live wellbeing companion: findings and recommendations
We ran a battery of synthetic users against AURI, a live wellbeing companion, over 50 conversations of 15 turns each. This is what we found, and what we would recommend on each finding.
Aug 5, 2026
22 min read
AI safetyvulnerable users
06
The claims one AI can't see
We tested a committee of AI models against the single best model available, on a public benchmark: catching every claim a real advertising regulator flagged on a marketing page. The committee won, especially on the implied claims a single AI misses.
Jun 22, 2026
5 min read
synthetic personasAI evaluation
05
Which seat on the buying committee leaves your page
A B2B page isn't read by one person. We put a simulated buying committee in front of a live page to see which seat left before the demo — and why a single pass of a frontier model missed it.
Jun 16, 2026
6 min read
synthetic personascommittee
04
Precision engineering for synthetic psychology
We treated each Big-Five trait of a synthetic persona as a dial and measured what happens when you turn one. The other traits move too, an effect we call crosstalk, and it shows how a character's traits are wired together.
Jun 11, 2026
7 min read
synthetic personasAI psychology
03
What a strategic-advice ablation reveals about AI
We asked one AI model the same strategic question three ways: vanilla, full persona architecture, and persona with the cognitive mechanisms stripped out. The ablation reveals why surface prompt engineering drifts back to hedged, category-level answers — and what architecture is needed to escape it.
Apr 19, 2026
8 min read
AI strategysynthetic personas
02
Trajectory safety observatory for vulnerable-user interactions
We tested seven AI models with three simulated vulnerable personas over 18 turns each. The failures were quiet, warm, and dependency-forming rather than dramatic.
Mar 5, 2026
13 min read
AI safetyvulnerable users
01
Why synthetic personas need architecture, not just prompts
Most AI personas are surface decorations. We explain why treating personas as testable architectures — with measured traits, cross-model validation, and stress testing — produces fundamentally different results.
Mar 3, 2026
3 min read
synthetic personasAI psychology