Labs

Practical experiments for better AI delivery.

Labs explores the tools and methods behind our work: agentic development, knowledge retrieval, context management and evaluation.

We aim to share useful methods and findings as the work develops. Nothing here is released yet, and there is no download to collect.

Research themes

Concept

Clearer task specifications for agents

Most agent failures are decided before the agent runs, in how the task was described. We are testing what a task brief needs to contain — inputs, boundaries, completion criteria, escalation — for the result to be reviewable.

Feeds into stage 03 of our ADLC.

Concept

Knowledge retrieval with source traceability and access controls

An answer is only useful if you can see where it came from and know the reader was allowed to see it. We are working on retrieval that keeps derived content linked to approved sources while respecting existing permissions.

Feeds into our knowledge systems work.

Status vocabulary: Concept · Prototype · Internal use · Released. A released item carries a usable link. A field note carries a date. We will not list either before it exists.

What we are examining

Agent harnesses

The runtime around an agent: state, tools, permissions, execution limits and evaluation.

Context engineering

Managing instructions, retrieved information, tool outputs and task state — beyond prompt writing.

Evaluations

Real questions and real tasks, scored consistently enough to notice a regression.

Model placement

Self-hosted and hybrid setups measured on quality, cost, latency and data handling.

Working on the same problems?

We are interested in comparing notes, and in the cases where these methods failed for you.

Discuss your project