What we are exploring
The central question of this lab is narrow but hard: how far can a language model be trusted to plan and act, and where must a person stay firmly in control? We build small, observable agents that take a bounded task - drafting a reply, assembling a summary, proposing a next step - and we watch closely how they decompose the work, where they reach for tools and how they behave when they are uncertain. The point is not to chase autonomy but to map the boundary between genuinely useful assistance and risky overreach.
Because this is research, we treat every prototype as a question rather than an answer. We deliberately probe failure: ambiguous instructions, missing context, conflicting tools and adversarial inputs all feature in our experiments. What we learn shapes the guardrails - confirmation steps, scoped permissions, refusal behaviours - that any later product would need. Nothing here is a finished, deployable assistant; it is a body of evidence about what would have to be true before one could be.
How we evaluate what works
A prototype that looks impressive in a demo can still be unreliable in ways that only show up at scale, so we lean heavily on repeatable evaluation rather than anecdote. We assemble fixed task suites, replay them across versions, and record not just success rates but the shape of the failures - silent mistakes, confident hallucinations and unsafe tool calls matter far more to us than occasional slow responses. Findings are written up honestly, including the experiments that did not pan out.
We are candid that these evaluations are internal and exploratory; they are not formal certifications, benchmarks of record or guarantees of behaviour in the real world. They help us decide, with eyes open, whether a pattern is worth carrying forward into a scoped engagement. When a result is promising we say so cautiously, and when an approach is brittle we retire it rather than dress it up.
- Fixed task suites replayed across agent versions to detect regressions.
- Failure taxonomy that separates silent errors from visible ones.
- Logged plans and tool calls so every decision can be traced after the fact.
- Explicit refusal and escalation tests for sensitive or out-of-scope requests.
- Honest write-ups, including negative results and abandoned approaches.
From prototype to production
A research prototype and a production assistant are different things, and we try never to blur that line. A prototype proves a pattern can work under controlled conditions; a production system has to handle real data, real load, real edge cases and a clear accountability chain. Moving from one to the other is a deliberate, separately scoped step that adds hardening, monitoring, access controls and human approval gates appropriate to the actual task.
When research does graduate into client work, it does so through our normal services engagement rather than as an off-the-shelf product. We carry forward the guardrails learned in the lab - least-privilege tool access, audit trails, confirmation before consequential actions - and we are explicit with clients about what the system can and cannot be relied upon to do. The human stays in the loop by design, not as a temporary safeguard.
