Engineering deep dives, research commentary, product updates, and clear-eyed perspectives on multi-agent AI, alignment, and what intelligent systems actually require to work reliably.
When one agent fabricates a fact, how does that error travel through a chain of downstream agents? We ran 10,000 simulations to find out — and the results changed how we design every system we ship.
How we built a routing layer that sends tasks to the right-sized model based on complexity signals — without degrading output quality.
A deep look at the mechanisms by which user intent erodes as tasks pass through orchestrator → sub-agent → tool layers.
A technical overview of how Brainspawn handles task decomposition, agent communication, and result synthesis at scale.
What happens when agents with different specializations and confidence levels disagree? How do you resolve it without losing signal from minority positions?
Data residency, compliance, and latency requirements mean cloud-only AI isn't enough. Here's what a real on-prem deployment looks like.
The difference between AI companies that treat safety as a checkbox and those that build it into the architecture from day one — and why it matters.
