Agentic systems inherit every classic distributed systems failure mode plus one they don't have a name for yet: an agent can fail completely and still sound like it succeeded. A look at which reliability patterns transfer, and which gap you have to close yourself.
A one-model-per-pod architecture is simple, reliable and cost-effective until it isn’t. This article explores where that approach starts to break down, why those limits aren’t obvious at first and the engineering trade-offs that emerge as ML platforms scale.
AI agents love searching everything in parallel. Your token budget probably doesn’t. A practical look at where the cost actually comes from in multi-agent systems and how to reduce it.
A “one-click” ML observability feature looked like an easy win until we modeled the cost at production scale. What seemed like basic logging turned into a high-volume data pipeline with non-trivial latency and a surprising price curve.
We enabled tracing on a production ML system expecting a small latency overhead. Instead inference latency increased by ~700ms — far beyond the documented range, revealing how benchmarking assumptions may break under real workloads.
An end-to-end implementation of a UCITS‑compliant quarterly momentum rotation strategy, complete with Bayesian parameter tuning, walk‑forward validation and practical insights from real‑world retail constraints.
Analysis of Hierarchical Risk Parity (HRP) for crypto portfolios. Comparing HRP variants vs Bitcoin holding over 3 years with surprising results on portfolio optimisation strategies.