Why this matters
Observability is not about beautiful dashboards. In AI and quant, it is the mechanism that separates signal from a convincing story.
Agents need to expose cost, latency, context, tool calls, and output quality. Backtests need to expose assumptions, slippage, costs, windows, and regimes. Without that, the system looks intelligent until it meets production or the market.
Principles
- Collect less, with more context.
- Measure cost and latency as part of the answer.
- Treat logs, metrics, traces, and backtest results as internal products.
- Prefer reproducibility over convenience.
Proposed architecture
agent/backtest -> events -> traces / metrics / results
-> open storage
-> analysis and visualization
Trade-offs
The cost of a minimalist stack is that you need to make some decisions a closed product would make for you. The upside is knowing where money, latency, and complexity are going.