Analysis of 14 production agent workflows reveals the optimal balance between deterministic code & LLM inference. 65% of nodes run as pure code, saving tokens & improving reliability.
Practical lessons from a year building AI agent systems : prototype with frontier models, fine-tune for stable tasks, use static typing, & run competing agents as critics.
The most capable AI models now spend their cycles orchestrating other tools. Specialization creates startup opportunity : build the specialists the executives call first.
Rather than manually debugging AI failures, I built a Ralph Wiggum loop that pushes the model against its failures each night until it dreams a correct solution.
Analysis of internal Asana data reveals a statistically significant 'Agent Inflection Point' on Oct 12, 2025, where daily automated task volume tripled & shifted to higher-value work.
