Building Great AI Agents Is a Path Exploration Problem (verdik.substack.com)

The author reframes building conversational AI—especially for sensitive domains like mental-health counseling—as a path-exploration/search problem: at each turn the agent must choose a strategy and delivery from a large space of possible continuations, and success is measured over an entire session (Good Strategy + Good Delivery = Good Session). To make this tractable they shrink the decision space by forcing the model to select from a fixed set of intents (~50 labels), apply phase-aware constraints (discovery vs exploration), and aggressively prune improbable or harmful paths. This yields clearer directional control, observability, and focused path exploration while countering the default “mean of the internet” blandness and sycophancy of vanilla LLM output. Technically, the piece gives concrete levers: algorithmic gains (a domain-scoring heat penalty that discourages repeated intents and enforces cooldowns), and data augmentation (synthetic example generation seeded by a small hand-crafted set). A back‑of‑the‑envelope calculation—15 agent decisions/session × ~8 strategies/turn → ~34K nodes—shows full coverage would need ~338K examples, but topic focusing and Pareto principles can reduce that to ~22K high‑quality synthetic records. The approach aligns with the “Bitter Lesson” (favor search/learning) and emphasizes rigorous evaluation, phase-appropriate decisioning, and iterative engineering+data work to produce safer, more effective agents.