When building AI agents, I keep seeing the same core problems.

When building AI agents, I keep seeing the same core problems.

Especially in wellbeing / support use cases:

Tone of voice (TOV) — consistency, warmth, non-judgment across every interaction. Product quality — stability of answers, handling edge cases, knowing when to escalate. Structured memory systems — the agent needs to remember context across sessions without hallucinating past conversations.

After working on several agent implementations, here’s what I’ve found actually works:

1. Consistent TOV requires a layered prompt architecture. Not just a system prompt — but examples, guardrails, and a feedback loop that catches tone drift.

2. Quality comes from structured evaluation. Run your agent through 100+ real scenarios, score each response, and track regression. No shortcut here.

3. Memory needs to be explicit, not magic. Store structured facts (not raw conversation logs), use retrieval with relevance scoring, and always let the user correct the agent’s memory.

The teams that ship reliable agents treat them like products, not demos. They measure, iterate, and have clear quality bars before launch.