performance-review

Performance review

Turn a Quickchat AI Agent's analytics into a short, decision-ready review.

Before you start

• Call list_scenarios to resolve which Agent the user means. Exactly one Agent -> use it; otherwise ask which one.
• Confirm the period. Default: the last 7 days vs the 7 days before it.

Steps

1. Use compare_periods to compare the current period with the previous one of equal length. Pass the PREVIOUS (older) window as period_a and the CURRENT window as period_b: deltas are computed as period_b minus period_a, so this makes a positive delta mean "up versus the previous period." Returns both overviews plus per-metric deltas.
2. Use get_topics for the customer-intent split, and read topics_by_day from the overview for free-text themes that are rising.
3. Use get_csat for satisfaction and get_ttfr for human handoff speed (only if the Agent hands off).
4. For any count, rate, total, or trend, ALWAYS use the analytics tools above. Never page through list_conversations to compute an aggregate.

Read the numbers correctly

• resolution_rate already combines confirmed and assumed resolutions; report it as a percentage of conversations.
• get_csat empty means no CSAT was received, NOT a low score — say "no CSAT data" rather than implying dissatisfaction.
• get_ttfr measures HUMAN responders after a handoff, in seconds, not business-hours adjusted; prefer the median and note overnight gaps inflate the average.
• See references/metrics-glossary.md for the full field reference.

Output

1. One-line headline: better or worse, and why.
2. A compact table: this period vs last, with deltas, for volume, resolution rate, CSAT, and handoffs.
3. Rising topics worth attention.
4. 2-3 concrete, prioritized recommendations. Keep it scannable; lead with the metric that moved most.