Deep Insight

Agent Mode (activated by Deep Insights) turns Kaiya into an always-on AI Analyst that plans, executes, validates, and explains multi-step analysis from a single question. It combines the flexibility of agents with the consistency of workflows so you get 20× deeper insights and recommended next steps.

1. Go to Kaiya Conversational AI.

2. Click the Deep Insights button (left of the query bar).

3. Select your Business View from the dropdown. If you’re unsure about the required Business View, feel free to leave it at “All Business Views”.

4. Type your question and press Enter (or click on Submit).

Deep Insights can be toggled per question; you can run standard chat for simple asks and switch on Deep Insights for multi-step deep dives.

Who is it for?

• Business users who want answers that go beyond chat (timing, magnitude, why, and what to do next).

• Analysts who need repeatable deep dives without manual orchestration.

• Data teams that require governed, audited execution across SQL and Python.

How Agent Mode works (at a glance)

A simple loop, Ask → Plan → Execute → Validate → Explain → Save, turns complex questions into governed, multi-step analysis. A multi-agent planner picks SQL or Python per step, applies transparent checks (samples, joins, fiscal calendar), then returns a clear narrative with next steps.

Plan

• A multi-agent planner breaks the question into steps.

• Chooses the right tools: SQL, Python, and visualization.

• Applies sensible defaults and only asks brief clarifications when necessary.

Execute

• SQL: Runs warehouse-native work—joins, subqueries, dynamic formulas, nested aggregations.

• Python: Handles methods SQL can’t. Changepoints, contribution/variance with volume weighting, sample-size checks, cohorts/retention, correlation/association, causal probes, forecasting with intervals, segmentation/clustering.

Validate & Reflect

• Checks whether each group has a big enough sample so results aren’t noisy.

• Prevents bad joins that duplicate rows (fan-out/chasm traps).

• Keeps the join at the correct grain so numbers add up.

• Uses your fiscal calendar so results respect your business timeline.

Explain

• Delivers timing and magnitude, ranked drivers, and relevant charts/tables.

• Includes concise recommended next steps.

Example walk-through

Consider this question: “How did cancellation rate change week-over-week in Q3, and which regions/products/channels contributed most to the WoW delta?”

Step 0: Ask You ask the question in plain English. If a detail is missing (e.g., fiscal vs. calendar weeks), Kaiya asks clarification once, then proceeds.

Step 1: Build the baseline Kaiya pulls governed data at weekly grain for Q3, honoring row-level policies and your fiscal calendar. It computes the conversion rate series and applies obvious filters (e.g., exclude tests). Outputs are audited and preview-able so you can confirm scope.

Step 2: Create analysis slices Kaiya enriches the baseline with Region / Product / Channel via governed joins. It generates only the slices relevant to the question to avoid fan-out and noise. Sample sufficiency and grain consistency checks ensure stability.

Step 3: Apply the best method Based on intent (“why + drivers”), Kaiya runs variance/contribution analysis and changepoint detection; routes heavy work to SQL and advanced methods to Python. Guardrails enforce sensible defaults and minimum samples; confidence cues are added where possible. You get a ranked, interpretable driver table aligned to the question.

Step 4: Validate & reflect Kaiya verifies enough data, correct join grain, no double counting, and correct week mapping. All defaults used are disclosed; if something looks off, it adjusts filters/grain/method and reruns the affected step.

Step 5: Explain & package You receive a plain-English narrative with timing & magnitude of the drop, ranked drivers by region/product/channel, clear visuals, and recommended next steps (e.g., staffing fix, promo tweak). All assumptions, steps, and artifacts are disclosed for auditability; you can save this run as a reusable workflow.

Supported data sources for Agent Mode and Text-to-SQL: Snowflake, Redshift, ClickHouse. Spark is not yet supported. Please migrate critical datasets to a supported warehouse for these features.