AI and Agents
An agent that shows its work—so you can verify, improve, and trust the results.
A tool loop, not a black box
The agent thinks, plans, executes tools, and iterates until the analysis is complete. You see every step—queries written, cells created, dependencies formed.
- Extended thinking (up to 8,000 tokens of reasoning) before acting.
- Executes tools in a loop: create cells, run queries, build pipelines.
- Stops when the answer is complete or asks for clarification.
Here's what I found across your 5 active campaigns:
Top performer: Product Hunt (Organic) — 528 conversions at $0 spend. This is your highest-converting channel by far.
Best paid channel: Google Ads — $4,200 spend → 186 conversions ($22.58 CPA).
Underperforming: LinkedIn Brand Refresh — $3,800 for only 94 conversions ($40.43 CPA), nearly double Google's cost per acquisition.
Done! I've added a bar chart showing spend vs. conversions by channel. The LinkedIn campaign stands out as the clear underperformer — I'd recommend reallocating that $3,800 to Organic and Email, which have the best conversion rates. Want me to model what that reallocation would look like?
The agent introspects your actual database schema before writing queries. Add a semantic layer and it uses your metric definitions, not invented ones.
- Browses connections, schemas, and tables before querying.
- Uses semantic layer definitions for consistent metrics.
- References real column names—not guesses.
For destructive or high-impact actions, the agent asks before executing. You see exactly what it wants to do and approve or reject.
- Write SQL queries from natural language descriptions.
- Create, edit, and execute notebook cells.
- Build multi-step analyses with proper dependencies.
- Browse schemas and preview table data.
- Search and use semantic layer definitions.
- Create charts and visualizations.
- Hide logic behind opaque answers that can't be verified.
- Use your data to train models.
- Execute destructive actions without explicit approval.
- Pretend confidence when the schema is ambiguous.
- Trade correctness for speed.
The chat interface where you interact with the agent—embedded in notebooks so work stays executable.
Monitor agent usage across your team—see patterns, find failure modes, improve outcomes.
The method matters as much as the result
If you can't see how an answer was derived, you can't verify it, improve it, or trust it in production. 42Cells shows the work—queries, dependencies, reasoning—so analysis stays auditable and repeatable.