Analyses store every step. Human-readable. Reproducible.
Each block in an analysis shows exactly what the agent did. Text, charts, and conclusions are stored together with the code that created them. No black boxes, full transparency.
1 margin_yoy = (
2 df["margin"].pct_change(4) * 100
3 ).iloc[-1]
4 cost_by_quarter = df.groupby("quarter")["cost_per_unit"].mean()
5 cost_trend = cost_by_quarter.diff().iloc[-1]
6 findings = pd.Series({
7 "margin_yoy_pct": margin_yoy,
8 "cost_trend": cost_trend
9 })1 margin_by_quarter = (
2 df.groupby("quarter")["margin_pct"].mean()
3 )
4 fig = margin_by_quarter.plot(
5 kind="line",
6 title="Margin trend"
7 )- Margin compression: cost +24%, price +5%
- Seasonal: Q3 spike, Q4 recovery
- Elasticity: limited pricing room
Reproducibility
Every block shows how it was generated
Analyses store each step in a human-readable format. Every text and graph block has an associated codeblock that shows exactly how the agent produced it. Auditable, reproducible, shareable.
Key findings
Margin improved by 12% YoY. Cost per unit trend is rising; sales price holds. Recommend reviewing pricing in Q2.
margin_yoy = (
df["margin"].pct_change(4) * 100
).iloc[-1]
cost_by_quarter = df.groupby("quarter")["cost_per_unit"].mean()
cost_trend = cost_by_quarter.diff().iloc[-1]
findings = pd.Series({
"margin_yoy_pct": margin_yoy,
"cost_trend": cost_trend
})Margin trend
margin_by_quarter = (
df.groupby("quarter")["margin_pct"].mean()
)
fig = margin_by_quarter.plot(
kind="line",
title="Margin trend"
)Conclusion
Overall margin trend is positive. Next step: run pricing scenario analysis to identify optimal price points.
blocks = [findings, margin_by_quarter]
combined = pd.concat(
blocks,
axis=0
)Agents & Analyses
Agents produce and run the code. Analyses store it all.
Your analyses store every step agents take in a format you can read and verify. Text summaries, charts, and conclusions each come with the code that generated them, so you see the full chain from data to result.
- Structured blocks: text, charts, conclusions. Each step the agent takes is stored in the analysis in a clear, human-readable form.
- Every block stores the code or tool call that created it. One click to see how that step was generated.
- The execution trace links each agent step to the blocks in your analysis. Full visibility: every step stored, human-readable, reproducible.
Why datalyft
Every step stored, human-readable and reproducible
Analyses capture every step your agents take and store them in a clear, human-readable format. See how each result was produced, re-run it, or share it.
- Agentic, not just automated
- Multiple specialized agents work in a defined topology. Each step they take is stored in your analysis in a human-readable, reproducible format.
- Configure once, run on schedule
- Set daily, weekly, monthly, or custom schedule. Each run produces an analysis that stores all steps in human-readable form, delivered when it matters.
- Every step stored, human-readable
- Text and graph blocks show exactly how they were generated. Every agent step is stored in the analysis so you can verify, re-run, or share.
- Transparency and control
- Every analysis stores the full chain of steps in a human-readable format. Execution trace, step-by-step view, and saved analyses with codeblocks. One place to configure workflows and see run history.
Get access
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- Always-on analytics that works for you.
- Configured once. Continuous Insights.
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