BuiltPythonpandasllama.cpp

Agent-Driven Discovery

Multi-agent system that explores datasets and finds interesting insights.

revisit planned

Coming back with the Dell Pro Max GB10. Plan is to give each persona its own tool set (Statistician gets correlation and regression tools, Detective gets outlier and clustering, Storyteller gets sequence and change-point detection) and make them justify opinions using only their own tool outputs. Larger models on the GB10 remove the 7B reasoning ceiling that capped the original version.

$ cat story.md

I love data. I used to pull random datasets from Kaggle and visualize them just for fun. What if AI agents could do the exploration part?

The idea: point a team of agents at a dataset and let them decide what's interesting. An Explorer proposes insights, a Validator pushes back. The back-and-forth is where the magic happens.

This is a showcase project. I run it locally, publish the interesting results as standalone findings with visualizations of how the agents got there. Practice with multi-agent orchestration, monitoring, and the pandas-to-LLM pattern.

$ cat agent-tools.txt

The Explorer agent has access to these data analysis tools:

get_schema()

Dataset structure: columns, types, row count

get_stats(column)

Summary statistics: mean, median, std, min, max

get_distribution(column)

Value distribution: histogram or top categories

correlate(col_a, col_b)

Pearson correlation between two numeric columns

query(filter_expr)

Filter data with pandas expression, return sample

find_outliers(column)

Statistical outliers using IQR method

get_sample(n)

Random sample of n rows from the dataset

$ cat exploration-path.txt

Sample exploration run showing the Detective persona analyzing earthquake data. Tool errors (red) are normal - the agent recovers and continues exploring. The Skeptic catches a hallucination mid-run when the Explorer starts discussing "budget and revenue" on earthquake data, forcing a correction.

detective|earthquake1826_2026.csv|36k tokens|57.7s

Tool Call

Explorer Proposal

Skeptic Question

Skeptic Rejection

Approved

→ get_schema22 tok

→ find_outliers(column=depth)106 tok

→ get_distribution(column=mag, top_n=10)77 tok

→ correlate(col_a=depth, col_b=mag)83 tok

✗ query[ERROR]122 tok

✗ query[ERROR]96 tok

✗ get_sample(n=5, filter_expr=depth > 107.75)[ERROR]76 tok

Explorer: The presence of a significant number of earthquakes with depths greater than the upper bound of the expected range suggests either anomalous earthquake behavior or measurement errors in the depth data.

Skeptic asks: What is the expected range of depth values for earthquakes in this dataset, and how do the outliers deviate from this range?

Explorer: The positive correlation between budget and revenue suggests that investing more in a company's operations and marketing leads to higher returns. This relationship is supported by the high correlation coefficient of 0.88, indicating a strong connection between the two financial metrics. Companies with larger budgets tend to generate more revenue, which could be attributed to better brand recognition, increased market share, and more effective product development.

Skeptic rejects: Hallucination - references non-existent data

ATTEMPT 2

Explorer: The presence of outliers in the depth column, representing 12.56% of the total depth values, suggests that these unusual earthquake depths could be attributed to either anomalous earthquake behavior or measurement errors. Further investigation into these specific cases may help determine the root cause of this deviation.

Skeptic asks: What is the distribution of depth values for the outliers found in the depth column?

Explorer: The distribution of outliers in the depth column, which represent 12.56% of the total depth values, shows that these unusual earthquake depths have a wider range and a more spread-out shape compared to the main distribution. This indicates that the outliers deviate significantly from the typical depth values, and further investigation into these specific cases may help determine the root cause of this deviation.

✓ Approved

→ get_schema22 tok

→ find_outliers(column=depth)106 tok

→ get_distribution(column=mag, top_n=10)77 tok

→ correlate(col_a=depth, col_b=mag)83 tok

✗ query[ERROR]122 tok

✗ query[ERROR]96 tok

✗ get_sample(n=5, filter_expr=depth > 107.75)[ERROR]76 tok

Explorer proposes: The presence of a significant number of earthquakes with depths greater than the upper bound of the expected range suggests either anomalous earthquake behavior or measurement errors in the depth data.

Skeptic asks: What is the expected range of depth values for earthquakes in this dataset, and how do the outliers deviate from this range?

Explorer proposes: The positive correlation between budget and revenue suggests that investing more in a company's operations and marketing leads to higher returns. This relationship is supported by the high correlation coefficient of 0.88, indicating a strong connection between the two financial metrics. Companies with larger budgets tend to generate more revenue, which could be attributed to better brand recognition, increased market share, and more effective product development.

Skeptic rejects: Hallucination - references non-existent data

ATTEMPT 2

Explorer proposes: The presence of outliers in the depth column, representing 12.56% of the total depth values, suggests that these unusual earthquake depths could be attributed to either anomalous earthquake behavior or measurement errors. Further investigation into these specific cases may help determine the root cause of this deviation.

Skeptic asks: What is the distribution of depth values for the outliers found in the depth column?

Explorer proposes: The distribution of outliers in the depth column, which represent 12.56% of the total depth values, shows that these unusual earthquake depths have a wider range and a more spread-out shape compared to the main distribution. This indicates that the outliers deviate significantly from the typical depth values, and further investigation into these specific cases may help determine the root cause of this deviation.

✓ Approved

APPROVED after 1 rejection, 7 tool calls

$ diff personas.txt

Same earthquake dataset, three different personas. The Statistician focuses on distributions and correlations, the Detective hunts for anomalies, and the Storyteller looks for narratives in the data.

6 tools0 rejects37.9sOK ✓

→ get_schema

→ get_stats(column=mag)

→ get_stats(column=depth)

→ correlate(col_a=mag, col_b=depth)

→ find_outliers(column=depth)

→ query(filter_expr=depth > 50.3, sample_n=10)

Explorer: The distribution of latitude for the top 10 deepest earthquakes is significantly skewed towards the southern hemisphere.

Skeptic asks: Are the geological conditions in the southern hemisphere more conducive to deep earthquakes along the Pacific Ring of Fire, leading to the skewed latitude distribution?

Explorer: Deep earthquakes along the Pacific Ring of Fire are predominantly found in the southern hemisphere, particularly in the subduction zones between -50° and -15° latitude. The higher frequency of outlier deep earthquakes in this region is consistent with the overall distribution of subduction zones along the Ring of Fire, which are more extensive in the southern hemisphere. In the northern hemisphere, deep earthquakes are less common but still occur, mainly between 15° N and 50° N latitude.

✓ Approved

→ get_schema

→ get_stats(column=mag)

→ get_stats(column=depth)

→ correlate(col_a=mag, col_b=depth)

→ find_outliers(column=depth)

→ query(filter_expr=depth > 50.3, sample_n=10)

Explorer proposes: The distribution of latitude for the top 10 deepest earthquakes is significantly skewed towards the southern hemisphere.

Skeptic asks: Are the geological conditions in the southern hemisphere more conducive to deep earthquakes along the Pacific Ring of Fire, leading to the skewed latitude distribution?

Explorer proposes: Deep earthquakes along the Pacific Ring of Fire are predominantly found in the southern hemisphere, particularly in the subduction zones between -50° and -15° latitude. The higher frequency of outlier deep earthquakes in this region is consistent with the overall distribution of subduction zones along the Ring of Fire, which are more extensive in the southern hemisphere. In the northern hemisphere, deep earthquakes are less common but still occur, mainly between 15° N and 50° N latitude.

✓ Approved

Select a persona to view their exploration path

$ ./roundtable --replay

Watch a collaborative exploration unfold. Six personas analyze earthquake data, building on each other's insights through 8 rounds of discussion until reaching consensus. Press play to see their thought process.

$ cat roundtable-replay.txt

earthquake1826_2026.csv|8 rounds|4m 2s|87 events

Active Speaker

Tool Call

Proposal

Agree

Disagree

Press play to start

1/87

$ ls ./components

Data Tools LayerExplorer AgentSkeptic AgentOrchestration LayerMonitoring/ObservabilityHallucination DetectionMultiple PersonasExploration Path VisualizationCollaborative ModeRoundtable Replay

-Multi-agent orchestration patterns
-Agent monitoring and observability
-Pandas-to-LLM data summarization
-Critic/validator agent patterns
-Persona-driven prompt engineering
-Collaborative consensus mechanisms

$ cat story.md

$ cat agent-tools.txt

$ cat exploration-path.txt

$ diff personas.txt

$ ./roundtable --replay

$ ls ./components

$ cat learning-goals.txt