, we regularly want to analyze what’s happening with KPIs: whether or not we’re reacting to anomalies on our dashboards or simply routinely doing a numbers replace. Based mostly on my years of expertise as a KPI analyst, I might estimate that greater than 80% of those duties are pretty customary and will be solved simply by following a easy guidelines.
Right here’s a high-level plan for investigating a KPI change (yow will discover extra particulars within the article “Anomaly Root Cause Analysis 101”):
- Estimate the top-line change within the metric to grasp the magnitude of the shift.
- Examine information high quality to make sure that the numbers are correct and dependable.
- Collect context about inside and exterior occasions which may have influenced the change.
- Slice and cube the metric to determine which segments are contributing to the metric’s shift.
- Consolidate your findings in an government abstract that features hypotheses and estimates of their impacts on the primary KPI.
Since we have now a transparent plan to execute, such duties can probably be automated utilizing AI brokers. The code brokers we not too long ago discussed could possibly be a very good match there, as their skill to write down and execute code will assist them to analyse information effectively, with minimal back-and-forth. So, let’s strive constructing such an agent utilizing the HuggingFace smolagents framework.
Whereas engaged on our process, we are going to talk about extra superior options of the smolagents framework:
- Methods for tweaking all types of prompts to make sure the specified behaviour.
- Constructing a multi-agent system that may clarify the Kpi modifications and hyperlink them to root causes.
- Including reflection to the move with supplementary planning steps.
MVP for explaining KPI modifications
As traditional, we are going to take an iterative method and begin with a easy MVP, specializing in the slicing and dicing step of the evaluation. We’ll analyse the modifications of a easy metric (income) cut up by one dimension (nation). We’ll use the dataset from my earlier article, “Making sense of KPI changes”.
Let’s load the information first.
raw_df = pd.read_csv('absolute_metrics_example.csv', sep = 't')
df = raw_df.groupby('nation')[['revenue_before', 'revenue_after_scenario_2']].sum()
.sort_values('revenue_before', ascending = False).rename(
columns = {'revenue_after_scenario_2': 'after',
'revenue_before': 'earlier than'})Subsequent, let’s initialise the mannequin. I’ve chosen the OpenAI GPT-4o-mini as my most popular possibility for easy duties. Nonetheless, the smolagents framework supports all types of fashions, so you should utilize the mannequin you like. Then, we simply have to create an agent and provides it the duty and the dataset.
from smolagents import CodeAgent, LiteLLMModel
mannequin = LiteLLMModel(model_id="openai/gpt-4o-mini",
api_key=config['OPENAI_API_KEY'])
agent = CodeAgent(
mannequin=mannequin, instruments=[], max_steps=10,
additional_authorized_imports=["pandas", "numpy", "matplotlib.*",
"plotly.*"], verbosity_level=1
)
process = """
Here's a dataframe displaying income by section, evaluating values
earlier than and after.
Might you please assist me perceive the modifications? Particularly:
1. Estimate how the overall income and the income for every section
have modified, each in absolute phrases and as a proportion.
2. Calculate the contribution of every section to the overall
change in income.
Please spherical all floating-point numbers within the output
to 2 decimal locations.
"""
agent.run(
process,
additional_args={"information": df},
)The agent returned fairly a believable end result. We received detailed statistics on the metric modifications in every section and their affect on the top-line KPI.
{'total_before': 1731985.21, 'total_after':
1599065.55, 'total_change': -132919.66, 'segment_changes':
{'absolute_change': {'different': 4233.09, 'UK': -4376.25, 'France':
-132847.57, 'Germany': -690.99, 'Italy': 979.15, 'Spain':
-217.09}, 'percentage_change': {'different': 0.67, 'UK': -0.91,
'France': -55.19, 'Germany': -0.43, 'Italy': 0.81, 'Spain':
-0.23}, 'contribution_to_change': {'different': -3.18, 'UK': 3.29,
'France': 99.95, 'Germany': 0.52, 'Italy': -0.74, 'Spain': 0.16}}}Let’s check out the code generated by the agent. It’s superb, however there’s one potential situation. The Llm recreated the dataframe primarily based on the enter information as an alternative of referencing it immediately. This method is just not perfect (particularly when working with large datasets), as it will possibly result in errors and better token utilization.
import pandas as pd
# Creating the DataFrame from the offered information
information = {
'earlier than': [632767.39, 481409.27, 240704.63, 160469.75,
120352.31, 96281.86],
'after': [637000.48, 477033.02, 107857.06, 159778.76,
121331.46, 96064.77]
}
index = ['other', 'UK', 'France', 'Germany', 'Italy', 'Spain']
df = pd.DataFrame(information, index=index)
# Calculating whole income earlier than and after
total_before = df['before'].sum()
total_after = df['after'].sum()
# Calculating absolute and proportion change for every section
df['absolute_change'] = df['after'] - df['before']
df['percentage_change'] = (df['absolute_change'] /
df['before']) * 100
# Calculating whole income change
total_change = total_after - total_before
# Calculating contribution of every section to the overall change
df['contribution_to_change'] = (df['absolute_change'] /
total_change) * 100
# Rounding outcomes
df = df.spherical(2)
# Printing the calculated outcomes
print("Complete income earlier than:", total_before)
print("Complete income after:", total_after)
print("Complete change in income:", total_change)
print(df)It’s price fixing this downside earlier than transferring on to constructing a extra advanced system.
Tweaking prompts
For the reason that LLM is simply following the directions given to it, we are going to deal with this situation by tweaking the immediate.
Initially, I tried to make the duty immediate extra specific, clearly instructing the LLM to make use of the offered variable.
process = """Here's a dataframe displaying income by section, evaluating
values earlier than and after. The information is saved in df variable.
Please, use it and do not attempt to parse the information your self.
Might you please assist me perceive the modifications?
Particularly:
1. Estimate how the overall income and the income for every section
have modified, each in absolute phrases and as a proportion.
2. Calculate the contribution of every section to the overall change in income.
Please spherical all floating-point numbers within the output to 2 decimal locations.
"""It didn’t work. So, the following step is to look at the system immediate and see why it really works this manner.
print(agent.prompt_templates['system_prompt'])
#...
# Listed here are the principles you need to at all times comply with to resolve your process:
# 1. All the time present a 'Thought:' sequence, and a 'Code:n```py' sequence ending with '```' sequence, else you'll fail.
# 2. Use solely variables that you've got outlined.
# 3. All the time use the proper arguments for the instruments. DO NOT cross the arguments as a dict as in 'reply = wiki({'question': "What's the place the place James Bond lives?"})', however use the arguments immediately as in 'reply = wiki(question="What's the place the place James Bond lives?")'.
# 4. Take care to not chain too many sequential instrument calls in the identical code block, particularly when the output format is unpredictable. For example, a name to go looking has an unpredictable return format, so do not need one other instrument name that will depend on its output in the identical block: relatively output outcomes with print() to make use of them within the subsequent block.
# 5. Name a instrument solely when wanted, and by no means re-do a instrument name that you just beforehand did with the very same parameters.
# 6. Do not title any new variable with the identical title as a instrument: as an illustration do not title a variable 'final_answer'.
# 7. By no means create any notional variables in our code, as having these in your logs will derail you from the true variables.
# 8. You need to use imports in your code, however solely from the next record of modules: ['collections', 'datetime', 'itertools', 'math', 'numpy', 'pandas', 'queue', 'random', 're', 'stat', 'statistics', 'time', 'unicodedata']
# 9. The state persists between code executions: so if in a single step you have created variables or imported modules, these will all persist.
# 10. Do not quit! You are in control of fixing the duty, not offering instructions to resolve it.
# Now Start! On the finish of the immediate, we have now the instruction "# 2. Use solely variables that you've got outlined!". This could be interpreted as a strict rule to not use some other variables. So, I modified it to "# 2. Use solely variables that you've got outlined or ones offered in extra arguments! By no means attempt to copy and parse extra arguments."
modified_system_prompt = agent.prompt_templates['system_prompt']
.substitute(
'2. Use solely variables that you've got outlined!',
'2. Use solely variables that you've got outlined or ones offered in extra arguments! By no means attempt to copy and parse extra arguments.'
)
agent.prompt_templates['system_prompt'] = modified_system_promptThis transformation alone didn’t assist both. Then, I examined the duty message.
╭─────────────────────────── New run ────────────────────────────╮
│ │
│ Here's a pandas dataframe displaying income by section, │
│ evaluating values earlier than and after. │
│ Might you please assist me perceive the modifications? │
│ Particularly: │
│ 1. Estimate how the overall income and the income for every │
│ section have modified, each in absolute phrases and as a │
│ proportion. │
│ 2. Calculate the contribution of every section to the overall │
│ change in income. │
│ │
│ Please spherical all floating-point numbers within the output to 2 │
│ decimal locations. │
│ │
│ You have got been supplied with these extra arguments, that │
│ you possibly can entry utilizing the keys as variables in your python │
│ code: │
│ {'df': earlier than after │
│ nation │
│ different 632767.39 637000.48 │
│ UK 481409.27 477033.02 │
│ France 240704.63 107857.06 │
│ Germany 160469.75 159778.76 │
│ Italy 120352.31 121331.46 │
│ Spain 96281.86 96064.77}. │
│ │
╰─ LiteLLMModel - openai/gpt-4o-mini ────────────────────────────╯It has an instruction associated the the utilization of extra arguments "You have got been supplied with these extra arguments, which you can entry utilizing the keys as variables in your python code". We will attempt to make it extra particular and clear. Sadly, this parameter is just not uncovered externally, so I needed to find it in the source code. To search out the trail of a Python bundle, we will use the next code.
import smolagents
print(smolagents.__path__)Then, I discovered the brokers.py file and modified this line to incorporate a extra particular instruction.
self.process += f"""
You have got been supplied with these extra arguments obtainable as variables
with names {",".be a part of(additional_args.keys())}. You possibly can entry them immediately.
Here's what they include (only for informational functions):
{str(additional_args)}."""It was a little bit of hacking, however that’s generally what occurs with the LLM frameworks. Don’t overlook to reload the bundle afterwards, and we’re good to go. Let’s take a look at whether or not it really works now.
process = """
Here's a pandas dataframe displaying income by section, evaluating values
earlier than and after.
Your process can be perceive the modifications to the income (after vs earlier than)
in several segments and supply government abstract.
Please, comply with the next steps:
1. Estimate how the overall income and the income for every section
have modified, each in absolute phrases and as a proportion.
2. Calculate the contribution of every section to the overall change
in income.
Spherical all floating-point numbers within the output to 2 decimal locations.
"""
agent.logger.degree = 1 # Decrease verbosity degree
agent.run(
process,
additional_args={"df": df},
)Hooray! The issue has been fastened. The agent now not copies the enter variables and references df variable immediately as an alternative. Right here’s the newly generated code.
import pandas as pd
# Calculate whole income earlier than and after
total_before = df['before'].sum()
total_after = df['after'].sum()
total_change = total_after - total_before
percentage_change_total = (total_change / total_before * 100)
if total_before != 0 else 0
# Spherical values
total_before = spherical(total_before, 2)
total_after = spherical(total_after, 2)
total_change = spherical(total_change, 2)
percentage_change_total = spherical(percentage_change_total, 2)
# Show outcomes
print(f"Complete Income Earlier than: {total_before}")
print(f"Complete Income After: {total_after}")
print(f"Complete Change: {total_change}")
print(f"Proportion Change: {percentage_change_total}%")Now, we’re prepared to maneuver on to constructing the precise agent that may clear up our process.
AI agent for KPI narratives
Lastly, it’s time to work on the AI agent that may assist us clarify KPI modifications and create an government abstract.
Our agent will comply with this plan for the foundation trigger evaluation:
- Estimate the top-line KPI change.
- Slice and cube the metric to grasp which segments are driving the shift.
- Search for occasions within the change log to see whether or not they can clarify the metric modifications.
- Consolidate all of the findings within the complete government abstract.
After a number of experimentation and a number of other tweaks, I’ve arrived at a promising end result. Listed here are the important thing changes I made (we are going to talk about them intimately later):
- I leveraged the multi-agent setup by including one other group member — the change log Agent, who can entry the change log and help in explaining KPI modifications.
- I experimented with extra highly effective fashions like
gpt-4oandgpt-4.1-minisincegpt-4o-miniwasn’t enough. Utilizing stronger fashions not solely improved the outcomes, but in addition considerably decreased the variety of steps: withgpt-4.1-miniI received the ultimate end result after simply six steps, in comparison with 14–16 steps withgpt-4o-mini. This implies that investing in dearer fashions could be worthwhile for agentic workflows. - I offered the agent with the advanced instrument to analyse KPI modifications for easy metrics. The instrument performs all of the calculations, whereas LLM can simply interpret the outcomes. I mentioned the method to KPI modifications evaluation intimately in my previous article.
- I reformulated the immediate into a really clear step-by-step information to assist the agent keep on monitor.
- I added planning steps that encourage the LLM agent to suppose by means of its method first and revisit the plan each three iterations.
After all of the changes, I received the next abstract from the agent, which is fairly good.
Govt Abstract:
Between April 2025 and Might 2025, whole income declined sharply by
roughly 36.03%, falling from 1,731,985.21 to 1,107,924.43, a
drop of -624,060.78 in absolute phrases.
This decline was primarily pushed by vital income
reductions within the 'new' buyer segments throughout a number of
international locations, with declines of roughly 70% in these segments.
Probably the most impacted segments embody:
- other_new: earlier than=233,958.42, after=72,666.89,
abs_change=-161,291.53, rel_change=-68.94%, share_before=13.51%,
affect=25.85, impact_norm=1.91
- UK_new: earlier than=128,324.22, after=34,838.87,
abs_change=-93,485.35, rel_change=-72.85%, share_before=7.41%,
affect=14.98, impact_norm=2.02
- France_new: earlier than=57,901.91, after=17,443.06,
abs_change=-40,458.85, rel_change=-69.87%, share_before=3.34%,
affect=6.48, impact_norm=1.94
- Germany_new: earlier than=48,105.83, after=13,678.94,
abs_change=-34,426.89, rel_change=-71.56%, share_before=2.78%,
affect=5.52, impact_norm=1.99
- Italy_new: earlier than=36,941.57, after=11,615.29,
abs_change=-25,326.28, rel_change=-68.56%, share_before=2.13%,
affect=4.06, impact_norm=1.91
- Spain_new: earlier than=32,394.10, after=7,758.90,
abs_change=-24,635.20, rel_change=-76.05%, share_before=1.87%,
affect=3.95, impact_norm=2.11
Based mostly on evaluation from the change log, the primary causes for this
pattern are:
1. The introduction of recent onboarding controls carried out on Might
8, 2025, which decreased new buyer acquisition by about 70% to
forestall fraud.
2. A postal service strike within the UK beginning April 5, 2025,
inflicting order supply delays and elevated cancellations
impacting the UK new section.
3. A rise in VAT by 2% in Spain as of April 22, 2025,
affecting new buyer pricing and inflicting larger cart
abandonment.
These elements mixed clarify the outsized adverse impacts
noticed in new buyer segments and the general income decline.The LLM agent additionally generated a bunch of illustrative charts (they have been a part of our progress explaining instrument). For instance, this one exhibits the impacts throughout the mixture of nation and maturity.
The outcomes look actually thrilling. Now let’s dive deeper into the precise implementation to grasp the way it works underneath the hood.
Multi-AI agent setup
We’ll begin with our change log agent. This agent will question the change log and attempt to determine potential root causes for the metric modifications we observe. Since this agent doesn’t have to do advanced operations, we implement it as a ToolCallingAgent. As a result of this agent can be known as by one other agent, we have to outline its title and description attributes.
@instrument
def get_change_log(month: str) -> str:
"""
Returns the change log (record of inside and exterior occasions which may have affected our KPIs) for the given month
Args:
month: month within the format %Y-%m-01, for instance, 2025-04-01
"""
return events_df[events_df.month == month].drop('month', axis = 1).to_dict('data')
mannequin = LiteLLMModel(model_id="openai/gpt-4.1-mini", api_key=config['OPENAI_API_KEY'])
change_log_agent = ToolCallingAgent(
instruments=[get_change_log],
mannequin=mannequin,
max_steps=10,
title="change_log_agent",
description="Helps you discover the related data within the change log that may clarify modifications on metrics. Present the agent with all of the context to obtain data",
)For the reason that supervisor agent can be calling this agent, we gained’t have any management over the question it receives. Due to this fact, I made a decision to switch the system immediate to incorporate extra context.
change_log_system_prompt = '''
You are a grasp of the change log and also you assist others to clarify
the modifications to metrics. While you obtain a request, lookup the record of occasions
occurred by month, then filter the related data primarily based
on offered context and return again. Prioritise essentially the most possible elements
affecting the KPI and restrict your reply solely to them.
'''
modified_system_prompt = change_log_agent.prompt_templates['system_prompt']
+ 'nnn' + change_log_system_prompt
change_log_agent.prompt_templates['system_prompt'] = modified_system_promptTo allow the first agent to delegate duties to the change log agent, we merely have to specify it within the managed_agents subject.
agent = CodeAgent(
mannequin=mannequin,
instruments=[calculate_simple_growth_metrics],
max_steps=20,
additional_authorized_imports=["pandas", "numpy", "matplotlib.*", "plotly.*"],
verbosity_level = 2,
planning_interval = 3,
managed_agents = [change_log_agent]
)Let’s see the way it works. First, we will have a look at the brand new system immediate for the first agent. It now contains details about group members and directions on methods to ask them for assist.
You can too give duties to group members.
Calling a group member works the identical as for calling a instrument: merely,
the one argument you may give within the name is 'process'.
On condition that this group member is an actual human, you have to be very verbose
in your process, it needs to be a protracted string offering informations
as detailed as mandatory.
Here's a record of the group members which you can name:
```python
def change_log_agent("Your question goes right here.") -> str:
"""Helps you discover the related data within the change log that
can clarify modifications on metrics. Present the agent with all of the context
to obtain data"""
```The execution log exhibits that the first agent efficiently delegated the duty to the second agent and obtained the next response.
<-- Main agent calling the change log agent -->
─ Executing parsed code: ───────────────────────────────────────
# Question change_log_agent with the detailed process description
ready
context_for_change_log = (
"We analyzed modifications in income from April 2025 to Might
2025. We discovered massive decreases "
"primarily within the 'new' maturity segments throughout international locations:
Spain_new, UK_new, Germany_new, France_new, Italy_new, and
other_new. "
"The income fell by round 70% in these segments, which
have outsized adverse affect on whole income change. "
"We need to know the 1-3 most possible causes for this
vital drop in income within the 'new' buyer segments
throughout this era."
)
rationalization = change_log_agent(process=context_for_change_log)
print("Change log agent rationalization:")
print(rationalization)
────────────────────────────────────────────────────────────────
<-- Change log agent execution begin -->
╭──────────────────── New run - change_log_agent ─────────────────────╮
│ │
│ You are a useful agent named 'change_log_agent'. │
│ You have got been submitted this process by your supervisor. │
│ --- │
│ Activity: │
│ We analyzed modifications in income from April 2025 to Might 2025. │
│ We discovered massive decreases primarily within the 'new' maturity segments │
│ throughout international locations: Spain_new, UK_new, Germany_new, France_new, │
│ Italy_new, and other_new. The income fell by round 70% in these │
│ segments, which have outsized adverse affect on whole income │
│ change. We need to know the 1-3 most possible causes for this │
│ vital drop in income within the 'new' buyer segments throughout │
│ this era. │
│ --- │
│ You are serving to your supervisor clear up a wider process: so be sure that to │
│ not present a one-line reply, however give as a lot data as │
│ attainable to provide them a transparent understanding of the reply. │
│ │
│ Your final_answer WILL HAVE to include these components: │
│ ### 1. Activity final result (brief model): │
│ ### 2. Activity final result (extraordinarily detailed model): │
│ ### 3. Extra context (if related): │
│ │
│ Put all these in your final_answer instrument, every part that you just do │
│ not cross as an argument to final_answer can be misplaced. │
│ And even when your process decision is just not profitable, please return │
│ as a lot context as attainable, in order that your supervisor can act upon │
│ this suggestions. │
│ │
╰─ LiteLLMModel - openai/gpt-4.1-mini ────────────────────────────────╯Utilizing the smolagents framework, we will simply arrange a easy multi-agent system, the place a supervisor agent coordinates and delegates duties to group members with particular abilities.
Iterating on the immediate
We’ve began with a really high-level immediate outlining the objective and a obscure route, however sadly, it didn’t work constantly. LLMs aren’t sensible sufficient but to determine the method on their very own. So, I created an in depth step-by-step immediate describing the entire plan and together with the detailed specs of the expansion narrative instrument we’re utilizing.
process = """
Here's a pandas dataframe displaying the income by section, evaluating values
earlier than (April 2025) and after (Might 2025).
You are a senior and skilled information analyst. Your process can be to grasp
the modifications to the income (after vs earlier than) in several segments
and supply government abstract.
## Observe the plan:
1. Begin by udentifying the record of dimensions (columns in dataframe that
aren't "earlier than" and "after")
2. There could be a number of dimensions within the dataframe. Begin high-level
by taking a look at every dimension in isolation, mix all outcomes
collectively into the record of segments analysed (do not forget to save lots of
the dimension used for every section).
Use the offered instruments to analyse the modifications of metrics: {tools_description}.
3. Analyse the outcomes from earlier step and maintain solely segments
which have outsized affect on the KPI change (absolute of impact_norm
is above 1.25).
4. Examine what dimensions are current within the record of serious section,
if there are a number of ones - execute the instrument on their mixtures
and add to the analysed segments. If after including a further dimension,
all subsegments present shut different_rate and impact_norm values,
then we will exclude this cut up (despite the fact that impact_norm is above 1.25),
because it would not clarify something.
5. Summarise the numerous modifications you recognized.
6. Attempt to clarify what's going on with metrics by getting data
from the change_log_agent. Please, present the agent the total context
(what segments have outsized affect, what's the relative change and
what's the interval we're taking a look at).
Summarise the knowledge from the changelog and point out
solely 1-3 essentially the most possible causes of the KPI change
(ranging from essentially the most impactful one).
7. Put collectively 3-5 sentences commentary what occurred high-level
and why (primarily based on the data obtained from the change log).
Then comply with it up with extra detailed abstract:
- Prime-line whole worth of metric earlier than and after in human-readable format,
absolute and relative change
- Listing of segments that meaningfully influenced the metric positively
or negatively with the next numbers: values earlier than and after,
absoltue and relative change, share of section earlier than, affect
and normed affect. Order the segments by absolute worth
of absolute change because it represents the facility of affect.
## Instruction on the calculate_simple_growth_metrics instrument:
By default, you need to use the instrument for the entire dataset not the section,
because it will provide you with the total details about the modifications.
Right here is the steerage methods to interpret the output of the instrument
- distinction - absolutely the distinction between after and earlier than values
- difference_rate - the relative distinction (if it is shut for
all segments then the dimension is just not informative)
- affect - the share of KPI differnce defined by this section
- segment_share_before - share of section earlier than
- impact_norm - affect normed on the share of segments, we're
in very excessive or very low numbers since they present outsized affect,
rule of thumb - impact_norm between -1.25 and 1.25 is not-informative
In case you're utilizing the instrument on the subset of dataframe be mindful,
that the outcomes will not be aplicable to the total dataset, so keep away from utilizing it
except you need to explicitly have a look at subset (i.e. change in France).
In case you determined to make use of the instrument on a specific section
and share these ends in the manager abstract, explicitly define
that we're diving deeper into a specific section.
""".format(tools_description = tools_description)
agent.run(
process,
additional_args={"df": df},
)Explaining every part in such element was fairly a frightening process, nevertheless it’s mandatory if we wish constant outcomes.
Planning steps
The smolagents framework allows you to add planning steps to your agentic move. This encourages the agent to start out with a plan and replace it after the desired variety of steps. From my expertise, this reflection may be very useful for sustaining concentrate on the issue and adjusting actions to remain aligned with the preliminary plan and objective. I undoubtedly advocate utilizing it in instances when advanced reasoning is required.
Setting it up is as simple as specifying planning_interval = 3 for the code agent.
agent = CodeAgent(
mannequin=mannequin,
instruments=[calculate_simple_growth_metrics],
max_steps=20,
additional_authorized_imports=["pandas", "numpy", "matplotlib.*", "plotly.*"],
verbosity_level = 2,
planning_interval = 3,
managed_agents = [change_log_agent]
)That’s it. Then, the agent offers reflections beginning with interested by the preliminary plan.
────────────────────────── Preliminary plan ──────────────────────────
Listed here are the details I do know and the plan of motion that I'll
comply with to resolve the duty:
```
## 1. Info survey
### 1.1. Info given within the process
- We've a pandas dataframe `df` displaying income by section, for
two time factors: earlier than (April 2025) and after (Might 2025).
- The dataframe columns embody:
- Dimensions: `nation`, `maturity`, `country_maturity`,
`country_maturity_combined`
- Metrics: `earlier than` (income in April 2025), `after` (income in
Might 2025)
- The duty is to grasp the modifications in income (after vs
earlier than) throughout completely different segments.
- Key directions and instruments offered:
- Determine all dimensions besides earlier than/after for segmentation.
- Analyze every dimension independently utilizing
`calculate_simple_growth_metrics`.
- Filter segments with outsized affect on KPI change (absolute
normed affect > 1.25).
- Study mixtures of dimensions if a number of dimensions have
vital segments.
- Summarize vital modifications and interact `change_log_agent`
for contextual causes.
- Present a closing government abstract together with top-line modifications
and segment-level detailed impacts.
- Dataset snippet exhibits segments combining international locations (`France`,
`UK`, `Germany`, `Italy`, `Spain`, `different`) and maturity standing
(`new`, `present`).
- The mixed segments are uniquely recognized in columns
`country_maturity` and `country_maturity_combined`.
### 1.2. Info to lookup
- Definitions or descriptions of the segments if unclear (e.g.,
what defines `new` vs `present` maturity).
- Probably not necessary to proceed, however could possibly be requested from
enterprise documentation or change log.
- Extra particulars on the change log (accessible through
`change_log_agent`) that would present possible causes for income
modifications.
- Affirmation on dealing with mixed dimension splits - how precisely
`country_maturity_combined` is shaped and needs to be interpreted in
mixed dimension evaluation.
- Information dictionary or description of metrics if any extra KPI
in addition to income is related (unlikely given information).
- Dates verify interval of research: April 2025 (earlier than) and Might
2025 (after). No have to look these up since given.
### 1.3. Info to derive
- Determine all dimension columns obtainable for segmentation:
- By excluding 'earlier than' and 'after', seemingly candidates are
`nation`, `maturity`, `country_maturity`, and
`country_maturity_combined`.
- For every dimension, calculate change metrics utilizing the given
instrument:
- Absolute and relative distinction in income per section.
- Affect, section share earlier than, and normed affect for every
section.
- Determine which segments have outsized affect on KPI change
(|impact_norm| > 1.25).
- If a number of dimensions have vital segments, mix
dimensions (e.g., nation + maturity) and reanalyze.
- Decide if mixed dimension splits present significant
differentiation or not, primarily based on delta charge and impact_norm
consistency.
- Summarize route and magnitude of KPI modifications at top-line
degree (mixture income earlier than and after).
- Determine prime segments driving constructive and adverse modifications
primarily based on ordered absolute absolute_change.
- Collect contextual insights from the change log agent concerning
possible causes tied to vital segments and the Might 2025 vs
April 2025 interval.
## 2. Plan
1. Determine all dimension columns current within the dataframe by
itemizing columns and excluding 'earlier than' and 'after'.
2. For every dimension recognized (`nation`, `maturity`,
`country_maturity`, `country_maturity_combined`):
- Use `calculate_simple_growth_metrics` on the total dataframe
grouped by that dimension.
- Extract segments with calculated metrics together with
impact_norm.
3. Combination outcomes from all single-dimension analyses and filter
segments the place |impact_norm| > 1.25.
4. Decide which dimensions these vital segments belong
to.
5. If a couple of dimension is represented in these vital
segments, analyze the mixed dimension shaped by these
dimensions (for instance, mixture of `nation` and `maturity`
or use present mixed dimension columns).
6. Repeat metric calculation utilizing
`calculate_simple_growth_metrics` on the mixed dimension.
7. Study if the mixed dimension splits create significant
differentiation - if all subsegments present shut difference_rate
and impact_norm, exclude the cut up.
8. Put together a abstract of serious modifications:
- Prime-line KPIs earlier than and after (absolute and relative
modifications).
- Listing of impactful segments sorted by absolute absolute_change
that influenced general income.
9. Present the record of segments with particulars (values earlier than,
after, absolute and relative change, share earlier than, affect,
impact_norm).
10. Utilizing this summarized data, question `change_log_agent`
with full context:
- Embody vital segments, their relative modifications, and
intervals (April to Might 2025).
11. Course of the agent's response to determine 1-3 fundamental possible
causes of the KPI modifications.
12. Draft government abstract commentary:
- Excessive-level overview of what occurred and why, primarily based on log
data.
- Detailed abstract together with top-line modifications and
segment-level metrics affect.
13. Ship the ultimate reply utilizing `final_answer` instrument containing
the above government abstract and data-driven insights.Then, after every three steps, the agent revisits and updates the plan.
────────────────────────── Up to date plan ──────────────────────────
I nonetheless want to resolve the duty I used to be given:
```
Here's a pandas dataframe displaying the income by section,
evaluating values earlier than (April 2025) and after (Might 2025).
You are a senior and skilled information analyst. Your process can be
perceive the modifications to the income (after vs earlier than) in
completely different segments
and supply government abstract.
<... repeating the total preliminary process ...>
```
Listed here are the details I do know and my new/up to date plan of motion to
clear up the duty:
```
## 1. Up to date details survey
### 1.1. Info given within the process
- We've a pandas dataframe with income by section, displaying
values "earlier than" (April 2025) and "after" (Might 2025).
- Columns within the dataframe embody a number of dimensions and the
"earlier than" and "after" income values.
- The objective is to grasp income modifications by section and supply
an government abstract.
- Steerage and guidelines about methods to analyze and interpret outcomes
from the `calculate_simple_growth_metrics` instrument are offered.
- The dataframe comprises columns: nation, maturity,
country_maturity, country_maturity_combined, earlier than, after.
### 1.2. Info that we have now realized
- The scale to investigate are: nation, maturity,
country_maturity, and country_maturity_combined.
- Analyzed income modifications by dimension.
- Solely the "new" maturity section has vital affect
(impact_norm=1.96 > 1.25), with a big adverse income change (~
-70.6%).
- Within the mixed section "country_maturity," the "new" segments
throughout international locations (Spain_new, UK_new, Germany_new, France_new,
Italy_new, other_new) all have outsized adverse impacts with
impact_norm values all above 1.9.
- The mature/present segments in these international locations have smaller
normed impacts beneath 1.25.
- Nation-level and maturity-level section dimension alone are
much less revealing than the mixed nation+maturity section
dimension which highlights the brand new segments as strongly impactful.
- Complete income dropped considerably from earlier than to after, largely
pushed by new segments shrinking drastically.
### 1.3. Info nonetheless to lookup
- Whether or not splitting the information by extra dimensions past
nation and maturity (e.g., country_maturity_combined) explains
additional heterogeneous impacts or if the sample is uniform.
- Clarification/context from change log about what brought on the key
drop predominantly in new segments in all international locations.
- Confirming whether or not any nation inside the new section behaved
in another way or mitigated losses.
### 1.4. Info nonetheless to derive
- A concise government abstract describing the top-level income
change and figuring out which segments clarify the declines.
- Clarification involving the change log agent with abstract of
possible causes for these outsized reductions in income within the
new segments throughout international locations for April-Might 2025.
## 2. Plan
### 2.1. Confirm if including the extra dimension
'country_maturity_combined' splits the impactful "new" segments
into subsegments with considerably completely different impacts or if the
change charges and normed impacts are comparatively homogeneous. If
homogeneous, we don't acquire deeper perception and may disregard
additional splitting.
### 2.2. Summarize all vital segments recognized with
outsized impact_norm ≥ 1.25, together with their earlier than and after
values, absolute and relative modifications, section shares earlier than,
affect, and normalized affect, ordered by absolute worth of the
change.
### 2.3. Question the change_log_agent with the total context:
vital segments are the brand new country_maturity segments with
massive adverse modifications (~ -70%), timeframe April 2025 to Might 2025,
and request prime 1-3 most possible causes for the KPI income drop
in these segments.
### 2.4. Based mostly on the change log agent's response, synthesize a
3-5 sentence high-level commentary explaining what occurred
broadly and why.
### 2.5. Draft an in depth government abstract together with:
- Complete income earlier than and after in human-readable format with
absolute and relative change.
- An inventory of serious segments driving these modifications, so as
by absolute affect, with detailed numbers (earlier than, after, absolute
and relative change, section share earlier than, affect, normed affect).
### 2.6. Use the `final_answer` instrument to provide the finalized
government abstract report.I actually like how the agent is inspired to reiterate on the preliminary process and keep centered on the primary downside. Common reflection like that is useful in actual life as effectively, as groups usually get slowed down within the course of and lose sight of the why behind what they’re doing. It’s fairly cool to see managerial finest practices being built-in into agentic frameworks.
That’s it! We’ve constructed a code agent able to analysing KPI modifications for easy metrics and explored all the important thing nuances of the method.
You will discover the whole code and execution logs on GitHub.
Abstract
We’ve experimented lots with code brokers and are actually prepared to attract conclusions. For our experiments, we used the HuggingFace smolagents framework for code brokers — a really helpful toolset that provides:
- simple integration with completely different LLMs (from native fashions through Ollama to public suppliers like Anthropic or OpenAI),
- excellent logging that makes it simple to grasp the entire thought technique of the agent and debug points,
- skill to construct advanced methods leveraging multi-AI agent setups or planning options with out a lot effort.
Whereas smolagents is at the moment my favorite agentic framework, it has its limitations:
- It might lack flexibility at instances. For instance, I needed to modify the immediate immediately within the supply code to get the behaviour I needed.
- It solely helps hierarchical multi-agent set-up (the place one supervisor can delegate duties to different brokers), however doesn’t cowl sequential workflow or consensual decision-making processes.
- There’s no assist for long-term reminiscence out of the field, which means you’re ranging from scratch with each process.
Thank you numerous for studying this text. I hope this text was insightful for you.
Reference
This text is impressed by the “Building Code Agents with Hugging Face smolagents” brief course by DeepLearning.AI.
