Code Agents: The Future of Agentic AI

of AI brokers. LLMs are not simply instruments. They’ve turn out to be energetic contributors in our lives, boosting productiveness and remodeling the best way we dwell and work. 

• OpenAI lately launched Operator, an AI agent that may autonomously carry out varied duties, from looking the net to filling out varieties and scheduling appointments.
• Anthropic launched MCP (Model Context Protocol), a brand new customary for a way AI assistants work together with the surface world. With over 5 thousand energetic MCP servers already, adoption is rising quickly.
• AI brokers are additionally altering the panorama of software program engineering. Instruments like GitHub Copilot’s agentic mode, Claude Code, OpenAI Codex, and others are usually not solely improving developer productivity and code quality but in addition democratising the sector, making software program growth accessible to individuals with no technical background.

We’ve beforehand checked out totally different AI Agent frameworks, comparable to LangGraph or CrewAI. On this article, I wish to talk about a brand new one I’ve been exploring lately — HuggingFace smolagents. It’s an fascinating framework because it implements the idea of code brokers.

On this article, we’ll discover a number of matters:

• What code brokers are (teaser: it’s not associated to vibe coding).
• Tips on how to use the HuggingFace smolagents framework in observe. 
• Whether or not it’s safe to provide LLMs a lot company.
• The actual distinction in efficiency between code brokers and conventional tool-calling brokers.

AI Brokers recap

Let’s begin with a fast refresher: what precisely are AI brokers? HuggingFace provides a transparent and concise definition of what they imply by brokers. 

AI Brokers are applications the place LLM outputs management the workflow.

So, we want an agentic stream after we need a system to motive and act based mostly on observations. Really, company will not be a binary variable (sure or no), however a spectrum. 

• At one finish, we are able to have programs with out company in any respect, for instance, a easy course of the place an LLM defines the sentiment of a textual content, interprets it or summarises it. 
• The following degree is routing, the place an LLM can classify an incoming query and determine which path to take —  for instance, calling a instrument if a buyer is asking in regards to the standing of their present order, and transferring the dialog to a human CS agent in any other case. 
• Extra superior programs can exhibit greater levels of company. These may embrace the flexibility to execute different LLMs (multi-agent setup) and even create new instruments on the fly.

Code brokers fall into this extra superior class. They’re multi-step brokers that execute instrument calls within the type of code, in distinction to the extra conventional method utilizing a JSON format with the instrument title and arguments.

A number of latest papers have proven that utilizing code in agentic flows results in higher outcomes: 

It is sensible when you consider it. We’ve been creating programming languages for many years to resolve advanced issues. So, it’s pure that these languages are higher suited to LLM’s duties than easy JSON configs. A further profit is that LLMs are already fairly good at writing code in frequent programming languages, because of the huge quantity of accessible knowledge for coaching.

This method comes with a number of different advantages as nicely:

• By producing code, an LLM will not be restricted to a predefined set of instruments and may create its personal capabilities.
• It might probably mix a number of instruments inside a single motion utilizing situations and loops, which helps cut back the variety of steps required to finish a activity.  
• It additionally allows the mannequin to work with a greater diversity of outputs, comparable to producing charts, photos, or different advanced objects.

These advantages aren’t simply theoretical; we are able to observe them in observe. In “Executable Code Actions Elicit Better LLM Agents”, the authors present that code brokers outperform conventional strategies, attaining a better success charge and finishing a activity in fewer steps, which in flip reduces prices. 

Code brokers look promising, which impressed me to do that method in observe.

HuggingFace smolagents framework

First attempt

Fortunately, we don’t have to construct code brokers from scratch, as HuggingFace has launched a useful library known as smolagents that implements this method. 

Let’s begin by putting in the library. 

pip set up smolagents[litellm] 
# I've used litellm, since I am planning to make use of it with OpenAI mannequin

Subsequent, let’s construct a primary instance. To initialise the agent, we want simply two parameters: mannequin and instruments. 

I plan to make use of OpenAI for the mannequin, which is accessible by way of LiteLLM. Nonetheless, the framework helps different choices as nicely. You should utilize an area mannequin by way of Ollama or TransformersModel, or public fashions by way of Inference Providers or select different choices (you’ll find extra particulars in the documentation). 

I didn’t specify any instruments, however used add_base_tools = True, so my agent has a default set of tools, comparable to a Python interpreter or DuckDuckGo search. Let’s attempt it out with a easy query.

from smolagents import CodeAgent, LiteLLMModel
mannequin = LiteLLMModel(model_id="openai/gpt-4o-mini", 
  api_key=config['OPENAI_API_KEY']) 
agent = CodeAgent(instruments=[], mannequin=mannequin, add_base_tools=True)

agent.run(
    """I've 5 totally different balls and I randomly choose 2. 
    What number of doable mixtures of the balls I can get?""",
)

In consequence, we see a extremely properly formatted execution stream. It’s simply wonderful and permits you to perceive the method completely.

So, the agent discovered a solution in a single step and wrote Python code to calculate the variety of mixtures. 

The output is kind of useful, however we are able to go even deeper and have a look at the whole info associated to execution (together with prompts), by way of agent.reminiscence.steps. Let’s have a look at the system immediate utilized by the agent. 

You're an professional assistant who can clear up any activity utilizing code blobs. 
You can be given a activity to resolve as finest you'll be able to.

To take action, you've gotten been given entry to an inventory of instruments: these instruments 
are mainly Python capabilities which you'll be able to name with code.

To resolve the duty, you should plan ahead to proceed in a collection of 
steps, in a cycle of 'Thought:', 'Code:', 
and 'Statement:' sequences.

At every step, within the 'Thought:' sequence, it is best to first clarify 
your reasoning in direction of fixing the duty and the instruments that you really want 
to make use of.

Then within the 'Code:' sequence, it is best to write the code in easy 
Python. The code sequence should finish with '' sequence.

Throughout every intermediate step, you should utilize 'print()' to save lots of 
no matter vital info you'll then want.

These print outputs will then seem within the 'Statement:' discipline, 
which can be accessible as enter for the subsequent step.

In the long run you must return a ultimate reply utilizing 
the final_answer instrument.

Listed below are a number of examples utilizing notional instruments: <...>

It’s fairly clear that smolagents implements the ReAct method (launched within the paper by Yao et al. “ReAct: Synergizing Reasoning and Acting in Language Models”) and makes use of a few-shot prompting approach.

The smolagents library handles all behind-the-scenes work concerned within the agent workflow: assembling the system immediate with all essential info for the LLM (i.e. accessible instruments), parsing the output and executing the generated code. It additionally offers complete logging and a retry mechanism to assist appropriate errors.

Moreover, the library provides reminiscence administration options. By default, all execution outcomes are saved to reminiscence, however you’ll be able to customise this behaviour. For instance, you’ll be able to take away some middleman outcomes from the reminiscence to cut back the variety of tokens or execute the agent step-by-step. Whereas we gained’t dive deep into reminiscence administration right here, you’ll find useful code examples in the documentation.

Safety

Now, it’s time to debate the drawbacks of the code brokers’ method. Giving an LLM extra company by permitting it to execute arbitrary code introduces greater dangers. Certainly, an LLM can run dangerous code both by mistake (since LLMs are nonetheless removed from good) or resulting from focused assaults like immediate injections or compromised fashions. 

To mitigate these dangers, the native Python executor applied within the smolagents library has a bunch of security checks: 

• By default, imports are usually not allowed except the bundle has been explicitly added to additional_authorized_imports listing. 
• Furthermore, submodules are blocked by default, so you should authorise them particularly (i.e. numpy.* ). It’s been accomplished as a result of some packages can expose probably dangerous submodules, i.e. random._os . 
• The overall variety of executed operations is capped, stopping infinite loops and useful resource bloating. 
• Any operation not explicitly outlined within the interpreter will increase an error. 

Let’s check whether or not these security measures truly work.

from smolagents.local_python_executor import LocalPythonExecutor

custom_executor = LocalPythonExecutor(["numpy.*", "random"])

# perform to have fairly formatted exceptions
def run_capture_exception(command: str):
    attempt:
        custom_executor(harmful_command)
    besides Exception as e:
        print("ERROR:n", e)

# Unauthorised imports are blocked 
harmful_command="import os; exit_code = os.system('')"
run_capture_exception(harmful_command)
# ERROR: Code execution failed at line 'import os' resulting from: 
# InterpreterError: Import of os will not be allowed. Approved imports 
# are: ['datetime', 'itertools', 're', 'math', 'statistics', 'time', 'queue', 
# 'numpy.*', 'random', 'collections', 'unicodedata', 'stat']

# Submodules are additionally blocked except acknowledged particularly
harmful_command="from random import _os; exit_code = _os.system('')"
run_capture_exception(harmful_command)
# ERROR: Code execution failed at line 'exit_code = _os.system('')' 
# resulting from: InterpreterError: Forbidden entry to module: os

# The cap on the variety of iterations breaks inifinity loops 
harmful_command = '''
whereas True: 
    go
'''
run_capture_exception(harmful_command)
# ERROR: Code execution failed at line 'whereas True: go' resulting from: 
# InterpreterError: Most variety of 1000000 iterations in Whereas loop 
# exceeded

# Undefined operations do not work
harmful_command="!echo "
custom_executor(harmful_command)
# ERROR: Code parsing failed on line 1 resulting from: SyntaxError

It appears we now have some security nets with code brokers. Nonetheless, regardless of these safeguards, dangers persist once you’re executing code domestically. For instance, an LLM can recursively create threads in your pc or create too many recordsdata, resulting in useful resource bloating. A doable resolution is to execute code in a sandboxed surroundings, comparable to utilizing Docker or options like E2B. I’m prepared to be adventurous and run my code domestically, however should you favor a extra risk-averse method, you’ll be able to comply with the sandbox set-up steerage in the documentation.

Code agent vs conventional Device-Calling agent

It’s claimed that the code brokers carry out higher in comparison with the normal JSON-based method. Let’s put this to the check. 
I’ll use the duty of metrics change evaluation that I described in my earlier article, “Making sense of KPI changes”. We are going to begin with an easy case: analysing a easy metric (income) break up by one dimension (nation).

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'})

The smolagents library helps two courses, which we are able to use to match two approaches: 

• CodeAgent — an agent that acts by producing and executing code,
• ToolCallingAgent — a standard JSON-based agent. 

Our brokers will want some instruments, so let’s implement them. There are multiple options to create tools in smolagents: we are able to re-use LangChain instruments, obtain them from HuggingFace Hub or just create Python capabilities. We are going to take essentially the most simple method by writing a few Python capabilities and annotating them with @instrument. 

I’ll create two instruments: one to estimate the relative distinction between metrics, and one other to calculate the sum of an inventory. Since LLM can be utilizing these instruments, offering detailed descriptions is essential. 

@instrument 
def calculate_metric_increase(earlier than: float, after: float) -> float: 
    """
    Calculate the share change of the metric between earlier than and after

    Args:
        earlier than: worth earlier than
        after: worth after
    """
    return (earlier than - after) * 100/ earlier than

@instrument 
def calculate_sum(values: listing) -> float: 
    """
    Calculate the sum of listing

    Args:
        values: listing of numbers
    """
    return sum(values)

Teaser: I’ll later realise that I ought to have supplied extra instruments to the agent, however I genuinely missed them.

CodeAgent

Let’s begin with a CodeAgent. I’ve initialised the agent with the instruments we outlined earlier and authorised the utilization of some Python packages that may be useful. 

agent = CodeAgent(
    mannequin=mannequin,
    instruments=[calculate_metric_increase, calculate_sum],
    max_steps=10,
    additional_authorized_imports=["pandas", "numpy", "matplotlib.*", 
      "plotly.*"],
    verbosity_level=1 
)

activity = """
Here's a dataframe displaying income by phase, evaluating values 
earlier than and after.
May you please assist me perceive the modifications? Particularly:
1. Estimate how the whole income and the income for every phase 
have modified, each in absolute phrases and as a proportion.
2. Calculate the contribution of every phase to the whole 
change in income.

Please spherical all floating-point numbers within the output 
to 2 decimal locations.
"""

agent.run(
    activity,
    additional_args={"knowledge": df},
)

Total, the code agent accomplished the duty in simply two steps, utilizing solely 5,451 enter and 669 output tokens. The consequence additionally appears to be like fairly believable.

{'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 execution stream. The LLM obtained the next immediate. 

╭─────────────────────────── New run ────────────────────────────╮
│                                                                │
│ Here's a pandas dataframe displaying income by phase,         │
│ evaluating values earlier than and after.                             │
│ May you please assist me perceive the modifications?               │
│ Particularly:                                                  │
│ 1. Estimate how the whole income and the income for every     │
│ phase have modified, each in absolute phrases and as a          │
│ proportion.                                                    │
│ 2. Calculate the contribution of every phase to the whole     │
│ change in income.                                             │
│                                                                │
│ Please spherical all floating-point numbers within the output to 2   │
│ decimal locations.                                                │
│                                                                │
│ You've got been supplied with these extra arguments, that   │
│ you'll be able to 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 ────────────────────────────╯

In step one, the LLM generated a dataframe and carried out all calculations. Apparently, it selected to jot down all of the code independently quite than utilizing the supplied instruments. 

Much more surprisingly, the LLM recreated the dataframe based mostly on the enter knowledge as a substitute of referencing it straight. This method will not be best (particularly when working with huge datasets), as it may result in errors and better token utilization. This behaviour may probably be improved through the use of a extra express system immediate. Right here’s the code the agent executed in step one.

import pandas as pd                                                                                                        
 
# Creating the DataFrame from the supplied knowledge                 
knowledge = {                                                        
    '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(knowledge, index=index)                            
                                                                
# Calculating complete income earlier than and after                    
total_before = df['before'].sum()                               
total_after = df['after'].sum()                                 
                                                                
# Calculating absolute and proportion change for every phase   
df['absolute_change'] = df['after'] - df['before']              
df['percentage_change'] = (df['absolute_change'] /              
df['before']) * 100                                             
                                                                
# Calculating complete income change                              
total_change = total_after - total_before                       
                                                                
# Calculating contribution of every phase to the whole change  
df['contribution_to_change'] = (df['absolute_change'] /         
total_change) * 100                                             
                                                                
# Rounding outcomes                                              
df = df.spherical(2)                                                
                                                                
# Printing the calculated outcomes                               
print("Whole income earlier than:", total_before)                    
print("Whole income after:", total_after)                      
print("Whole change in income:", total_change)                 
print(df)

Within the second step, the LLM merely constructed the ultimate reply by referring to the variables calculated on the earlier step (which is basically neat). 

final_answer({                                                  
    "total_before": spherical(total_before, 2),                     
    "total_after": spherical(total_after, 2),                       
    "total_change": spherical(total_change, 2),                     
    "segment_changes": df[['absolute_change',                   
'percentage_change', 'contribution_to_change']].to_dict()       
})

It labored fairly nicely.

ToolCallingAgent

Now, it’s time to see how conventional tool-calling brokers can sort out this drawback. We initialised it in an identical method and ran the duty.

from smolagents import ToolCallingAgent
traditional_agent = ToolCallingAgent(
    mannequin=mannequin,
    instruments=[calculate_metric_increase, calculate_sum],
    max_steps=30,
)

activity = """
Here's a dataframe displaying income by phase, evaluating values 
earlier than and after.
May you please assist me perceive the modifications? Particularly:
1. Estimate how the whole income and the income for every phase 
have modified, each in absolute phrases and as a proportion.
2. Calculate the contribution of every phase to the whole 
change in income.

Please spherical all floating-point numbers within the output 
to 2 decimal locations.
"""

traditional_agent.run(
    activity,
    additional_args={"knowledge": df},
)

The outcomes are removed from best: solely the relative modifications are appropriate, whereas the remainder of the numbers are pure hallucinations. I’ve to confess, the core difficulty was the dearth of acceptable instruments (particularly, instruments to calculate variations and to estimate shares). Nonetheless, the agent ought to have flagged lacking instruments quite than producing random numbers.

Whole income change: -7319.66 (-7.67%). Income Modifications by Phase:
- Different: +232.09 (-0.67%)
- UK: -4376.25 (0.91%)
- France: -132847.57 (55.19%)
- Germany: -690.99 (0.43%)
- Italy: +979.15 (-0.81%)
- Spain: -217.09 (0.23%)
Contribution to complete change:
- Different: 0.03%
- UK: -59.88%
- France: -181.77%
- Germany: -9.43%
- Italy: +13.38%
- Spain: -0.03%

By way of useful resource utilization, the tool-calling agent carried out considerably worse: 12 steps, with 29,201 enter and 1,695 output tokens. So, code brokers clearly supply value financial savings with regards to agent execution.

Let’s dig a bit deeper to grasp what the agent truly did. First, it took 4 iterations simply to determine methods to name calculate_sum appropriately and estimate the whole income earlier than and after. 

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling instrument: 'calculate_sum' with arguments:                │
│ {'values': {'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]}}                                       │
╰──────────────────────────────────────────────────────────────╯
Invalid name to instrument 'calculate_sum' with arguments           
{"values": {"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]}}:                                       
unsupported operand kind(s) for +: 'int' and 'str'            
You must name this instrument with appropriate enter arguments.       
Anticipated inputs: {"values": {"kind": "object",                
"description": "listing of values"}}                             
Returns output kind: quantity                                   
Device description: 'Calculate the sum of listing'                 
[Step 1: Duration 4.06 sec | Input tokens: 1,190 | Output: 250]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling instrument: 'calculate_sum' with arguments:                │
│ {'values': {'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]}}                                       │
╰──────────────────────────────────────────────────────────────╯
Invalid name to instrument 'calculate_sum' with arguments           
{"values": {"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]}}:                                       
unsupported operand kind(s) for +: 'int' and 'str'            
You must name this instrument with appropriate enter arguments.       
Anticipated inputs: {"values": {"kind": "object",                
"description": "listing of values"}}                             
Returns output kind: quantity                                   
Device description: 'Calculate the sum of listing'                 
[Step 2: Duration 8.60 sec | Input tokens: 2,815 | Output: 386]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling instrument: 'calculate_sum' with arguments:                │
│ {'values': [632767.39, 481409.27, 240704.63, 160469.75,      │
│ 120352.31, 96281.86]}                                        │
╰──────────────────────────────────────────────────────────────╯
Observations: 1731985.2100000002                              
[Step 3: Duration 1.23 sec | Input tokens: 4,871 | Output: 488]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling instrument: 'calculate_sum' with arguments:                │
│ {'values': [637000.48, 477033.02, 107857.06, 159778.76,      │
│ 121331.46, 96064.77]}                                        │
╰──────────────────────────────────────────────────────────────╯
Observations: 1599065.55                                                                                                                      

The following seven steps had been spent calculating the relative metric modifications utilizing the calculate_metric_increase instrument.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling instrument: 'calculate_metric_increase' with              │
│ arguments: {'earlier than': 1731985.21, 'after': 1599065.55}       │
╰──────────────────────────────────────────────────────────────╯
Observations: 7.674410799385517                               

<-- comparable instrument requires all nation segments -->            

In the long run, the agent put collectively a ultimate name. 

So, if the LLM had had instruments to calculate absolutely the distinction and the share of the sum, it could have taken an extra 14 iterations and much more tokens. After all, we are able to forestall such inefficiencies by fastidiously designing the instruments we offer:

• We may modify our capabilities to work with lists of values as a substitute of single gadgets, which might considerably cut back the variety of steps. 
• Moreover, we may create extra advanced capabilities that calculate all essential metrics without delay (just like what the code agent did). This fashion, LLM wouldn’t have to carry out calculations step-by-step. Nonetheless, this method may cut back the pliability of the system. 

Though the outcomes weren’t best resulting from a poor alternative of instruments, I nonetheless discover this instance fairly insightful. It’s clear that code brokers are extra highly effective, cost-efficient and versatile as they will invent their very own complete instruments and carry out a number of actions in a single step.

Yow will discover the whole code and execution logs on GitHub.

Abstract

We’ve discovered rather a lot in regards to the code brokers. Now, it’s time to wrap issues up with a fast abstract.

Code brokers are LLM brokers that “assume” and act utilizing Python code. As a substitute of calling instruments by way of JSON, they generate and execute precise code. It makes them extra versatile and cost-efficient as they will invent their very own complete instruments and carry out a number of actions in a single step. 

HuggingFace has introduced this way of living of their framework, smolagents. Smolagents makes it straightforward to construct fairly advanced brokers with out a lot trouble, whereas additionally offering security measures through the code execution. 

On this article, we’ve explored the fundamental performance of the smolagents library. However there’s much more to it. Within the subsequent article, we’ll dive into extra superior options (like multi-agent setup and planning steps) to construct the agent that may narrate KPI modifications. Keep tuned!

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.