Code Agents: The Future of Agentic AI

of AI brokers. LLMs are not simply instruments. They’ve develop into lively individuals in our lives, boosting productiveness and reworking the best way we dwell and work. 

• OpenAI just lately launched Operator, an AI agent that may autonomously carry out varied duties, from shopping the net to filling out types and scheduling appointments.
• Anthropic launched MCP (Model Context Protocol), a brand new normal for the way AI assistants work together with the skin world. With over 5 thousand lively 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 will not be solely improving developer productivity and code quality but additionally democratising the sphere, making software program growth accessible to folks with no technical background.

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

On this article, we are going to discover a number of matters:

• What code brokers are (teaser: it’s not associated to vibe coding).
• The right way to use the HuggingFace smolagents framework in apply. 
• 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 packages the place LLM outputs management the workflow.

So, we want an agentic circulate after we desire a system to purpose and act primarily based on observations. Truly, company just isn’t a binary variable (sure or no), however a spectrum. 

• At one finish, we will 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 subsequent stage is routing, the place an LLM can classify an incoming query and resolve 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 increased levels of company. These would possibly embody the power 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 identify 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 unravel complicated 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, due to the huge quantity of obtainable knowledge for coaching.

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

• By producing code, an LLM just isn’t restricted to a predefined set of instruments and may create its personal features.
• It may mix a number of instruments inside a single motion utilizing circumstances and loops, which helps scale back the variety of steps required to finish a process.  
• It additionally allows the mannequin to work with a greater diversity of outputs, equivalent to producing charts, pictures, or different complicated objects.

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

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

HuggingFace smolagents framework

First strive

Fortunately, we don’t must construct code brokers from scratch, as HuggingFace has launched a helpful library referred to 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 through LiteLLM. Nonetheless, the framework helps different choices as effectively. You should use a neighborhood mannequin through Ollama or TransformersModel, or public fashions through Inference Providers or select different choices (you will discover 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, equivalent to a Python interpreter or DuckDuckGo search. Let’s strive 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 potential mixtures of the balls I can get?""",
)

Consequently, we see a extremely properly formatted execution circulate. It’s simply superb 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 will go even deeper and take a look at the whole info associated to execution (together with prompts), through agent.reminiscence.steps. Let’s take a look at the system immediate utilized by the agent. 

You might be an professional assistant who can remedy any process utilizing code blobs. 
You can be given a process to unravel as finest you possibly can.

To take action, you may have been given entry to an inventory of instruments: these instruments 
are principally Python features which you'll name with code.

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

At every step, within the 'Thought:' sequence, it's 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's best to write the code in easy 
Python. The code sequence should finish with '' sequence.

Throughout every intermediate step, you should use 'print()' to avoid wasting 
no matter necessary info you'll then want.

These print outputs will then seem within the 'Statement:' area, 
which shall be out there as enter for the subsequent step.

Ultimately you must return a ultimate reply utilizing 
the final_answer instrument.

Listed here are just a few 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 method.

The smolagents library handles all behind-the-scenes work concerned within the agent workflow: assembling the system immediate with all obligatory info for the LLM (i.e. out there instruments), parsing the output and executing the generated code. It additionally supplies 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 possibly can customise this behaviour. For instance, you possibly can take away some middleman outcomes from the reminiscence to scale back the variety of tokens or execute the agent step-by-step. Whereas we gained’t dive deep into reminiscence administration right here, you will discover helpful 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 increased dangers. Certainly, an LLM can run dangerous code both by mistake (since LLMs are nonetheless removed from good) or on account of 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 will not be allowed until the package deal has been explicitly added to additional_authorized_imports listing. 
• Furthermore, submodules are blocked by default, so you could 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 full variety of executed operations is capped, stopping infinite loops and useful resource bloating. 
• Any operation not explicitly outlined within the interpreter will elevate 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"])

# operate to have fairly formatted exceptions
def run_capture_exception(command: str):
    strive:
        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' on account of: 
# InterpreterError: Import of os just isn't allowed. Licensed imports 
# are: ['datetime', 'itertools', 're', 'math', 'statistics', 'time', 'queue', 
# 'numpy.*', 'random', 'collections', 'unicodedata', 'stat']

# Submodules are additionally blocked until said 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('')' 
# on account of: InterpreterError: Forbidden entry to module: os

# The cap on the variety of iterations breaks inifinity loops 
harmful_command = '''
whereas True: 
    move
'''
run_capture_exception(harmful_command)
# ERROR: Code execution failed at line 'whereas True: move' on account of: 
# 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 on account of: SyntaxError

It appears we have now some security nets with code brokers. Nonetheless, regardless of these safeguards, dangers persist whenever you’re executing code domestically. For instance, an LLM can recursively create threads in your pc or create too many information, resulting in useful resource bloating. A potential answer is to execute code in a sandboxed atmosphere, equivalent to utilizing Docker or options like E2B. I’m keen to be adventurous and run my code domestically, however should you want a extra risk-averse method, you possibly can comply with the sandbox set-up steering in the documentation.

Code agent vs conventional Software-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’ll 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 lessons, which we will use to match two approaches: 

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

Our brokers will want some instruments, so let’s implement them. There are multiple options to create tools in smolagents: we will re-use LangChain instruments, obtain them from HuggingFace Hub or just create Python features. We’ll take essentially the most easy method by writing a few Python features 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 shall be utilizing these instruments, offering detailed descriptions is essential. 

@instrument 
def calculate_metric_increase(earlier than: float, after: float) -> float: 
    """
    Calculate the proportion 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 offered 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 is likely to 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 
)

process = """
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 share.
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(
    process,
    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 end result 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 circulate. The LLM acquired 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          │
│ share.                                                    │
│ 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 gotten been supplied with these further 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 ────────────────────────────╯

In step one, the LLM generated a dataframe and carried out all calculations. Apparently, it selected to put in writing all of the code independently reasonably than utilizing the offered instruments. 

Much more surprisingly, the LLM recreated the dataframe primarily based on the enter knowledge as an alternative of referencing it instantly. This method just isn’t ultimate (particularly when working with large datasets), as it may result in errors and better token utilization. This behaviour might probably be improved through the use of a extra specific system immediate. Right here’s the code the agent executed in step one.

import pandas as pd                                                                                                        
 
# Creating the DataFrame from the offered 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 share 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 effectively.

ToolCallingAgent

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

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

process = """
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 share.
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(
    process,
    additional_args={"knowledge": df},
)

The outcomes are removed from ultimate: solely the relative modifications are appropriate, whereas the remainder of the numbers are pure hallucinations. I’ve to confess, the core situation was the dearth of applicable instruments (particularly, instruments to calculate variations and to estimate shares). Nonetheless, the agent ought to have flagged lacking instruments reasonably 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 price 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 the best way 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                                   
Software 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                                   
Software 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 subsequent seven steps have 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 -->            

Ultimately, 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 a further 14 iterations and much more tokens. After all, we will forestall such inefficiencies by rigorously designing the instruments we offer:

• We might modify our features to work with lists of values as an alternative of single gadgets, which might considerably scale back the variety of steps. 
• Moreover, we might create extra complicated features that calculate all obligatory metrics directly (just like what the code agent did). This fashion, LLM wouldn’t must carry out calculations step-by-step. Nonetheless, this method would possibly scale back the pliability of the system. 

Regardless that the outcomes weren’t ultimate on account of 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’ll invent their very own complete instruments and carry out a number of actions in a single step.

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

Abstract

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

Code brokers are LLM brokers that “suppose” and act utilizing Python code. As an alternative of calling instruments through JSON, they generate and execute precise code. It makes them extra versatile and cost-efficient as they’ll 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 complicated brokers with out a lot problem, whereas additionally offering security measures throughout the code execution. 

On this article, we’ve explored the essential performance of the smolagents library. However there’s much more to it. Within the subsequent article, we are going to 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 a large number 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” quick course by DeepLearning.AI.