Code Agents: The Future of Agentic AI

of AI brokers. LLMs are now not simply instruments. They’ve change into energetic contributors in our lives, boosting productiveness and remodeling the way in which we stay and work. 

• OpenAI not too long ago launched Operator, an AI agent that may autonomously carry out numerous duties, from looking the online 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 skin 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 usually are not solely improving developer productivity and code quality but in addition democratising the sphere, making software program growth accessible to folks with no technical background.

We’ve beforehand checked out totally different AI Agent frameworks, similar to LangGraph or CrewAI. On this article, I want to talk about a brand new one I’ve been exploring not too long ago — HuggingFace smolagents. It’s an attention-grabbing framework because it implements the idea of code brokers.

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

• What code brokers are (teaser: it’s not associated to vibe coding).
• Easy methods to use the HuggingFace smolagents framework in apply. 
• Whether or not it’s safe to present 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’d like an agentic stream after we desire a system to purpose and act based mostly on observations. Really, company just isn’t 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 subsequent stage is routing, the place an LLM can classify an incoming query and resolve which path to take —  for instance, calling a software 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 larger levels of company. These would possibly embody 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 software calls within the type of code, in distinction to the extra conventional strategy utilizing a JSON format with the software identify and arguments.

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

It is sensible when you concentrate on it. We’ve been creating programming languages for many years to resolve complicated issues. So, it’s pure that these languages are higher suited to LLM’s duties than easy JSON configs. An extra profit is that LLMs are already fairly good at writing code in frequent programming languages, due to the huge quantity of obtainable information for coaching.

This strategy 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 might create its personal features.
• It might probably mix a number of instruments inside a single motion utilizing circumstances and loops, which helps cut back the variety of steps required to finish a job.  
• It additionally permits the mannequin to work with a greater variety of outputs, similar to producing charts, photographs, or different complicated objects.

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

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

HuggingFace smolagents framework

First attempt

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

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’d like 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 effectively. You should use a neighborhood mannequin by way of Ollama or TransformersModel, or public fashions by way of 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, similar 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 combos of the balls I can get?""",
)

Because of this, we see a extremely properly formatted execution stream. 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 combos. 

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 resolve any job utilizing code blobs. 
You may be given a job to resolve as greatest you'll be able to.

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

To unravel the duty, you have to plan ahead to proceed in a collection of 
steps, in a cycle of 'Thought:', 'Code:', 
and 'Commentary:' sequences.

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

Then within the 'Code:' sequence, you need to write the code in easy 
Python. The code sequence should finish with '' sequence.

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

These print outputs will then seem within the 'Commentary:' area, 
which might be out there as enter for the following step.

Ultimately you must return a closing reply utilizing 
the final_answer software.

Listed below are just a few examples utilizing notional instruments: <.../>

It’s fairly clear that smolagents implements the ReAct strategy (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 vital info for the LLM (i.e. out there instruments), parsing the output and executing the generated code. It additionally gives 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 will discover helpful code examples in the documentation.

Safety

Now, it’s time to debate the drawbacks of the code brokers’ strategy. Giving an LLM extra company by permitting it to execute arbitrary code introduces larger dangers. Certainly, an LLM can run dangerous code both by mistake (since LLMs are nonetheless removed from excellent) or as a consequence 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 usually are not allowed except the bundle has been explicitly added to additional_authorized_imports checklist. 
• Furthermore, submodules are blocked by default, so you have to authorise them particularly (i.e. numpy.* ). It’s been performed as a result of some packages can expose doubtlessly 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 increase an error. 

Let’s take a look at whether or not these security measures really 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):
    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' as a consequence of: 
# InterpreterError: Import of os just isn't 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('')' 
# as a consequence of: InterpreterError: Forbidden entry to module: os

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

It appears we have now 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 information, resulting in useful resource bloating. A doable answer is to execute code in a sandboxed surroundings, similar to utilizing Docker or options like E2B. I’m prepared to be adventurous and run my code domestically, however for those who choose a extra risk-averse strategy, 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 strategy. Let’s put this to the take a look at. 
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 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 features. We’ll take essentially the most simple strategy by writing a few Python features and annotating them with @software. 

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

@software 
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

@software 
def calculate_sum(values: checklist) -> float: 
    """
    Calculate the sum of checklist

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

Teaser: I’ll later realise that I ought to have offered extra instruments to the agent, however I genuinely ignored 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 could 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 
)

job = """
Here's a dataframe exhibiting income by section, evaluating values 
earlier than and after.
Might you please assist me perceive the adjustments? 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(
    job,
    additional_args={"information": df},
)

General, 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 stream. The LLM acquired the next immediate. 

╭─────────────────────────── New run ────────────────────────────╮
│                                                                │
│ Here's a pandas dataframe exhibiting income by section,         │
│ evaluating values earlier than and after.                             │
│ Might you please assist me perceive the adjustments?               │
│ 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 might have been supplied with these further 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 write down all of the code independently fairly than utilizing the offered instruments. 

Much more surprisingly, the LLM recreated the dataframe based mostly on the enter information as a substitute of referencing it straight. This strategy just isn’t supreme (particularly when working with huge datasets), as it will possibly result in errors and better token utilization. This behaviour may doubtlessly 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 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("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 de facto 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 drawback. 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,
)

job = """
Here's a dataframe exhibiting income by section, evaluating values 
earlier than and after.
Might you please assist me perceive the adjustments? 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.
"""

traditional_agent.run(
    job,
    additional_args={"information": df},
)

The outcomes are removed from supreme: solely the relative adjustments are appropriate, whereas the remainder of the numbers are pure hallucinations. I’ve to confess, the core situation was the shortage of applicable instruments (particularly, instruments to calculate variations and to estimate shares). Nonetheless, the agent ought to have flagged lacking instruments fairly than producing random numbers.

Whole income change: -7319.66 (-7.67%). Income Adjustments 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 whole change:
- Different: 0.03%
- UK: -59.88%
- France: -181.77%
- Germany: -9.43%
- Italy: +13.38%
- Spain: -0.03%

When it comes to 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 on the subject of agent execution.

Let’s dig a bit deeper to know what the agent really did. First, it took 4 iterations simply to determine tips on how to name calculate_sum accurately and estimate the overall income earlier than and after. 

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling software: '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 software '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 need to name this software with appropriate enter arguments.       
Anticipated inputs: {"values": {"kind": "object",                
"description": "checklist of values"}}                             
Returns output kind: quantity                                   
Device description: 'Calculate the sum of checklist'                 
[Step 1: Duration 4.06 sec | Input tokens: 1,190 | Output: 250]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling software: '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 software '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 need to name this software with appropriate enter arguments.       
Anticipated inputs: {"values": {"kind": "object",                
"description": "checklist of values"}}                             
Returns output kind: quantity                                   
Device description: 'Calculate the sum of checklist'                 
[Step 2: Duration 8.60 sec | Input tokens: 2,815 | Output: 386]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
╭──────────────────────────────────────────────────────────────╮
│ Calling software: '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 software: '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 adjustments utilizing the calculate_metric_increase software.

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

            

Ultimately, the agent put collectively a closing name. 

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

• We may modify our features 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 complicated features that calculate all vital 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 strategy would possibly cut back the pliability of the system. 

Despite the fact that the outcomes weren’t supreme as a consequence 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 will 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 realized 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 “assume” and act utilizing Python code. As an alternative 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 lifestyle of their framework, smolagents. Smolagents makes it simple to construct fairly complicated brokers with out a lot problem, whereas additionally offering security measures through 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’ll dive into extra superior options (like multi-agent setup and planning steps) to construct the agent that may narrate KPI adjustments. 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” brief course by DeepLearning.AI.