6 min read
The Evolution of Tool Use in AI: From Functions to Computer Control
Tool use in AI has evolved dramatically - from simple function calling to controlling entire computers. Let’s trace this evolution and understand where it’s heading.
The Tool Use Timeline
from dataclasses import dataclass
from typing import Dict, List, Any
from enum import Enum
from datetime import date
class ToolCapability(Enum):
TEXT_ONLY = "text_only" # 2022: Just text generation
FUNCTION_CALLING = "function_calling" # 2023: Structured function calls
PARALLEL_TOOLS = "parallel_tools" # 2024: Multiple simultaneous tools
COMPUTER_USE = "computer_use" # 2024: Full computer control
AUTONOMOUS_AGENTS = "autonomous_agents" # Future: Self-directed agents
@dataclass
class ToolEvolutionStage:
"""A stage in tool use evolution"""
name: str
year: int
capabilities: List[str]
limitations: List[str]
example_use_cases: List[str]
EVOLUTION_TIMELINE = [
ToolEvolutionStage(
name="Text Generation",
year=2022,
capabilities=[
"Generate text responses",
"Answer questions",
"Write content"
],
limitations=[
"No access to external data",
"No real-time information",
"Cannot take actions"
],
example_use_cases=[
"Chatbots",
"Content writing",
"Q&A systems"
]
),
ToolEvolutionStage(
name="Function Calling",
year=2023,
capabilities=[
"Call predefined functions",
"Access external APIs",
"Structured output generation",
"Multi-turn tool use"
],
limitations=[
"Functions must be predefined",
"Limited to API capabilities",
"No visual understanding"
],
example_use_cases=[
"API integrations",
"Data retrieval",
"Action automation"
]
),
ToolEvolutionStage(
name="Parallel Tools & Vision",
year=2024,
capabilities=[
"Multiple simultaneous tool calls",
"Image understanding",
"Strict schema enforcement",
"Tool choice control"
],
limitations=[
"Still limited to defined tools",
"No real-time interaction",
"Cannot observe results directly"
],
example_use_cases=[
"Complex data pipelines",
"Image analysis workflows",
"Multi-service orchestration"
]
),
ToolEvolutionStage(
name="Computer Use",
year=2024,
capabilities=[
"See screen content",
"Control mouse and keyboard",
"Interact with any application",
"Observe action results"
],
limitations=[
"Requires visual feedback loop",
"Slower than direct API calls",
"Safety concerns"
],
example_use_cases=[
"Legacy system automation",
"UI testing",
"Desktop workflow automation"
]
)
]Comparing Tool Paradigms
from openai import OpenAI
import anthropic
# Paradigm 1: Simple Function Calling (2023)
def simple_function_calling():
"""Original function calling pattern"""
client = OpenAI()
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"]
}
}
}]
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "What's the weather in Seattle?"}],
tools=tools
)
# Model decides to call function
# Developer executes and returns result
return response
# Paradigm 2: Parallel Tools with Strict Mode (2024)
def parallel_strict_tools():
"""Enhanced function calling with parallel execution"""
client = OpenAI()
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather",
"strict": True,
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"units": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location", "units"],
"additionalProperties": False
}
}
},
{
"type": "function",
"function": {
"name": "get_time",
"description": "Get current time",
"strict": True,
"parameters": {
"type": "object",
"properties": {
"timezone": {"type": "string"}
},
"required": ["timezone"],
"additionalProperties": False
}
}
}
]
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Weather and time in Tokyo?"}],
tools=tools,
parallel_tool_calls=True # Can call both simultaneously
)
return response
# Paradigm 3: Computer Use (2024)
def computer_use_paradigm():
"""Full computer control paradigm"""
client = anthropic.Anthropic()
tools = [
{
"type": "computer_20241022",
"name": "computer",
"display_width_px": 1920,
"display_height_px": 1080
}
]
# Model can now see and interact with any UI
response = client.beta.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=4096,
tools=tools,
messages=[{
"role": "user",
"content": "Open the weather app and check Seattle's weather"
}],
betas=["computer-use-2024-10-22"]
)
return responseHybrid Tool Strategies
class HybridToolAgent:
"""Agent that uses multiple tool paradigms"""
def __init__(self):
self.openai_client = OpenAI()
self.anthropic_client = anthropic.Anthropic()
self.api_tools = self._define_api_tools()
def execute_task(self, task: str) -> dict:
"""Execute task using best tool paradigm"""
# Analyze task to determine best approach
approach = self._select_approach(task)
if approach == "api":
return self._use_api_tools(task)
elif approach == "computer":
return self._use_computer_control(task)
else:
return self._hybrid_approach(task)
def _select_approach(self, task: str) -> str:
"""Determine the best tool approach for a task"""
response = self.openai_client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": """Analyze this task and determine the best approach:
- "api": Task can be done with API calls (faster, more reliable)
- "computer": Task requires UI interaction (legacy apps, visual tasks)
- "hybrid": Task needs both approaches
Return just the approach name."""
},
{"role": "user", "content": task}
]
)
return response.choices[0].message.content.strip().lower()
def _use_api_tools(self, task: str) -> dict:
"""Use traditional API tools"""
response = self.openai_client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": task}],
tools=self.api_tools
)
return {
"approach": "api",
"response": response,
"latency": "fast"
}
def _use_computer_control(self, task: str) -> dict:
"""Use computer control for UI tasks"""
tools = [{
"type": "computer_20241022",
"name": "computer",
"display_width_px": 1920,
"display_height_px": 1080
}]
response = self.anthropic_client.beta.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=4096,
tools=tools,
messages=[{"role": "user", "content": task}],
betas=["computer-use-2024-10-22"]
)
return {
"approach": "computer",
"response": response,
"latency": "slow"
}
def _hybrid_approach(self, task: str) -> dict:
"""Combine API and computer control"""
# Break down task
subtasks = self._decompose_task(task)
results = []
for subtask in subtasks:
approach = self._select_approach(subtask)
if approach == "api":
result = self._use_api_tools(subtask)
else:
result = self._use_computer_control(subtask)
results.append(result)
return {
"approach": "hybrid",
"subtask_results": results
}The Future: Autonomous Tool Creation
class FutureToolAgent:
"""Speculative: Agent that creates its own tools"""
def __init__(self):
self.client = OpenAI()
self.dynamic_tools = {}
def create_tool_for_task(self, task_description: str) -> dict:
"""Generate a new tool to accomplish a task"""
response = self.client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": """Design a tool to accomplish this task.
Return JSON with:
- tool_definition: OpenAI function schema
- implementation: Python code for the function
- dependencies: Required packages"""
},
{"role": "user", "content": task_description}
],
response_format={"type": "json_object"}
)
import json
tool_spec = json.loads(response.choices[0].message.content)
# In the future, this could safely execute generated code
# For now, this is speculative
return tool_spec
def adapt_tool(self, tool_name: str, error_message: str) -> dict:
"""Adapt an existing tool based on error feedback"""
if tool_name not in self.dynamic_tools:
return {"error": "Tool not found"}
current_spec = self.dynamic_tools[tool_name]
response = self.client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": "Modify this tool to fix the error encountered."
},
{
"role": "user",
"content": f"""
Current tool: {json.dumps(current_spec)}
Error encountered: {error_message}
Provide updated tool specification.
"""
}
],
response_format={"type": "json_object"}
)
return json.loads(response.choices[0].message.content)The evolution of tool use in AI represents a fundamental shift from constrained function execution to general-purpose computer interaction. This trajectory points toward truly autonomous AI systems that can adapt and create their own capabilities.