Function Calling vs. Agentic AI: Understanding the Distinctions and Capabilities

Mladen Milanovic

Vice President, Automation

Published Jan 8, 2025

Large Language Models (LLMs) have transformed natural language processing, powering applications from chatbots to code assistance. While both function calling and Agentic AI extend LLM capabilities, understanding their distinct characteristics, limitations, and appropriate use cases is crucial for effective implementation. This article explores these technologies, their relationships, and practical considerations for implementation.

Function Calling: Bridging LLMs and External Systems

Function calling enables LLMs to interact with external systems through structured outputs representing calls to pre-defined functions. This capability allows LLMs to:

Identify when external actions are needed
Select appropriate functions from a defined set
Extract and format necessary arguments
Receive and process function results

When implementing function calling, several key aspects require attention:

Function Definition

{
    "name": "get_weather",
    "description": "Get current weather for a location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City name or coordinates"
            }
        },
        "required": ["location"]
    }
}

Input Validation

Parameter type checking
Value range validation
Security sanitization

Error Handling

Invalid parameter handling
External service failures
Rate limiting considerations

Security Risks

Protecting against prompt injections or malicious input.
Ensuring output from external systems is sanitized before further processing.

Agentic AI: Beyond Simple Function Calls

Agentic AI represents systems that maintain state, make decisions, and pursue goals over multiple interactions. Key characteristics include:

Core Capabilities

State Management

Maintaining context across interactions
Tracking progress toward goals
Managing resource utilization

Planning and Decision Making

Breaking complex goals into subtasks
Adapting to changing conditions
Handling unexpected situations

Goal-Directed Behavior

Understanding and maintaining objectives
Prioritizing actions
Evaluating success criteria

Current Limitations

It's important to understand current constraints:

Limited true autonomy due to bounded computational resources and predefined action spaces
Challenges in maintaining state consistency over extended interactions
Difficulties with long-term planning, including temporal consistency and adaptive prioritization
Security risks, such as vulnerabilities to malicious inputs or poorly defined action spaces

Architecture Comparison

The architectural differences between function calling and Agentic AI are highlighted below:

Function Calling Architecture

Article content — Function Calling Architecture

Agentic AI Architecture

Implementation Considerations

Security

Input Validation

Strict parameter validation
Content sanitization
Rate limiting

Execution Environment

Sandboxed environments
Resource limitations
Access control

Mitigating Prompt Injections

Use controlled input-output channels to avoid malicious exploitation.
Apply filters to sanitize function parameters and responses.

Error Handling

Function calling often requires strict error handling mechanisms.

def execute_function_call(function_name: str, parameters: dict) -> Result:
    try:
        # Validate parameters
        validated_params = validate_parameters(parameters)
        
        # Execute in controlled environment
        with resource_limits():
            result = function_registry[function_name](**validated_params)
            
        return Success(result)
    except ValidationError as e:
        return Failure(f"Invalid parameters: {e}")
    except ResourceExceeded as e:
        return Failure(f"Resource limits exceeded: {e}")
    except Exception as e:
        return Failure(f"Unexpected error: {e}")

For Agentic AI, state consistency is a critical challenge.

class AgentState:
    def __init__(self):
        self.current_goal = None
        self.action_history = []
        self.resources = ResourceTracker()
        self.constraints = ConstraintSet()
    
    def update_state(self, action_result: ActionResult):
        self.action_history.append(action_result)
        self.resources.update(action_result.resource_usage)
        self.evaluate_constraints()

Real-World Applications and Frameworks