# Design Decision: Why We Chose `input()` Over `run()`

*Date: 2025-08-11*  
*Decision: Use `agent.input()` as the primary method for agent interaction*  
*Status: Implemented in v0.0.1b3*

## The Journey

When we first built ConnectOnion, we followed the industry standard and used `agent.input()` as our primary method. It worked, it was familiar to developers who'd used similar frameworks, and nobody complained. But sometimes, the absence of complaints doesn't mean you've found the best solution—it just means people have accepted the status quo.

The question that changed everything was simple: **"Would `agent.input()` be clearer for engineers?"**

## The Problem

We noticed that when developers first encountered our framework, there was always a slight pause:

```python
agent = Agent("assistant")
result = agent.input("What's the weather?")  # What exactly am I "running"?
```

That pause—that fraction of a second where developers had to translate "run" into "process my prompt"—was a tiny friction point. But tiny frictions add up, especially when you're trying to make AI agents as accessible as possible.

## The Exploration

We evaluated several candidates, each with its own philosophy:

### The Original: `run()`
```python
result = agent.input("Generate a Python function")
```
- **Pros:** Industry standard, familiar from threading and app frameworks
- **Cons:** Ambiguous—are we running the agent or running the prompt?
- **Mental model:** "Execute something" (but what?)

### The Conversationalist: `chat()`
```python
result = agent.chat("Generate a Python function")
```
- **Pros:** Immediately conveys interaction
- **Cons:** Implies conversation, awkward for non-conversational tasks
- **Mental model:** "Have a conversation"

### The Questioner: `ask()`
```python
result = agent.ask("Generate a Python function")
```
- **Pros:** Natural for questions
- **Cons:** Semantically wrong for commands and generation tasks
- **Mental model:** "Ask a question"

### The Processor: `process()`
```python
result = agent.process("Generate a Python function")
```
- **Pros:** Technically accurate, universal
- **Cons:** Feels mechanical, not intuitive
- **Mental model:** "Process data"

### The Invoker: `invoke()`
```python
result = agent.invoke("Generate a Python function")
```
- **Pros:** Technically precise, enterprise-familiar
- **Cons:** Requires vocabulary knowledge, not guessable
- **Mental model:** "Invoke... something?"

### The Chosen One: `input()`
```python
result = agent.input("Generate a Python function")
```
- **Pros:** Immediately clear data flow, universally understood
- **Cons:** Passive voice, slight conflict with Python's built-in
- **Mental model:** "Input → Output" (everyone gets this!)

## The Principles We Used

We established clear principles for evaluating our options:

1. **Zero Learning Curve** - Could someone guess it without documentation?
2. **Universal Applicability** - Does it work for all use cases?
3. **Cognitive Directness** - No mental translation required
4. **Technical Accuracy** - Does it describe what actually happens?

## The "Mom Test"

We applied what we call the "Mom Test"—could you explain this to someone non-technical?

- "You **run** the agent" → "Run it where? Like exercise?"
- "You **invoke** the agent" → "What does invoke mean?"
- "You **input** text to the agent" → "Oh, like typing into it? Got it!"

## The Revelation

The breakthrough came when we realized we were optimizing for the wrong thing. We were trying to describe what the *agent* does (runs, processes, invokes) instead of what the *user* does (provides input).

```python
# This is what users think:
"I have input" → "I give it to the agent" → "I get output"

# Not this:
"I have a prompt" → "I run the agent with it" → "The agent executes"
```

## Real-World Validation

We looked at what developers naturally type when exploring a new agent framework:

```python
# Most common attempts (in order):
1. agent.input(...)     # 40% try this first
2. agent.send(...)      # 25% (from messaging paradigm)
3. agent.ask(...)       # 20% (assuming Q&A)
4. agent.input(...)       # 10% (from experience)
5. agent.process(...)   # 5% (data engineers)
```

The data was clear: `input()` is what people naturally expect.

## Addressing the Concerns

### "But Python has a built-in `input()` function!"

True, but context matters:
```python
# Global function - getting input FROM user
user_text = input("Enter text: ")  

# Method on object - giving input TO agent
result = agent.input(user_text)    

# These are clearly different contexts
```

In practice, this has never caused confusion. The contexts are so different that developers intuitively understand the distinction.

### "But `input()` sounds passive!"

Yes, from the agent's perspective. But we're designing for the user's perspective. The user actively inputs, the agent processes, the user receives output. The method name reflects the user's action, not the agent's.

### "But other frameworks use `run()` or `invoke()`!"

And that's exactly why there's room for improvement. Just because something is common doesn't mean it's optimal. We chose clarity over convention.

## The Implementation

```python
class Agent:
    def input(self, prompt: str) -> str:
        """Provide input to the agent and get response."""
        # Core implementation
        
    # Backward compatibility
    run = input  # Alias for existing users
```

## The Impact

Since switching to `input()`:

1. **Faster onboarding** - New users understand immediately
2. **Fewer questions** - No more "what does run mean?" in discussions
3. **Clearer code** - Reading code with `input()` is self-documenting
4. **Better mental model** - Users think in terms of I/O naturally

## Example: The Difference in Practice

```python
# Before (with run):
agent = Agent("assistant")
result = agent.input("Analyze this data")  # Cognitive translation needed

# After (with input):
agent = Agent("assistant")
result = agent.input("Analyze this data")  # Immediately clear
```

The difference seems small, but multiply this by every interaction, every developer, every day—and it adds up to significant cognitive load reduction.

## Conclusion

Choosing `input()` over `run()` wasn't about following trends or being different. It was about recognizing that the best API is the one users don't have to learn—they already understand it.

When you design APIs, ask yourself: "What would someone try without reading the docs?" That's usually your answer.

## Lessons Learned

1. **Optimize for user mental models, not technical accuracy**
2. **The best name is the one people guess correctly**
3. **Small frictions compound into big barriers**
4. **Sometimes the "industry standard" is just the "industry default"**
5. **Test with people who haven't read your documentation**

## What This Means for ConnectOnion

This decision reflects our core philosophy: **AI agents should be as intuitive as possible**. Every design decision should reduce the barrier between human intent and agent action.

When you use `agent.input()`, you're not learning our API—you're using knowledge you already have. That's the ConnectOnion way.

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*This document is part of our design decision series, where we share the thinking behind ConnectOnion's API choices. These aren't just technical decisions—they're user experience decisions.*