#3781 How AI Agents are Not just New-Fangled Functions and Methods

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#3781 How AI Agents are Not just New-Fangled Functions and Methods

#3781 Explain why AI agents are not simply the new functions or methods, programmed in natural language, and able to access AI

AI agents aren't just new functions or methods because they're not limited to a predefined set of rules or a single task. Unlike traditional software, which is reactive and follows a static, hard-coded path, AI agents are proactive, autonomous, and goal-driven. They can handle complex, multi-step workflows, make decisions on their own, and adapt to changing environments.

Core Distinctions from Traditional Functions

A function or a method in traditional programming is a self-contained block of code that performs a specific, often singular, task. It takes a set of inputs and, based on a fixed algorithm, produces an output. AI agents, on the other hand, operate on a different paradigm, with a sense-plan-act-reflect cycle, which mimics human problem-solving.

  • Autonomy and Proactivity: While a function is reactive, only executing when called, an AI agent can initiate actions and pursue a goal without continuous human intervention. For example, a function might be coded to "send a welcome email" when a new user signs up. An AI agent, however, could be given the goal "onboard new customers" and would proactively manage the entire process, including sending the welcome email, scheduling follow-up messages, and checking for user engagement.

  • Adaptability and Learning: Traditional functions are rigid; if an unexpected input is provided, they'll either fail or produce an incorrect output. AI agents, powered by large language models (LLMs), are designed to adapt to new situations and learn from their experiences. They can dynamically adjust their plan or course of action based on new information or feedback. This is a fundamental difference: agents learn, while functions must be manually updated by a developer.

  • Planning and Reasoning: A function's logic is predetermined. It doesn't plan. An AI agent, when given a complex goal, can break it down into smaller, manageable subtasks, reason about the best sequence of actions, and even orchestrate multiple tools or other agents to achieve the final objective. For example, to "plan a vacation," an agent might first research flights, then hotels, then local activities, and finally book all the components by using different external tools (APIs) for each step.


Does not sound deterministic

Correct, AI agents are not deterministic, which is one of the key reasons they are different from traditional functions or methods.

Non-Determinism in AI Agents 🤖

Determinism means that for a given input, a program will always produce the exact same output. Traditional functions are deterministic by design. For example, a function that adds two numbers will always return the same sum for the same two numbers.

AI agents, however, are probabilistic and non-deterministic due to their core technology: Large Language Models (LLMs). This means that even with the same input, an AI agent's response can vary. Here's why:

  • LLM Randomness: LLMs generate text by predicting the next word or "token" based on probabilities. They don't follow a fixed set of rules. Parameters like "temperature" introduce intentional randomness to make the output more creative and less repetitive. Even with the temperature set to zero, there can be slight variations in the output due to the inherent nature of the models.

  • Adaptive Behavior: Unlike a function that has a fixed, single path, an AI agent's "thinking" process is a dynamic, multi-step loop. It plans, acts, and reflects. For a single goal, an agent might decide to take different paths based on real-time feedback, tool outputs, or context. This adaptability is the source of its non-deterministic behavior. For example, a "research" agent might choose to search different websites or rephrase its queries if the initial search results are not satisfactory. This creates a variable, non-linear path to the final goal.

  • Contextual Sensitivity: AI agents are extremely sensitive to context. A slight change in a user's prompt or the information available in the environment can lead to a completely different series of actions and outputs. This makes their behavior feel unpredictable to users accustomed to traditional software that either works or fails in a predictable way.

This non-deterministic nature is a double-edged sword: it allows for incredible flexibility, creativity, and adaptability, but it also introduces challenges with reliability, consistency, and debugging in a way that is foreign to traditional software development.

The provided video offers a quick explanation of how non-determinism affects AI projects and what developers need to know about it. Evaluating and Debugging Non-Deterministic AI Agents


From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro

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