LLM Agents Beyond Utility: An Open-Ended Perspective

This paper investigates whether LLM agents can transcend pure utility and operate as open-ended entities by augmenting them with self-task generation, knowledge accumulation, and broad environmental interaction. The study qualitatively characterizes both the emergent capabilities and fundamental limitations of such open-ended agents built on pretrained LLMs.

Problem Statement

Current LLM agents are predominantly designed as narrow problem-solving tools optimized for user-defined utility functions, leaving open the question of whether they can pursue broader, self-directed, and ambiguous goals. This limits their potential as autonomous agents capable of long-term exploration and self-improvement. The research addresses the gap between current agentic AI and open-ended intelligence by empirically studying what pretrained LLMs can and cannot do when freed from fixed task specifications.

Key Novelty

Open-ended experimental framework that augments a pretrained LLM agent with autonomous task generation capabilities, allowing the agent to define and pursue its own goals
Qualitative study of emergent behaviors in open-ended LLM agents, including cross-run knowledge accumulation and self-directed planning beyond utility optimization
Systematic characterization of the current limits of pretrained LLMs toward open-endedness, including sensitivity to prompt design, repetitive task generation, and inability to form self-representations

Evaluation Highlights

Agent reliably follows complex multi-step instructions and can store/reuse information across runs, demonstrating functional memory and knowledge persistence
Agent can propose and solve its own tasks but exhibits prompt sensitivity, repetitive task generation patterns, and lacks stable self-representation — highlighting key failure modes for open-ended behavior

Breakthrough Assessment

5/10 The paper makes a solid conceptual contribution by framing and empirically probing open-ended LLM agency, but remains largely qualitative without novel training methods or architectural innovations, placing it as an exploratory study that maps the problem space rather than solving it.

Methodology

Augment a pretrained LLM agent with modules for self-task generation, persistent knowledge storage, and rich environment interaction to create an open-ended agent scaffold
Deploy the agent in an open-ended experimental setting without fixed utility objectives, allowing it to autonomously generate, plan, and execute its own tasks across multiple runs
Qualitatively analyze agent behavior across dimensions including instruction-following, memory reuse, task diversity, prompt sensitivity, and self-representation to characterize capabilities and failure modes

System Components

Task Generation Module

Enables the agent to propose its own tasks rather than receiving them externally, supporting self-directed exploration

Knowledge Accumulation System

Persistent memory mechanism allowing the agent to store information from previous runs and reuse it in future interactions

Chain-of-Thought Reasoning

Multi-step reasoning backbone leveraging the pretrained LLM's ability to decompose complex goals and plan sequences of actions

Function Calling Interface

Structured mechanism for the agent to interact with its environment by invoking external tools and APIs

Open-Ended Evaluation Framework

Experimental setting designed to assess agent behavior under self-directed, ambiguous goals rather than fixed benchmarks

Results

Capability/Behavior	Standard LLM Agent	Open-Ended Agent	Observation
Complex multi-step instruction following	Capable (prompted)	Reliable	Maintained with open-ended scaffold
Cross-run knowledge reuse	Not present	Functional	Enabled by persistent memory module
Self-task generation	Not present	Functional but repetitive	Prone to low-diversity task proposals
Self-representation formation	Absent	Absent	No improvement; identified as key limitation
Prompt sensitivity	Moderate	High	Open-ended setting amplifies prompt dependence

Key Takeaways

Persistent memory and self-task generation can be bolted onto pretrained LLMs to enable rudimentary open-ended behavior, but prompt engineering remains a critical and fragile dependency that practitioners must carefully manage
Repetitive task generation is a concrete failure mode when LLMs self-direct — practitioners building autonomous agents should incorporate explicit diversity mechanisms or novelty rewards to drive productive exploration
Achieving genuine open-endedness likely requires training-time interventions (e.g., intrinsic motivation, self-model learning) rather than inference-time augmentation alone, pointing to a clear research gap for those developing next-generation agentic systems

Abstract

Recent LLM agents have made great use of chain of thought reasoning and function calling. As their capabilities grow, an important question arises: can this software represent not only a smart problem-solving tool, but an entity in its own right, that can plan, design immediate tasks, and reason toward broader, more ambiguous goals? To study this question, we adopt an open-ended experimental setting where we augment a pretrained LLM agent with the ability to generate its own tasks, accumulate knowledge, and interact extensively with its environment. We study the resulting open-ended agent qualitatively. It can reliably follow complex multi-step instructions, store and reuse information across runs, and propose and solve its own tasks, though it remains sensitive to prompt design, prone to repetitive task generation, and unable to form self-representations. These findings illustrate both the promise and current limits of adapting pretrained LLMs toward open-endedness, and point to future directions for training agents to manage memory, explore productively, and pursue abstract long-term goals.