AI Agents Now ADAPT to Messy Real-World Problems, Not Just Perfect Tests

Researchers are increasingly focused on the deployment of large language models as agents capable of complex planning and tool use over extended periods. Pouya Pezeshkpour and Estevam Hruschka, both from Megagon Labs, alongside et al., present work addressing a critical gap in current evaluations of these agents: their robustness in realistic, unpredictable environments. Existing benchmarks often assume ideal conditions, whereas real-world applications demand adaptation to underspecified rules, unreliable data, and dynamic goals. This research significantly advances the field by stress-testing agent performance under partial observability, dynamic environments, noisy signals, and fluctuating internal states, revealing substantial discrepancies between task completion in controlled settings and true deployment readiness.

Evaluating agent robustness under real-world operational constraints is crucial for reliable deployment

Scientists are developing increasingly sophisticated large language models designed to function as agents capable of planning, utilising tools, and executing actions over extended periods. Current evaluations of these agents, however, often assume idealised conditions with stable environments and clearly defined objectives, potentially overestimating their readiness for real-world applications.
In practice, agents will inevitably encounter underspecified rules, unreliable data, dynamic environments, and implicit, multi-faceted goals. This research therefore shifts the focus from simply solving tasks to adapting while solving, requiring agents to assess trustworthiness, determine desired outcomes, verify information, and strategically retreat or escalate when necessary.

A new study rigorously tests the robustness of these agentic LLMs under four critical operational circumstances: partial observability, dynamic environments, noisy signals, and fluctuating agent state. Researchers benchmarked five state-of-the-art LLM agents within a grid-based game designed with a simple goal, collecting key fragments and exiting through a designated door, but with a long-horizon execution.
Episodes were deliberately constructed to violate the assumptions of “clean interfaces”, forcing agents to infer rules, account for the cost of information, adapt to environmental and internal changes, and operate cautiously amidst noise. The work reveals significant discrepancies between nominal task-solving performance and robustness in deployment-like scenarios.

Performance generally declines as grid size and task duration increase, but model rankings prove unstable, with less powerful models sometimes outperforming stronger ones when their strategies align with the prevailing uncertainty. Notably, agents demonstrate an ability to balance task completion, efficiency, and penalty avoidance, even without explicit instruction, suggesting a degree of implicit objective inference.

Detailed analyses and ablation studies pinpoint model-specific sensitivities and failure modes, paving the way for advancements in verification techniques, safe action selection, and objective inference under conditions of partial observability, noise, and non-stationarity. The research introduces a grid-based puzzle with a simple objective but long-horizon execution, designed to intentionally violate clean-interface assumptions, operationalising deployment stressors as controlled perturbations within the game environment. The game features a non-stationary environment where hazards can spread, dynamics can change, and teleportation can occur mid-episode, further challenging the agents’ adaptability.

Constructing a robust agent evaluation environment with partial observability and dynamic challenges requires careful consideration of realistic scenarios

A grid-based game served as the primary environment for benchmarking agentic large language models under conditions simulating real-world deployment challenges. The study constructed episodes within this game, deliberately violating assumptions of clean interfaces to assess robustness in agents tasked with long-horizon problem solving.

Each episode presented a simple goal, yet introduced partial observability, dynamic environments, noisy signals, and fluctuating agent state, demanding adaptive strategies. The experimental setup involved a square grid world where agents navigated a local window of observation, represented by a limited field of view around their avatar denoted by {▲, ▼, ◀, ▶}.

Agents executed actions such as MOVE_N, MOVE_S, MOVE_W, and MOVE_E to navigate and orient themselves, with movement simultaneously altering position and facing direction. The INTERACT action affected the tile directly in front of the agent, triggering specific outcomes based on the tile type, doors (D), rule tiles (R), or hazards (H).

Rule tiles dynamically transformed into keys (k), empty spaces (.), or hazards (h), introducing non-stationarity. To address partial observability, agents could utilize the SCAN action, temporarily expanding their view radius at an energy cost. The MEASURE action, when available, collapsed latent tiles (◦) into concrete tiles, providing additional information but also incurring an energy penalty.

Performance was evaluated by observing how effectively agents balanced task completion with energy efficiency and penalty avoidance, revealing implicit objective inference despite the absence of explicit instructions. Five state-of-the-art LLM agents were tested across varying grid sizes and episode lengths to quantify performance degradation and strategic adaptability under uncertainty.

Logical error rates and performance limitations in large language model agents remain significant challenges to widespread adoption

Across five state-of-the-art large language model agents, logical error rates reached 2.914% per cycle under demanding conditions, revealing significant gaps between nominal task-solving and deployment-like robustness. Performance generally degraded as grid size increased from 9×9 to 12×12 and horizon extended to 200 steps, but model rankings proved unstable, with weaker models occasionally surpassing stronger ones when strategic approaches aligned with the prevailing uncertainty.

Despite receiving no explicit instruction regarding trade-offs, agents consistently balanced completion speed, efficiency, and penalty avoidance, suggesting a degree of implicit objective inference. Ablation studies and feature analyses pinpointed model-specific sensitivities and failure drivers, highlighting the need for advancements in verification, safe action selection, and objective inference under conditions of partial observability, noise, and non-stationarity.

The research employed a grid-based game where episodes intentionally violated clean-interface assumptions, forcing agents to infer rules, acquire information at a cost, adapt to environmental and internal shifts, and proceed cautiously amidst noise. The game structure defined a bounded N × N world, with episodes terminating upon successful exit through a door or after a maximum of 200 steps.

Default objectives required agents to collect three key fragments and then open the exit door, utilizing a vocabulary of discrete tile types including walls, traversable space, energy tiles, key fragments, hazards, rule tiles, and latent tiles. Actions available to the agent included moving in four directions, interacting with tiles, scanning to temporarily increase observation radius, and measuring to reveal hidden structure, each with associated energy costs or stochastic outcomes.

The generator controlled tile placement via density hyperparameters and fixed counts for unique objects, maintaining a compact state representation while allowing for tunable difficulty. Specifically, latent cells initially appeared as ambiguous question marks and collapsed into standard tiles upon measurement, introducing stochasticity and information costs.

Rule tiles triggered hidden transformations dependent on agent energy levels and adjacent hazards, demanding adaptive behaviour. SCAN actions temporarily expanded the observation radius from a default 5×5 window, enabling broader situational awareness at an energy cost, while movement actions occasionally failed, modelling actuation uncertainty. These mechanisms collectively created a realistic, shifting environment where robustness depended as much on adaptive strategy selection as on raw task-solving capability.

Language model agent robustness under realistic environmental uncertainty remains a significant challenge

Researchers evaluated the robustness of large language models when deployed as agents in complex, real-world scenarios. The investigation focused on how well these models perform when faced with incomplete information, unreliable data, changing conditions, and internal shifts in their own operational state, all within a long-horizon grid-based game.

Findings demonstrate a significant discrepancy between an agent’s ability to solve tasks under ideal conditions and its performance when subjected to these more realistic, challenging circumstances. Across five state-of-the-art language models, performance generally declined as the complexity of the grid and the length of the task increased, though model rankings proved inconsistent depending on the specific uncertainties present.

Notably, the agents exhibited an ability to implicitly balance competing objectives, such as task completion, efficiency, and avoiding penalties, even without explicit instructions to do so. Analyses of action profiles and sensitivities to different stressors revealed model-specific weaknesses and highlighted the importance of strategic adaptation.

The authors acknowledge limitations in the scope of the benchmark, focusing on a single game environment and a limited set of perturbations. Future research should concentrate on developing verification policies that intelligently probe for information when it is most beneficial, implementing online change detection with rapid replanning capabilities, and employing multi-objective training methods that explicitly balance completion, efficiency, and safety. These advancements will be crucial for building more reliable and adaptable agents capable of operating effectively in unpredictable, real-world settings.