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Position: Behavioral Systems Require Behavioral Tests

Manuel Cherep, Nikhil Singh, Pattie Maes

arXiv:2608.18081v1cs.AI

TL;DR

Performance-focused evaluation can miss the behavioral processes behind agents’ actions, even when different strategies produce the same outcome. This position paper proposes behavioral tests based on observing, perturbing, and interpreting agents in context to develop a science of AI behavior.

  • Problem

    Outcome-based evaluation can mask distinct behavioral pathways, limiting understanding of the processes that generate agents’ observable outputs.

  • Method

    The paper proposes behavioral tests that infer strategies from action sequences, isolate behavioral differences through controlled environments, and probe multi-agent dynamics.

  • Results

    The paper concludes that evaluating agents as agent-in-environment systems requires probing action, context, and strategy beyond performance outcomes alone.

  • Takeaways & Limitations

    Behavioral tests are presented as a necessary step toward understanding and governing systems that act.

  • Takeaways & Limitations

    Focusing only on inputs and outputs limits behavioral understanding.

Abstract

from arXiv · show

Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation methods largely focus on performance outcomes, not the underlying behavioral processes that produce them. This paper argues that AI agents must be evaluated like other behavioral systems: through systematic observation, perturbation, and interpretation of their actions. We draw on lessons from the behavioral sciences to motivate this position, and propose a research agenda focused on developing rigorous behavioral tests. These include methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi-agent systems. Taken together, these directions offer a roadmap for developing a science of AI behavior.

1. Introduction

AI agents should be evaluated as behavioral systems whose observable outputs must be interpreted through the processes generating them, not solely by whether they reach intended outcomes. This agenda requires new theories, methods, and engineering for studying agent–environment interactions, while complementing interpretability, verification, and benchmarking.

  • Motivation: Equal outcomes can conceal fundamentally different behavioral pathways, such as persuasion versus intimidation in negotiation.This illustrates equifinality: similar endpoints may mask how differently agents arrived there.
  • Contribution: AI agent systems should be treated like other behavioral systems by interpreting observable outputs through the processes that generate them.The paper frames this as a foundation for developing a science of AI agent behavior.
  • Conceptual framework: Behavior consists of action patterns arising from agent–environment interactions and conditioned by objectives, constraints, inputs, and internal mechanisms.This perspective supports asking what an agent did, when and why it did so, and how its behavior may change under different conditions.
  • Research agenda: A science of AI behavior demands new theory, methods, and engineering informed by behavioral sciences but adapted to artificial agents’ distinct design constraints and world interfaces.The paper highlights probing behavior across controlled environment variations and inferring strategies from observed actions.
  • Relation to existing evaluation: Behavioral science for AI agents is distinct from but complementary to interpretability, formal verification, and benchmarking because it centers system–environment relationships.It provides a basis for explaining unexpected behaviors and comparing qualitative behavioral differences.

2. Lessons from Studying Behavior … 2.3. Empiricists

The study of behavior shifted from supernatural and anthropocentric interpretations toward empirical inquiry, while rationalist and empiricist traditions offered contrasting explanations of how behavior arises. These historical lessons support resisting mystification, formulating hypotheses about emergent behavior, and recognizing that learned behavior inherits data biases.

  • 2. Lessons from Studying Behavior: Behavioral understanding evolved from supernatural interpretations toward rigorous empirical approaches, while animal studies challenged the assumption that humans alone possess meaningful cognition.The passage contrasts earlier anthropocentric views with later findings that animal behavior revealed surprising intelligence.
  • 2.1. The Dawn of Behavioral Study: Early explanations attributed thoughts, emotions, and dreams to direct intervention by spirits or deities rather than internal processes.Dreams were often treated as divine messages containing information about the past, present, and future.
  • 2.1. The Dawn of Behavioral Study: In the sixth century B.C., perspectives in India, China, and Greece proposed that thoughts arise from sensation and perception and that people can shape principles rather than be ruled by them.Ancient Greek philosophers also identified and hypothesized psychological problems that remain important.
  • 2.1. The Dawn of Behavioral Study: The historical shift toward behavioral inquiry implies that we should resist the urge to mystify behavior.This takeaway follows the movement from supernatural explanations toward explanatory inquiry.
  • 2.2. Rationalists: Rationalist inquiry reopened the mind-body debate and generated mechanistic hypotheses about behavior, including an early model of the reflex.Descartes’s account of animal spirits was incorrect but described a mechanism later termed the reflex.
  • 2.2. Rationalists: When observing emergent behavior, researchers should formulate hypotheses about why it arises.This methodological takeaway accompanies the rationalist discussion of explanatory models.
  • 2.3. Empiricists: Empiricists rejected innate ideas and argued that the mind develops through experience, with complex thoughts arising from simpler sensations.Hobbes described mental activity as motions of atoms in the nervous system, while Locke characterized the mind at birth as an empty slate.
  • 2.3. Empiricists: Empirically learned behavior inherits the biases of its data.This takeaway highlights a consequence of explaining behavior through experience and sense-derived learning.

2.4. Nativists · 2.5. Physicalists · 2.6. Modern Psychology

The paper traces behavioral inquiry from nativist accounts of innate mental organization, through physicalist explanations of mental functions via measurable neural processes, to modern psychology’s experimental and functional study of behavior. Together, these traditions motivate probing internal mechanisms and precisely characterizing behavioral differences across models.

  • 2.4. Nativists: German Nativism held that the mind contributes innate structures to experience rather than functioning as a tabula rasa.Kant argued that the mind actively organizes and transforms experience, while Leibniz introduced different levels of consciousness.
  • 2.4. Nativists: Leibniz’s different levels of consciousness were presented as a distant precursor to Freudian concepts.The passage attributes this idea to Leibniz in 1714.
  • 2.4. Nativists: Nativist theories illustrate how architectures encode inductive biases.This is the section’s stated takeaway.
  • 2.5. Physicalists: Eighteenth-century physiologists explained psychological processes through observable physical events and measurable nervous-system activity.Mesmer, Gall, Müller, and Weber connected physical processes to functions such as perception and reflexes.
  • 2.5. Physicalists: Von Helmholtz’s materialism of neural processes implied that mental functions could be examined through scientific experimentation.Fechner’s psychophysics additionally showed a non-linear relationship between sensation and stimulus intensity.
  • 2.5. Physicalists: The physicalist tradition motivates exploring behavior by probing internal mechanisms, including interpretability.This is the section’s stated methodological takeaway.
  • 2.6. Modern Psychology: Wundt established modern psychology as a distinct field through experimental methods, while James’s Functionalism linked higher mental processes to adaptive survival value.James also developed ideas concerning stream of consciousness, the self, the unconscious, and emotion.
  • 2.6. Modern Psychology: Modern psychology motivates precisely characterizing behavioral differences across models.This is the section’s stated takeaway and methodological implication.

2.7. Behaviorists · 2.8. Other Branches of Psychology

Behaviorism shifted psychology toward observable stimulus–response processes, learning laws, conditioning, and reinforcement while warning that inputs and outputs alone limit behavioral understanding. Other psychological branches emphasized holistic understanding and studying agents across individual, social, and developmental levels.

  • 2.7. Behaviorists: Behaviorism contrasted with the introspective methods of Wundt, James, and Freud by treating mental experiences as physiological events responding to stimuli.Behaviorists argued that the mind is an illusion.
  • 2.7. Behaviorists: Thorndike and Pavlov established groundwork for behaviorism by discovering laws of natural learning and classical conditioning.The passage dates these contributions to 1898 and 1927.
  • 2.7. Behaviorists: Skinner’s operant conditioning molded behavior through systematic reinforcement of incremental steps.The passage identifies this as Skinner’s 1935 work.
  • 2.7. Behaviorists: Focusing only on inputs and outputs limits behavioral understanding.This is the section’s stated behaviorist takeaway.
  • 2.8. Other Branches of Psychology: Gestalt psychologists challenged structuralism’s decomposition of psychological phenomena, arguing that “the whole is greater than the sum of its parts.”The passage presents Gestalt psychology as an early-20th-century movement associated with Köhler.
  • 2.8. Other Branches of Psychology: Developmental psychology emphasized that children are not just small adults.Piaget is identified as a towering figure in this branch’s emergence.
  • 2.8. Other Branches of Psychology: Agents should be studied at individual, social, and different developmental levels.This is the stated takeaway from the discussion of other psychological branches.

2.9. The Cognitivists · 2.10. Behavioral Economics · 2.11. Animal Behavior

The sections trace a shift from computational accounts of cognition toward bounded-rationality views of decision-making and direct behavioral study across organisms. Animal behavior research further emphasizes evolutionary explanations, systematic analysis, and caution when interpreting absent evidence.

  • 2.9. The Cognitivists: The cognitive revolution rejected behaviorism’s dismissal of the mind and reframed cognition through computational metaphors of programs, inputs, storage, and computation.This shift changed psychology’s focus and methods, while computational abstractions were proposed as tools for understanding mental processes.
  • 2.9. The Cognitivists: Cognitive science used computational abstractions to investigate the processes driving the mind.
  • 2.10. Behavioral Economics: Expected utility theory modeled rational agents as maximizing utility under uncertainty, shaping economics’ account of decision-making.Bernoulli formulated the principle, which von Neumann and Morgenstern later axiomatized.
  • 2.10. Behavioral Economics: Critics argued that Homo economicus assumed unrealistic macroeconomic understanding and forecasting, while Keynes emphasized spontaneous optimism and Simon introduced bounded rationality.Bounded rationality stresses cognitive limitations and uncertainty in decision-making.
  • 2.10. Behavioral Economics: Behavioral systems should be studied from a bounded-rationality perspective.
  • 2.11. Animal Behavior: Understanding complex behavioral systems requires studying behavior directly, with evolutionary pressures shaping emotions and actions toward useful, survival-enhancing outcomes.Romanes used introspection by analogy to reason about animal psychology.
  • 2.11. Animal Behavior: Behavioral analysis developed from competing explanations of animal action to ethology’s systematic questions about what organisms do, why, how, when, and how behavior evolved.Morgan rejected higher-level explanations when lower faculties sufficed, while Loeb characterized animals as stimulus-driven automatons.
  • 2.11. Animal Behavior: Failure to detect a capacity does not establish its definitive absence, especially when flawed methods or anthropocentric assumptions cloud behavioral interpretation.

2.12. Precedents in Behavioral Testing for AI

Prior work across AI shows that scalar success metrics can hide meaningful behavioral differences, motivating direct behavioral evaluation. The paper concludes that such tests should be systematic, rigorous, precise, and sensitive to behavioral complexity.

  • Motivation: Scalar success metrics can treat behaviorally different systems as equivalent, so evaluation must examine behavior directly.This principle appears across AI subfields when outcome measures fail to distinguish systems.
  • NLP precedents: Structured test suites can reveal systematic capability failures that aggregate accuracy masks, even for static input-output models.Ribeiro et al. (2020) motivate case-based testing because scalar accuracy is an unreliable behavioral summary.
  • Continuous-control precedents: A deliberately simple full-on-or-off controller can match conventional controllers’ task return while behaving substantially differently.Seyde et al. (2021) demonstrate that outcome parity does not imply behavioral equivalence in continuous control.
  • Environment precedents: Two ostensibly equivalent task variants can change absolute performance, algorithm rankings, and qualitative behavior in ways scalar return does not reveal.Voelcker et al. (2024) show that environment design itself can expose behavioral differences.
  • Motivation: Behavioral testing is an established response to metric failures, not a speculative method specific to language-model agents.Prior work uses action inspection and environmental perturbation to distinguish systems that scalar metrics certify as roughly equivalent.
  • Summary: Useful behavioral tests must be systematic, rigorous, precise, and attentive to the complexity of behavioral systems.The paper presents these requirements as the central lesson from behavioral science and the basis for constructing future tests.

3. Behavioral Tests for Artificial Behavioral Systems

Behavioral tests are needed because agents with identical task outcomes can differ in robustness, value alignment, and decision-making strategy. The paper proposes systematic evaluation through policy inference, controlled environmental manipulation, and analysis of multi-agent dynamics.

  • Motivation: Agents can achieve identical evaluation scores through behaviorally distinct policies, so outcome metrics alone may fail to distinguish robustness, alignment, or decision-making strategy.Formally, a metric M does not identify a behavioral property ϕ when M(πA) = M(πB) but ϕ(πA) ≠ ϕ(πB).
  • Motivation: Posttraining trajectory inspection and retrospective reward-related analysis are insufficient because undesirable behavioral differences may remain hidden despite satisfactory aggregate performance.The paper argues that small-sample inspection is good practice but cannot provide systematic behavioral evaluation.
  • Policy inference: Policy inference uses action sequences as process traces to recover the implicit objectives, constraints, or heuristics driving agent behavior.Human trajectory interpretation can produce qualitative descriptions, but it is slow, subjective, and limited in scalability.
  • Environment design: Behavioral tests require environments that support systematic manipulations capable of eliciting, isolating, and testing specific behaviors.One proposed direction is to instrument realistic environments with controlled interventions, balancing interpretability against realism.
  • Multi-agent dynamics: Multi-agent behavioral research must characterize how individual tendencies change in social contexts while modeling each agent conditional on the others’ behavior.Competition can transform cautious agents into risk-seeking ones, and combinatorial interaction complexity creates additional methodological challenges.

4. Emerging Examples and Limitations

Research on AI behavior reveals systematic reasoning, social, emotional, and adversarial limitations, while generative-agent simulations require behavioral tests to establish when they are human-like. The paper highlights three evaluation priorities: recovering strategies from action sequences, causally testing environmental influences, and analyzing emergent multi-agent behavior.

  • Emerging Examples and Limitations: LLMs exhibit probabilistic reasoning in deterministic settings, declining logical accuracy with complexity, distorted trade-offs, over-assumed rationality, framing sensitivity, social-dilemma differences, affect representations, stereotypes, randomness biases, and adversarial vulnerability.These findings illustrate diverse behavioral patterns and limitations across reasoning, preferences, social behavior, emotion, interpretation, and robustness.
  • Emerging Examples and Limitations: Generative agents offer promising tools for simulating human behavior and accelerating social science, but studies identify limitations requiring tests of when their behavior is human-like.Systematic behavioral tests are also needed because people often overestimate what these systems can do, supporting safer deployment.
  • Behavioral Evaluation Priorities: The proposed priorities are recovering decision-making strategies from action sequences, using systematic environment variants to test causal influences, and analyzing emergent behavior in adaptive multi-agent systems.These categories organize the paper’s case studies of chains of actions, systematic environments, and multi-agent systems.
  • Case Studies: Trajectory analysis identifies maximizing and satisficing strategies, counterfactual web environments attribute decisions to manipulated factors, and multi-agent setups combine LLMs with associative memory to generalize toward human behavior.Trajectory strategies can depend on brittle heuristics hidden by payoff outcomes; shopping agents were more sensitive than humans to price, rating, nudges, and user profiles.

5. Alternative Views

The paper treats behavioral tests as complementary to competitive performance evaluation and mechanistic interpretability, while distinguishing behavioral evaluation from reward specification and multiobjective scoring. It argues that process-level analysis is needed to distinguish strategies, assess generalization, and understand outcomes under changing conditions.

  • Competitive evaluation: Competitive performance-based evaluation remains effective when its metric captures the properties of interest, but that condition does not always hold.The paper does not argue that competitive testing should be replaced.
  • Mechanistic interpretability: Mechanistic interpretability can support causal claims about internal computation, but its findings are difficult to translate into high-level behavioral descriptions that generalize across environments or tasks.
  • Reward specification: Reward signals may induce desirable behavior during training, but they do not identify how behavior is produced or how it generalizes under environmental variation and strategic interaction.Evaluation should distinguish policies that achieve similar rewards while differing in strategy.
  • Multiobjective evaluation: Multiobjective evaluation does not resolve the identification problem because policies can attain the same reward vector while differing in strategy or robustness.Encoding a property as an objective also presumes that the property has already been identified.
  • Epistemic perspective: For agentic systems in complex environments, behavioral processes can provide reliable information about outcomes under distribution shift, interaction, or scale, independently of whether ends justify means.The paper separates this epistemic claim from the moral question of whether outcomes or rewards justify strategies or behavior.

6. Call to Action

The paper calls for behavioral testing to complement performance metrics by revealing how AI agents achieve outcomes and whether their behavior is robust, aligned, and safe. It proposes integrating these tests into research, deployment, and user trust decisions through controlled evaluation and iterative policy improvement.

  • Performance metrics alone are insufficient because they mask behavioral processes that determine whether agents are robust, aligned, or safe.
  • Researchers should build scalable tools to infer decision strategies from action chains and design benchmarks enabling controlled interventions and counterfactual testing.
  • Industry practitioners should integrate behavioral testing into deployment pipelines, auditing how agents succeed before releasing systems that interact with users or critical infrastructure.
  • Users should consider behavioral benchmarks when selecting agents for consequential tasks.
  • Behavioral tests can identify policy attributes missed by task rewards, convert them into training signals or constraints, and support behavioral retesting after policy updates.

7. Conclusion

For agentic systems, the relevant unit of analysis is the agent-in-environment rather than the model alone. Behavioral tests probe action, context, and strategy beyond performance outcomes to help understand and govern systems that act.

  • Conclusion: The agent-in-environment, not the model alone, is the relevant unit for evaluating agents.Traditional AI evaluation treats the model as the unit of analysis, but the paper argues this is no longer sufficient for agents.
  • Conclusion: Behavioral tests evaluate agents by probing action, context, and strategy beyond performance outcomes alone.The paper presents behavioral tests as a suite of methods for evaluating the agent-in-environment system.
  • Conclusion: Developing these methods is necessary to understand and govern systems that act.The conclusion frames behavioral testing as a necessary step for understanding and governing agentic systems.
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