cs.AIOct 12, 2026

Agency as a New Organizing Abstraction for Computer Science

Authors: Ian Foster

Organizations: Argonne National Laboratory and The University of Chicago

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Abstract

Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate actions, and interact with open environments. These systems are often framed as applications of foundation models. We argue that this framing is incomplete: agency is also a candidate new organizing abstraction for computer science. What is new is not the concept of an agent, nor runtime policy generation per se, but its general-purpose, open-ended form, in which systems can synthesize courses of action from goals, observations, and memory, over tools and environments for which no complete task-specific model was specified in advance. Thus agency becomes a systems-level abstraction as well as an AI modeling concept. Where the process abstraction organizes computation around executing programs, agency organizes it around pursuing goals under bounded resources: a process executes a program; an agent pursues a goal. From this perspective, the recurring failures of deployed agentic systems (runaway resource consumption, behavior redirected by adversarial content, opaque continuous operation) are symptoms of structural abstraction gaps, not isolated engineering defects. We examine these gaps across six dimensions (execution, interfaces, correctness, oversight, coordination, and persistence) and argue that closing them requires coordinated advances in systems,

Figures & tables

Figure 1: From models to agents. A foundation model is a stateless inference engine that maps prompts to responses; an agent embeds such a model in a persistent perceive–reason–act loop, accumulating state and interacting continuously with an open environment.
DimensionConventional organizing focusAgency makes first-class
ExecutionResource limitsResource budgets
InterfacesProtocolsContracts
CorrectnessFunctional correctnessBehavioral correctness
OversightCommandsAttention allocation
CoordinationSchedulingNegotiation
PersistenceStateMemory
Table 1: Six dimensions along which agency extends existing abstractions. In each, prior work addresses the right-hand concern in specialized settings; agency makes it a general and intrinsic requirement (Section 5 ).
Figure 2: Organizing abstractions address distinct concerns and are complements rather than successors. We ask whether autonomous goal pursuit is a concern of comparable generality, and the agent its organizing abstraction.
Figure 3: Four properties characterize agentic operation. Each has precedent in earlier systems; what is new is their combination in a single general-purpose system, enabled by foundation models. The deeper distinction lies in how behavior is produced (Section 3.2 ).
Figure 4: Architecture of an agentic system. The agent comprises ephemeral working state, goals ( GG ), persistent memory ( MM ), and a policy generator ( Π\Pi ). A foundation model provides general-purpose reasoning for policy generation. The agent interacts with an open environment ( EE ) through actions and observations, running a persistent control loop under human oversight.
Figure 5: A runaway-agent scenario. An agent monitoring a cloud deployment detects an anomaly and responds autonomously; the cost of its actions becomes visible only after budgets have been exceeded. Reactive resource control arrives too late; agentic systems require proactive constraint enforcement.
Figure 6: Lifecycle mismatch. Batch jobs run toward termination; services are organized around periods of request handling; agents blur these boundaries: intermittently active, spawning children that outlive their parent, and accumulating context that makes termination costly.
Figure 7: Failure attribution. Traditional debugging seeks a single faulty frame in a linear execution trace (left); agentic failures emerge from partial orders of locally reasonable decisions that compound into a globally undesirable outcome (right).
Observed Failure ModeAbstraction GapResulting Research Challenges
Autonomous resource consumption and unbounded action initiationExecution models assume externally invoked computation with reactive resource controlProactive resource budgeting, anticipatory constraint enforcement, isolation for self-directed processes
Ambiguous agent lifecycles and costly terminationProcess and service abstractions assume clear start, run, and termination phasesLifecycle models for persistent agents, state preservation and transfer, principled shutdown semantics
Distributed failure attribution across long decision sequencesDebugging and verification assume localized faults and linear executionCausal tracing across reasoning-action loops, partial-order explanations, long-horizon accountability
Misplaced responsibility at system boundariesControl and correctness are assumed to reside outside the computational componentNew interface contracts, responsibility assignment, end-to-end reasoning for autonomous initiators
Behavior redirected by adversarial contentAbstractions assume a separation between trusted code and untrusted dataTrust-boundary tracking, provenance-aware interpretation, limits on untrusted influence over actions
Opaque behavior under continuous operationInteraction models assume explicit user commands and episodic executionOversight interfaces, escalation policies, scalable human supervision mechanisms
Table 2: Characteristic failure modes of agentic systems reflect mismatches between persistent, autonomous behavior and existing computational abstractions.
Figure 8: Oversight at scale. A single supervisor cannot review every decision of a fleet of continuously operating agents; oversight becomes a problem of allocating scarce human attention, with escalation as a first-class mechanism.
Figure 9: The agent abstraction in synthesis. The agent of Figure 4 , now exercising authority delegated by its principal, embedded in a governing substrate whose mechanisms (budgets, contracts, provenance, runtime monitors, oversight) make generated behavior governable.
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