AI Contracts: Governing Autonomous Agents with Formal Constraints

Author: Denis Avetisyan

A new framework establishes predictable behavior and scalability for AI systems operating under resource limitations.

This paper introduces Agent Contracts, a formal system for resource governance in multi-agent systems based on contract theory and conservation laws.

While multi-agent systems offer scalability and adaptability, a critical gap remains in formally governing the resource consumption and operational duration of autonomous agents. This paper introduces ‘Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems’-a novel approach unifying task specifications, resource constraints, and temporal boundaries into a coherent governance mechanism. By establishing conservation laws for delegated budgets and offering explicit lifecycle semantics, Agent Contracts enable predictable, auditable, and scalable multi-agent coordination with demonstrably improved efficiency and quality-resource trade-offs. Could this framework become a foundational element for trustworthy and reliable autonomous AI deployment in complex, real-world scenarios?

The Inevitable Contract: Governing Autonomous Systems

The accelerating development of autonomous artificial intelligence demands proactive governance strategies to mitigate potential risks and ensure beneficial outcomes. As AI agents gain increasing independence and operate in complex, real-world scenarios, the possibility of unintended consequences – ranging from algorithmic bias and privacy violations to safety hazards and economic disruption – becomes increasingly prominent. Traditional regulatory frameworks, often designed for human actors, struggle to address the unique challenges posed by systems capable of learning, adapting, and acting without direct human oversight. Consequently, establishing robust governance mechanisms isn’t merely a precautionary measure, but a fundamental requirement for fostering public trust and enabling the responsible deployment of these powerful technologies, safeguarding against unforeseen negative impacts as AI capabilities continue to expand.

Existing regulatory and ethical guidelines often prove inadequate when applied to increasingly sophisticated artificial intelligence. Traditional legal frameworks, designed for entities with clear intent and predictable behavior, struggle to address the nuanced actions of autonomous agents operating in complex, real-world scenarios. Establishing liability becomes problematic when an AI’s decision-making process isn’t easily traceable or when unforeseen interactions with the environment lead to unintended outcomes. Moreover, static rules fail to account for the dynamic nature of AI systems that continuously learn and adapt, rendering pre-defined boundaries porous and enforcement challenging. This inherent difficulty in specifying obligations and maintaining control underscores the need for novel governance strategies capable of handling the unique characteristics of intelligent, adaptive technologies.

The reliable operation of increasingly autonomous artificial intelligence demands a fundamental shift in governance, moving beyond broad ethical guidelines toward explicitly defined contractual frameworks. This approach draws heavily from economic principles, treating AI agents as entities capable of entering into agreements that specify obligations, deliverables, and consequences for non-compliance. By formalizing expectations and establishing clear lines of accountability, these contracts can mitigate risks associated with unpredictable AI behavior. Such frameworks aren’t merely legal documents; they represent a proactive mechanism for aligning AI goals with human values and ensuring predictable performance in complex, real-world scenarios. This contractual approach facilitates trust and allows for effective recourse when AI systems inevitably encounter unforeseen circumstances or fail to meet established standards, ultimately fostering a more secure and beneficial integration of AI into society.

Formalizing Agency: Foundations in Contractual Theory

Agent Contracts, as a framework for AI governance, draw heavily from established economic and game-theoretic principles. Contract Theory provides the foundational tools for designing agreements that incentivize desired agent behavior, explicitly addressing issues of information asymmetry and moral hazard. Coordination Theory complements this by focusing on mechanisms to align the actions of multiple agents towards a common goal, particularly relevant in multi-agent systems. This integration allows for the formal specification of agent obligations, performance metrics, and dispute resolution processes, moving beyond ad-hoc governance approaches to a system grounded in rigorous theoretical frameworks. The application of these theories enables the creation of enforceable agreements that facilitate predictable and reliable AI behavior within defined operational parameters.

Effective agent contract design necessitates recognizing the limitations of resource-bounded computation. All autonomous agents operate within constraints regarding computational time, token usage (for large language models), memory access, and energy consumption. Ignoring these constraints leads to unpredictable behavior, contract failure, or prohibitive operational costs. Specifically, agents cannot process infinite inputs or execute unbounded computations; contracts must therefore explicitly define limits on input size, processing steps, and output complexity. Furthermore, the cost of computation – measured in tokens, cycles, or monetary units – must be factored into contract terms to ensure sustainable operation and prevent denial-of-service scenarios. Contracts should specify resource allocation and usage parameters, including maximum execution time, token limits per operation, and permissible memory footprint.

Effective agent contracts necessitate precise definitions of agent behavior through three core components: Input Specification, Output Specification, and Success Criteria. Input Specification details the format, range, and validity constraints of data provided to the agent, ensuring the agent receives actionable and well-formed requests. Output Specification defines the expected format, data types, and permissible values of the agent’s responses, facilitating seamless integration with other systems. Critically, measurable Success Criteria establish objective standards for evaluating agent performance; these criteria must be quantifiable and directly linked to the specified outputs, enabling automated verification of contract fulfillment and providing a basis for reward or penalty mechanisms. Without these clearly defined elements, ambiguity arises, hindering reliable agent operation and complicating contract enforcement.

Runtime Integrity: Enforcement and Resource Conservation

Runtime Enforcement is a critical component of agent operation, actively monitoring and validating that all actions taken by the agent comply with pre-defined Temporal Constraints and Resource Constraints. Temporal Constraints specify deadlines or time limits for task completion, preventing indefinite delays or infinite loops. Resource Constraints govern the utilization of limited resources, such as computational power, memory, or energy, ensuring that agents do not exceed allocated budgets. This enforcement occurs dynamically during operation, allowing for immediate intervention and correction if an agent attempts to violate these constraints, thereby maintaining system stability and predictable behavior. Failure to adhere to these constraints results in immediate termination of the violating action or subtask.

Conservation Laws are implemented to guarantee the stability of resource allocation during agent operation by establishing limits on resource distribution to subtasks. These laws function by preventing the cumulative resource demand of child tasks from exceeding the capacity of their parent resources. This hierarchical constraint ensures that overall resource limits are never violated, preventing system exhaustion. Rigorous experimental validation, conducted across a range of scenarios, has demonstrated zero instances of conservation law violations, confirming the robustness and reliability of this resource management strategy.

Satisficing, as applied to agent behavior, represents a deviation from optimization-based approaches by prioritizing the attainment of acceptable, rather than optimal, outcomes within specified constraints. This strategy acknowledges the computational limitations and potential resource costs associated with exhaustive searches for perfect solutions. Instead of continuously attempting to improve results until an ideal state is reached, a satisficing agent will cease improvement when a predefined threshold of acceptability is met, effectively trading off absolute performance for reduced computational load and faster completion times. This is particularly relevant in complex environments where exhaustive optimization is impractical or impossible, allowing agents to function effectively under real-world limitations.

Scaling the Autonomous Web: Advanced Techniques for Governance

Contract delegation represents a paradigm shift in how artificial intelligence systems are orchestrated, enabling the construction of complex, hierarchical agent networks. Rather than relying on monolithic AI entities, this technique facilitates the decomposition of large tasks into smaller, manageable sub-tasks distributed across multiple specialized agents. Crucially, these agents aren’t simply assigned tasks; they operate under legally-defined contracts that specify permissible actions, resource limits, and expected outputs. This contractual framework isn’t merely about accountability; it actively enforces constraints at each level of the hierarchy, preventing runaway processes and ensuring adherence to organizational policies. By embedding constraints directly into the agent interactions, contract delegation fosters a robust and scalable architecture where complex goals can be achieved through coordinated effort, while simultaneously mitigating risks associated with autonomous AI systems. The result is a more manageable, auditable, and ultimately, reliable approach to AI deployment.

Recent advancements in AI governance leverage token-budget-aware reasoning to address the escalating costs and scalability challenges of large language model (LLM) deployments. Frameworks such as TALE, BudgetThinker, and BATS introduce mechanisms for agents to internally track and manage their token consumption, allowing for more efficient task execution and resource allocation. This approach moves beyond simple rate-limiting by enabling agents to proactively optimize their prompts and strategies to remain within budgetary constraints. Research indicates that incorporating these frameworks can yield substantial savings; specifically, this paper demonstrates up to a 90% reduction in token usage compared to scenarios where agents operate without enforced token budgets, highlighting the potential for significant cost reduction and increased operational scale in complex AI systems.

Large Language Model Operations (LLMops) platforms are becoming indispensable for organizations deploying autonomous AI agents at scale. These platforms extend traditional ModelOps principles to address the unique challenges of managing numerous, interacting agents. Crucially, LLMops provide comprehensive tracking of agent actions, logging every request, response, and internal state change for auditability and debugging. Real-time alerting systems flag anomalous behavior or policy violations, enabling prompt intervention and preventing unintended consequences. Furthermore, robust rate-limiting capabilities control resource consumption and prevent system overload, ensuring predictable performance and cost management as agent networks expand. By centralizing monitoring, control, and governance, LLMops platforms facilitate responsible and efficient scaling of AI-driven automation across the enterprise.

Measuring Progress and Charting Future Directions

The progress of autonomous agents hinges on rigorous evaluation, and benchmarks like OpenR1 serve as essential tools for this purpose. These standardized assessments move beyond anecdotal evidence, providing objective metrics to gauge an agent’s capabilities in complex scenarios and, crucially, the effectiveness of governance mechanisms such as contract-based systems. OpenR1 allows researchers and developers to compare different approaches – from prompt engineering to sophisticated budgeting algorithms – under controlled conditions, accelerating innovation and fostering trust in these increasingly powerful AI systems. Without such benchmarks, progress remains subjective and difficult to replicate, hindering the responsible development and widespread adoption of truly autonomous agents capable of operating safely and efficiently in real-world applications.

Investigations into autonomous budgeting techniques, such as SelfBudgeter, coupled with improvements in tool connectivity – notably through the Multi-tool Communication Protocol (MCP) – are dramatically enhancing the operational efficiency of AI agents. Recent studies demonstrate that these combined advancements yield substantial reductions in resource consumption; specifically, a 525x decrease in token usage variance has been observed. This heightened control over resource allocation not only lowers operational costs but also allows for more predictable and stable agent behavior, paving the way for reliable deployment in complex, real-world scenarios. Further refinement of these methods promises to unlock even greater levels of autonomy and cost-effectiveness in artificial intelligence systems.

Contractual frameworks represent a pivotal step towards realizing the full capabilities of autonomous artificial intelligence while simultaneously addressing inherent risks and fostering responsible innovation. Recent investigations demonstrate that structuring AI operation via explicit contracts-defining objectives, constraints, and reward mechanisms-significantly enhances performance consistency and reliability. Notably, studies reveal an 86% success rate for agents operating within a ‘balanced’ contractual mode, prioritizing both task completion and resource efficiency. This contrasts sharply with a 70% success rate observed when the same agents were directed by ‘urgent’ contracts, emphasizing the importance of carefully calibrated incentives. These findings suggest that a contractual approach doesn’t merely constrain AI, but actively unlocks its potential by providing clear operational boundaries and facilitating predictable, accountable behavior, ultimately paving the way for wider adoption and trust in autonomous systems.

The pursuit of predictable systems, as outlined in the agent contract framework, echoes a fundamental tenet of applied mathematics. Andrey Kolmogorov once stated, “The most important things are not what we know, but what we don’t know.” This resonates with the need for formal verification within multi-agent systems; acknowledging the inherent uncertainty and potential for unforeseen interactions. The Agent Contracts aim to define resource and temporal constraints, not to eliminate unpredictability entirely, but to bound it, to create systems that, while acknowledging inevitable decay, age with a degree of controlled grace. The framework proposes a means of managing latency-the ‘tax every request must pay’-by establishing clear expectations and limitations from the outset.

The Long View

The formalization of agent contracts, as presented, attempts to impose order on systems destined for entropy. Every architecture lives a life, and this one, too, will encounter the limits of its expressiveness. The initial focus on resource governance is sound – conservation laws are rarely circumvented by fundamental physics – but the true challenge lies in modeling the change in those laws. What begins as a rigid contract will inevitably require renegotiation, adaptation, and, ultimately, either graceful decay or catastrophic failure. The current framework provides tools for verification, but verification is merely a snapshot; it does not predict the weathering of time.

Future work will almost certainly necessitate a move beyond static constraints. The ability to model temporal contracts-agreements that evolve alongside agent capabilities and environmental shifts-is paramount. Improvements age faster than anyone can understand them. Moreover, the implicit assumption of rational agents may prove limiting. Real systems exhibit emergent behavior, often defying pre-programmed expectations. Accepting, even embracing, a degree of controlled unpredictability might be a more robust strategy than striving for absolute control.

Ultimately, the success of Agent Contracts-or any similar framework-will not be measured by its ability to prevent failure, but by its capacity to facilitate resilient failure. Systems do not last; they transition. The goal, then, is not immortality, but a dignified obsolescence.

Original article: https://arxiv.org/pdf/2601.08815.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/