Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents

• URL: http://arxiv.org/abs/2602.22302v1
• Date: Wed, 25 Feb 2026 18:42:56 GMT
• Title: Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents
• Abstract summary: Agent Behavioral Contracts (ABC) is a formal framework that brings Design-by-Contract principles to autonomous AI agents.<n>ABC specifies Preconditions, Invariants, Governance policies, and Recovery mechanisms as runtime-enforceable components.<n>We implement ABC in AgentAssert, a runtime enforcement library, and evaluate on AgentContract-Bench, a benchmark of 200 scenarios across 7 models from 6 vendors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traditional software relies on contracts -- APIs, type systems, assertions -- to specify and enforce correct behavior. AI agents, by contrast, operate on prompts and natural language instructions with no formal behavioral specification. This gap is the root cause of drift, governance failures, and frequent project failures in agentic AI deployments. We introduce Agent Behavioral Contracts (ABC), a formal framework that brings Design-by-Contract principles to autonomous AI agents. An ABC contract C = (P, I, G, R) specifies Preconditions, Invariants, Governance policies, and Recovery mechanisms as first-class, runtime-enforceable components. We define (p, delta, k)-satisfaction -- a probabilistic notion of contract compliance that accounts for LLM non-determinism and recovery -- and prove a Drift Bounds Theorem showing that contracts with recovery rate gamma > alpha (the natural drift rate) bound behavioral drift to D* = alpha/gamma in expectation, with Gaussian concentration in the stochastic setting. We establish sufficient conditions for safe contract composition in multi-agent chains and derive probabilistic degradation bounds. We implement ABC in AgentAssert, a runtime enforcement library, and evaluate on AgentContract-Bench, a benchmark of 200 scenarios across 7 models from 6 vendors. Results across 1,980 sessions show that contracted agents detect 5.2-6.8 soft violations per session that uncontracted baselines miss entirely (p < 0.0001, Cohen's d = 6.7-33.8), achieve 88-100% hard constraint compliance, and bound behavioral drift to D* < 0.27 across extended sessions, with 100% recovery for frontier models and 17-100% across all models, at overhead < 10 ms per action.

Related papers

• AgentAudit: An Open, Extensible Framework for Full-Lifecycle Trust Evaluation of AI Agents [2.8000808592516293]
  AgentAudit evaluates the entire execution trace across ten capability, grounding, security and behavioural dimensions.<n>It reads only the recorded execution trace and does not interfere with how the agent runs.<n>We evaluate five language models (OpenAI GPT-5, Claude Sonnet 5, Sarvam 105B, Llama 3.3 70B and Gemini 2.5 Flash) across nine capability and adversarial tasks.
  arXiv  Detail & Related papers  (2026-09-09T08:29:16Z)
• EvoSafeHarness: Evolving Model- and Domain-Specific Harnesses for Securing Agents [74.54929751174289]
  EvoSafeHarness is a safety-specific optimization framework that synthesizes a deployable harness for a frozen model in a target domain.<n>It reduces average attack success rate from 45.6% to 10.0% at a 3.3-point utility cost.<n>It also achieves the best score on Agent-SafetyBench for every victim.
  arXiv  Detail & Related papers  (2026-09-05T05:56:41Z)
• VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents [57.240036084348354]
  We introduce VASO, a framework for verification-guided self-evolution of robot skill contracts.<n>VASO verifies logically inconsistent skill contracts, then verifies plans induced by the skill against global and local temporal specifications.<n>On Clearpath Jackal and PX4 quadcopter tasks, VASO reaches 97.2% formal-specification compliance using fewer than 100 optimization samples.
  arXiv  Detail & Related papers  (2026-06-03T20:02:35Z)
• Security Is Relative: Training-Free Vulnerability Detection via Multi-Agent Behavioral Contract Synthesis [14.657771106188115]
  We propose Phoenix, a training-free multi-agent framework for vulnerability detection.<n>Phoenix decomposes detection into three stages: a Semantic Slicer, a Requirement Reverse Engineer, and a Contract Judge.<n>On PrimeVul Paired, Phoenix achieves F1 = 0.825 and Pair-Correct = 64.4%, surpassing RASM-Vul (F1 = 0.668) and VulTrial (F1 = 0.563)
  arXiv  Detail & Related papers  (2026-04-21T03:02:34Z)
• AITH: A Post-Quantum Continuous Delegation Protocol for Human-AI Trust Establishment [9.198471344145092]
  We present AITH (AI Trust Handshake), a post-quantum continuous delegation protocol.<n>AITH introduces: (1) a Continuous Delegation Certificate signed once with ML-DSA-87 (FIPS 204, NIST Level 5), replacing per-operation signing with sub-microsecond boundary checks at 4.7M ops/sec; (2) a six-check Boundary Engine enforcing hard constraints, rate limits, and escalation triggers with zero cryptographic overhead on the critical path; and (3) a push-based Revocation Protocol propagating invalidation within one second.
  arXiv  Detail & Related papers  (2026-04-09T01:30:28Z)
• Agent Control Protocol: Admission Control for Agent Actions [0.4929694290403903]
  Agent Control Protocol (ACP) is a formal specification for admission control governance of autonomous agents in B2B institutional environments.<n>ACP defines cryptographic identity, capability-based authorization, deterministic risk evaluation, chained delegation, and cryptographically-chained auditing.<n>ACP operates on top of RBAC and Zero Trust, addressing what neither model solves.
  arXiv  Detail & Related papers  (2026-03-19T12:28:28Z)
• Governing Dynamic Capabilities: Cryptographic Binding and Reproducibility Verification for AI Agent Tool Use [0.0]
  No existing security layer verifies what an AI agent can do, whether it executed what it claims, or what happened in a multi-agent interaction.<n>Existing frameworks conflate the two, enabling silent capability escalation and leaving interactions without verifiable provenance.<n>We derive three Agent Governance Requirements: capability integrity (G1), behavioral verifiability (G2), and interaction auditability (G3)<n>We validate with two crypto-agnostic instantiations -- basic (Ed25519, SHA-256; 97 us verify) and enhanced (BBS+ selective disclosure, Groth16 DV-SNARK; 13.8 ms)
  arXiv  Detail & Related papers  (2026-03-15T11:46:57Z)
• AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows [0.0]
  AgentAssay is the first token-efficient framework for regression testing non-deterministic AI agents.<n>It achieves 78-100% cost reduction while maintaining rigorous statistical guarantees.
  arXiv  Detail & Related papers  (2026-03-03T04:59:25Z)
• Sleeper Cell: Injecting Latent Malice Temporal Backdoors into Tool-Using LLMs [0.0]
  Openweight Large Language Models (LLMs) have democratized agentic AI, yet finetuned weights are frequently shared and adopted with limited scrutiny beyond leaderboard performance.<n>This creates a risk where third-party models are incorporated without strong behavioral guarantees.<n>We show that poisoned models maintain state-of-the-art performance on benign tasks, incentivizing their adoption.
  arXiv  Detail & Related papers  (2026-03-02T22:01:08Z)
• Early-Stage Prediction of Review Effort in AI-Generated Pull Requests [0.0]
  We analyze 33,707 agent-authored PRs from the AIDev dataset across 2,807 repositories.<n>We propose a Circuit Breaker triage model that predicts high-review-effort PRs at creation time.
  arXiv  Detail & Related papers  (2026-01-02T17:18:01Z)
• Enforcing Temporal Constraints for LLM Agents [10.694240979134326]
  Existing guardrails rely on imprecise natural language instructions or post-hoc monitoring.<n>We present Agent-C, a novel framework that provides run-time guarantees ensuring LLM agents adhere to formal temporal safety properties.<n>We evaluate Agent-C across two real-world applications: retail customer service and airline ticket reservation system.
  arXiv  Detail & Related papers  (2025-12-25T06:12:13Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
  Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T05:01:57Z)
• Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents [58.00130492861884]
  TraitBasis is a lightweight, model-agnostic method for systematically stress testing AI agents.<n>TraitBasis learns directions in activation space corresponding to steerable user traits.<n>We observe on average a 2%-30% performance degradation on $tau$-Trait across frontier models.
  arXiv  Detail & Related papers  (2025-10-06T05:03:57Z)
• Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization [18.013438474903314]
  We propose SmartCoder-R1, a framework for secure and explainable smart contract generation.<n>We train the model to emulate human security analysis.<n>SmartCoder-R1 establishes a new state of the art, achieving top performance across five key metrics.
  arXiv  Detail & Related papers  (2025-09-12T03:14:50Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents [8.290987399121343]
  We propose AgentSpec, a lightweight language for specifying and enforcing runtime constraints on LLM agents.<n>With AgentSpec, users define structured rules that incorporate triggers, predicates, and enforcement mechanisms.<n>We implement AgentSpec across multiple domains, including code execution, embodied agents, and autonomous driving.
  arXiv  Detail & Related papers  (2025-03-24T13:31:48Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.