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[2602.22302] Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents
Summary
The paper presents Agent Behavioral Contracts (ABC), a framework for specifying and enforcing the behavior of autonomous AI agents, addressing issues of drift and governance failures in AI deployments.
Why It Matters
As AI agents increasingly operate autonomously, ensuring their reliability and compliance with expected behaviors is crucial. The introduction of ABC provides a structured approach to mitigate risks associated with AI drift and governance, enhancing trust in AI systems.
Key Takeaways
- Agent Behavioral Contracts (ABC) formalize AI behavior specifications to prevent drift.
- The framework includes components for preconditions, invariants, governance, and recovery.
- Probabilistic compliance measures help manage the non-determinism of AI agents.
- Implementation of ABC showed significant improvements in detecting violations and ensuring compliance.
- The framework is essential for safe multi-agent systems and enhances governance in AI applications.
Computer Science > Artificial Intelligence arXiv:2602.22302 (cs) [Submitted on 25 Feb 2026] Title:Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents Authors:Varun Pratap Bhardwaj View a PDF of the paper titled Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents, by Varun Pratap Bhardwaj View PDF HTML (experimental) 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 ...