Coordination Without Contracts: Toward Formally Grounded Agentic AI Systems

Abstract

Autonomous AI agents systems that plan, invoke external tools, spawn sub-agents, and iterate toward long-horizon goals are rapidly moving from research prototypes to production deployments. Yet the theoretical scaffolding needed to reason about agent behavior remains conspicuously thin. Unlike single-turn language models, which inherit decades of statistical learning theory and empirical benchmarking infrastructure, multi-agent LLM pipelines operate without formal contracts between participants, without verified memory semantics, and without evaluation protocols that reflect the temporal depth of real tasks. This paper argues that the central bottleneck in agentic AI research is not the capability of current frontier models, which are already impressive planners in isolation, but rather the absence of compositional safety guarantees that survive agent-to-agent delegation.

This work diagnoses four structural limits of the dominant paradigm: (1) context-window memory creates ephemeral, unverifiable state; (2) informal tool-calling interfaces lack precondition/postcondition semantics; (3) inter-agent trust is implicitly inherited rather than explicitly negotiated; and (4) existing benchmarks measure shallow reactive competence rather than long-horizon coherence under adversarial perturbation. Against this diagnosis, four technically-detailed research directions are proposed: typed agent communication protocols with verifiable postconditions; hierarchical memory architectures grounded in external write-ahead logs; a trust-propagation algebra for multi-agent delegation chains; and a new benchmark family, Long Horizon Agent Bench (LHAB) designed to stress-test agents over multi-day, multi-session task horizons. Proof-of-concept experiments feasible in 2026–2027 are outlined, closing with a 36-month research agenda for the community.