State-Aware Runtime for Long-Horizon LLM Agents: A Conceptual Framework and Research Agenda

Long-horizon LLM agents are increasingly expected to operate across extended interactions, evolving tasks, tool calls, memory updates, self-improvement loops, and multi-step plans. Many failures in such systems are not adequately explained by single-turn reasoning errors or insufficient model capability. They arise from unstable state maintenance, uncontrolled memory injection, protocol drift, tool-mediated side effects, evolving evaluators, and missing recovery mechanisms. This paper develops State-Aware Runtime as a conceptual framework for governing the lifecycle by which canonical state, bounded state views, model proposals, speculative future states, validators, commits, rollback or compensation, handoffs, and audit traces interact during long-horizon execution. Version 3 extends the transactional view with a deliberation loop: the model proposes candidate actions; a learned world model, executable sandbox, or other simulator projects possible consequences; evaluators rank and prune those speculative trajectories; and the runtime alone decides which current transition may affect reality. The resulting architecture treats long-horizon operation as bounded look-ahead followed by one governed commit, environment observation, prediction-error analysis, and replanning. Recovery must restore not only durable state but also the next context and memory view from which the stochastic model will propose. Highly autonomous or self-improving agents make this governance more important, not less: goals, evaluator versions, metric migrations, self-model changes, and deliberation depth should become durable only through governed state transitions. We conclude with a failure taxonomy, evaluation dimensions, and a research agenda for auditable, recoverable, deliberative, and state-aware agent infrastructure.The framework is conceptual; an executable V4 runtime and benchmark remain future work.