Context as a Computational Resource: Adaptive Hierarchical Context Orchestration for Long-Horizon AI Software Engineering

Artificial intelligence software-engineering agents are increasingly capable of performing long-horizon tasks involving code repositories, tools, tests, architectural constraints, and cross-domain dependencies. However, as these tasks become more complex, treating the entire project history as a single conversational context can create context pollution, unnecessary token consumption, semantic interference, and degraded reasoning. This paper proposes Adaptive Hierarchical Context Orchestration (AHCO), a systems framework that treats context as a computational resource. AHCO organizes project information into a dynamic context topology and allocates bounded context regions to specialized agent sessions. Unlike static multi-agent architectures, agents can request temporary, scoped interactions with other agents when direct collaboration is beneficial. A policy-aware Context Router controls these interactions through scope, budget, provenance, validation, and expiration rules. The framework introduces three primary mechanisms: context allocation, context routing, and context lifecycle management. Agents exchange validated contracts, schemas, decisions, evidence, and test results rather than unrestricted reasoning histories. Context can move through a lifecycle of discovery, activation, validation, durable storage, compaction, archival, and reactivation. The paper proposes metrics including Context Efficiency, Context Pollution, Communication Efficiency, Context Exposure, Context Debt, and Coordination Overhead. It also presents experimental conditions and ablation studies designed to compare monolithic agents, conventional multi-agent systems, shared-context architectures, static hierarchical orchestration, and adaptive hierarchical orchestration. Real-world scenarios include payment-platform development, enterprise monorepos, production incident diagnosis, regulated healthcare workflows, and large-scale authentication migrations. This manuscript is presented as a research framework and preprint. It does not claim empirical results that have not yet been experimentally obtained. The proposed architecture is intended to establish a testable research direction for context-aware AI software-engineering systems.