A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery

2026-07-13

AI Summary

This work addresses the challenges faced by large language model (LLM) agents operating over flat tool registries—namely, combinatorial explosion in decision space, context saturation, and degraded routing accuracy. To overcome these limitations, the authors propose a skill-tree-based hierarchical architecture that separates routing logic at internal nodes from execution at leaf nodes. Inspired by pushdown automata, the framework incorporates a LIFO stack-frame memory model and a lazy capability discovery mechanism, enabling isolated execution paths and scalable context management. The approach supports manifest-driven single-step execution loops and formal state modeling, significantly improving routing accuracy while reducing memory footprint and prompt costs under conditions of tool proliferation, multi-step workflows, and prompt exposure. This design meets enterprise-grade requirements for isolation and scalability.

Abstract

The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously. This leads to decision-space explosion, context window saturation, and degraded routing accuracy. To address these limitations, this paper presents a hierarchical, skill-based architecture for agentic orchestration. Capabilities are organized as a rooted tree where internal nodes make routing decisions and leaf nodes execute deterministic tasks. The runtime enforces a single-step execution loop governed by a Last-In-First-Out (LIFO) stack, giving the agent a form of memory akin to a Pushdown Automaton, therefore enabling it to track nested execution contexts and resume deterministically from any depth. Capability discovery follows a manifest-driven, lazy-loading protocol: only the immediate children of the active node are loaded, so memory and prompt costs scale with the explored path rather than the global registry. By replacing global memory with localized stack frames, the architecture prevents outputs from one execution branch from leaking into another, establishing the isolation guarantees required for deployment in regulated enterprise environments. We also discuss UPI Help, an AI-powered digital payments support product, as a motivating production deployment context. We provide a mathematical formalization of the orchestration state, detailed algorithmic analysis of the execution loop, and controlled benchmarks comparing flat and hierarchical routing under increasing tool catalogs, multi-step workflow pressure, and visible schema-token exposure per LLM call.

Problem

• LLM agents
• tool registry
• decision-space explosion
• context window saturation
• routing accuracy

Innovation

• hierarchical orchestration
• stack-based execution
• lazy discovery
• agentic architecture
• capability isolation