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

Prashant Devadiga, Abhishek, Adithya Mishra, Alok Singh, Amisha Sinha, Asit Desai, Gaurang Dahad, Harshit Bhushan, Mandati Pramod Reddy, Prakhar Gupta, Rupesh Patil, Siddhi Behere/A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy DiscoveryUnknown

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.

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AgentHub Score

55 / 100

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55Score

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40C

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30C

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70C+

Maturity

45C

Community

42C

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58C

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2026-07-262026-10-15

Practical assessment

Should you use it?

✓ Best for

Multi-agent orchestration
Production agentic workflows
Stateful long-running tasks

◎ Strengths

Stable API
Active release cadence
Strong GitHub community

✕ Not ideal for

Simple single-step automation
Teams without Python/ML expertise

⚠ Watch-outs

Breaking changes between minor versions
Ecosystem lock-in if tightly coupled

Technical details

What's inside

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PaperarXiv ↗

AgentHub Score

Score 55/100

Below average

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