Reliable agent engineering should integrate machine-compatible organizational principles

TL;DR

The paper argues for integrating machine-compatible organizational principles into the engineering of LLM-based agents to improve reliability and societal integration. It draws on organization science to propose architecture- and management-level approaches, including tool-use designs, scaling-aware structures, and HR-inspired interventions. By mapping human organizational concepts to machine agents, it highlights three preliminary accounts: balancing agency and capabilities, scaling with cost-benefit considerations, and internal/external management mechanisms. The work aims to provide a blueprint for governance, coordination, and safe deployment of sociotechnical AI systems.

Abstract

As AI agents built on large language models (LLMs) become increasingly embedded in society, issues of coordination, control, delegation, and accountability are entangled with concerns over their reliability. To design and implement LLM agents around reliable operations, we should consider the task complexity in the application settings and reduce their limitations while striving to minimize agent failures and optimize resource efficiency. High-functioning human organizations have faced similar balancing issues, which led to evidence-based theories that seek to understand their functioning strategies. We examine the parallels between LLM agents and the compatible frameworks in organization science, focusing on what the design, scaling, and management of organizations can inform agentic systems towards improving reliability. We offer three preliminary accounts of organizational principles for AI agent engineering to attain reliability and effectiveness, through balancing agency and capabilities in agent design, resource constraints and performance benefits in agent scaling, and internal and external mechanisms in agent management. Our work extends the growing exchanges between the operational and governance principles of AI systems and social systems to facilitate system integration.

Figures (4)

• Figure 1: Machine-compatible organization theory accounts for the issues of AI agents working in groups in contrast to human agents.
• Figure 2: (a) Tool-use agentic system with distinct delegational structures to handle user requests. Single-agent tool-use has high requirements on user agent capabilities; Provider bundling flows down the requirements externally; Supportive tooling agents can further outsource tool handling to tool providers in exchange for increased system capability. (b) Knowledge-based (as a hierarchical structure) and task-based (as a horizontal workflow) views of organization design for medical agentic systems for patient diagnostics.
• Figure 3: (a) Illustration of the economies and diseconomies in the scaling of firms (i.e. for-profit organizations). (b) Illustration of multiagent debate viewed as organizational conflict within a group which exhibits functional and dysfunctional zones ($R$ is a performance metric as evaluated in Eq. \ref{['eq:org_eval']}). "Conflict intensity" is controllable by the number of agents, number of turns in a debate, etc.
• Figure 4: (a) Design of agent reward according to different types of reward feedback (indicated as ✘ and ✔) in the trajectory of an agent action sequence (horizontal bars). (b) Improving the reliability of AI agents by cultivating an organizational change (left) and a potential implementation (right) with management intervention and performance feedback.

Theorems & Definitions (6)

• Example 2.1: Tool-use agentic systems
• Example 2.2: Medical agentic systems
• Example 3.1: Maintenance cost in scaling
• Example 3.2: Multiagent debate
• Example 4.1: Agent reward design
• Example 4.2: High reliability agent engineering