# Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows

arXiv: https://arxiv.org/abs/2606.14769 · HTML: https://mlsanity.com/paper/2606.14769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [Measuring AI Ability to Complete Long Software Tasks](https://mlsanity.com/paper/2503.14499/) (2025)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats](https://mlsanity.com/paper/2507.10621/) (2025)
- [Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories](https://mlsanity.com/paper/2404.01205/) (2024)
- [Duplicity Games for Deception Design with an Application to Insider Threat Mitigation](https://mlsanity.com/paper/2006.07942/) (2020)
- [Internet of Agentic AI: Incentive-Compatible Distributed Teaming and Workflow](https://mlsanity.com/paper/2602.03145/) (2026)
- [Revisiting Game-Theoretic Control in Socio-Technical Networks: Emerging Design Frameworks and Contemporary Applications](https://mlsanity.com/paper/2411.01794/) (2024)
- [Dynamic Games for Secure and Resilient Control System Design](https://mlsanity.com/paper/1910.07510/) (2019)

## Abstract (arXiv)

Agentic AI systems are increasingly being deployed as productive resources in organizational workflows, yet existing evaluation methods primarily measure isolated technical performance rather than economic contribution. This paper introduces Agentomics, a workflow-based framework for valuing, attributing, and pricing human and artificial agents. The framework models a workflow as a configuration of heterogeneous agents whose collective performance determines gross value, deployment cost, reliability, and expected failure loss. Workflow value is treated as a team-level quantity that may include complementarities, substitution effects, bottlenecks, and nonlinear production; additive stage-level value is only a special case. Building on this workflow model, the paper formulates AI deployment as a coalition-formation problem and defines coalition value as the incremental net surplus generated relative to a benchmark human workflow. The Shapley value is then used to attribute economic surplus among participating AI agents, yielding a principled connection among valuation, accountability, and market pricing. The resulting Shapley pricing equilibrium provides a normative benchmark for assessing whether agent prices reflect expected marginal contribution. A security-operations case study illustrates how the framework accounts for productivity gains, deployment costs, reliability losses, and coalition-level complementarities in hybrid human--AI workflows.