# Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains

arXiv: https://arxiv.org/abs/2605.26146 · HTML: https://mlsanity.com/paper/2605.26146/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 21 of 21)

- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Towards a Science of Scaling Agent Systems](https://mlsanity.com/paper/2512.08296/) (2025)
- [Multi-Agent Collaboration via Evolving Orchestration](https://mlsanity.com/paper/2505.19591/) (2025)
- [Learning Curve Theory](https://mlsanity.com/paper/2102.04074/) (2021)
- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce](https://mlsanity.com/paper/2506.06576/) (2025)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows](https://mlsanity.com/paper/2509.11079/) (2025)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [Transferability-Guided Cross-Domain Cross-Task Transfer Learning](https://mlsanity.com/paper/2207.05510/) (2022)
- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [Formalising Human-in-the-Loop: Computational Reductions, Failure Modes, and Legal-Moral Responsibility](https://mlsanity.com/paper/2505.10426/) (2025)
- [Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation](https://mlsanity.com/paper/2409.02391/) (2024)
- [Context Engineering: From Prompts to Corporate Multi-Agent Architecture](https://mlsanity.com/paper/2603.09619/) (2026)
- [A Mathematical Framework for AI-Human Integration in Work](https://mlsanity.com/paper/2505.23432/) (2025)
- [Characterization of Transfer Using Multi-task Learning Curves](https://mlsanity.com/paper/2512.24866/) (2025)
- [Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration](https://mlsanity.com/paper/2604.04258/) (2026)

## Abstract (arXiv)

Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform. Yet the meta-skills that make these tools effective, prompt engineering (interaction-level optimization) and context engineering (structured input pipeline design), are domain-portable: a practitioner who masters them can apply them to any purpose-built AI tool in any domain. This paper defines Augment Engineering as the discipline of orchestrating multiple purpose-built AI tools across distinct professional domains, applying prompt and context engineering as portable competencies that transfer across tool boundaries. We present a six-phase orchestration methodology and four portability metrics. A 5-month formative case study (November 2025 to March 2026) documents a single practitioner applying these skills across a ten-component orchestration stack spanning seven professional domains, producing work products that would traditionally involve separate domain specialists. Two quantitative observations are consistent with the framework's predictions: a Cochran-Armitage trend test (n = 200 interactions across two chat LLMs, p < 0.01) shows first-pass acceptance rising with prompt-sophistication level, and a Wright's Law fit (n = 82 artifacts, p < 0.01) shows production acceleration across the artifact portfolio. Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication. Augment Engineering completes a three-discipline progression: Prompt Engineering (one tool), Context Engineering (reproducible pipelines), Augment Engineering (a portfolio of tools across domains).