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Evaluating the Performance and Reliability of Action Models in Real-World Automation Tasks How AI Action Models Perform Real-World Tasks: A Critical Empirical Analysis


Authors : Ede Chizzy Ifesinachi; Abubakar Bello Bada; Sirajo Abdullahi Bakura; Ibrahim Musa Mungadi; B. T. Shehu; Abdulsalam Ibrahim Magawata; Mahe Hafsat Omar

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/2ymc8trm

Scribd : https://tinyurl.com/59nd6z4w

DOI : https://doi.org/10.38124/ijisrt/26May924

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Abstract : We can build AI that sees. We can build AI that talks. But can we build AI that truly acts reliably, safely, and intelligently in the messy, unpredictable conditions of real work? That is the defining question of this moment in artificial intelligence, and this paper answers it honestly, with data. This paper presents a critical empirical analysis of AI Action Models systems that do not merely generate text but execute sequences of real-world actions: sending emails, scheduling appointments, navigating websites, extracting data, and executing multi-step workflows. We investigate three tools representing the current state of action-model AI: GPT-4o integrated with Zapier, ChatGPT with Plugins, and Zapier AI Agents. We evaluate these tools across six practical task categories using four metrics: task success rate, error frequency, time efficiency, and adaptability to unexpected changes grounded in published benchmark data from WebArena, GAIA, and the AIMultiple Business Agent Study (2026).+

Keywords : Action Models, AI Agents, Autonomous AI, Human-AI Comparison, Task Automation, WebArena, GAIA, Zapier Agents, ChatGPT Plugins, Empirical Evaluation, Workflow Automation, Real-World Performance.

References :

  1. AIMultiple Research. (2026). AI Agent Performance: Success Rates and ROI in 2026. https://aimultiple.com/ai-agent-performance
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  5. Zhou, S., et al. (2023). WebArena: A Realistic Web Environment for Building Autonomous Agents. arXiv:2307.13854.
  6. Mialon, G., et al. (2023). GAIA: A Benchmark for General AI Assistants. Meta AI Research. arXiv:2311.12983.
  7.   Zapier. (2026). Zapier Agents: Combine AI Agents with Automation. Zapier Product Documentation.
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  11. Lindy.AI. (2025). Zapier + ChatGPT: Top Integrations and a Comparable Alternative. Lindy Blog.
  12. Epoch AI. (2026). AI Model Benchmarks April 2026. Epoch AI Benchmarks Database. https://epoch.ai/benchmarks
  13. IntuitionLabs AI. (2025). Latest AI Research (Dec 2025): GPT-5, Agents and Trends. IntuitionLabs.
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  16. Ord, T. (2025). Scaling AI Agent Task Success: Complexity and the Human Time Threshold. Independent Research Study.

We can build AI that sees. We can build AI that talks. But can we build AI that truly acts reliably, safely, and intelligently in the messy, unpredictable conditions of real work? That is the defining question of this moment in artificial intelligence, and this paper answers it honestly, with data. This paper presents a critical empirical analysis of AI Action Models systems that do not merely generate text but execute sequences of real-world actions: sending emails, scheduling appointments, navigating websites, extracting data, and executing multi-step workflows. We investigate three tools representing the current state of action-model AI: GPT-4o integrated with Zapier, ChatGPT with Plugins, and Zapier AI Agents. We evaluate these tools across six practical task categories using four metrics: task success rate, error frequency, time efficiency, and adaptability to unexpected changes grounded in published benchmark data from WebArena, GAIA, and the AIMultiple Business Agent Study (2026).+

Keywords : Action Models, AI Agents, Autonomous AI, Human-AI Comparison, Task Automation, WebArena, GAIA, Zapier Agents, ChatGPT Plugins, Empirical Evaluation, Workflow Automation, Real-World Performance.

Paper Submission Last Date
30 - June - 2026

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