AgentWare: Automating the Lifecycle of Agentic Applications across the Edge-to-Cloud Continuum
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity, limited observability, and the lack of systematic evaluation methods. Existing solutions address agent development, observability, or benchmarking separately, offering limited support for the full lifecycle of distributed agentic applications. This paper presents AgentWare, an AgenticOps framework that automates the provisioning, deployment, observability, and evaluation of agentic applications across Edge-to-Cloud infrastructures. AgentWare introduces an end-to-end lifecycle pipeline that automatically prepares heterogeneous execution environments, transforms user-defined agent implementations into distributed applications, deploys agent components across the continuum, and performs unified collection of execution traces, infrastructure telemetry, and evaluation metrics. The framework further supports automated semantic evaluation through LLM-as-a-Judge workflows and generates reproducible reports covering correctness, performance, resource utilization, and energy consumption. We demonstrate the applicability of AgentWare through a distributed book assistant agent deployed across real Edge-to-Cloud infrastructure under multiple deployment and model configurations. The results show that AgentWare enables systematic experimentation and evaluation of distributed agentic applications while significantly reducing the manual effort required for deployment, instrumentation, and analysis.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 17 references that are in the map. First posted on arXiv 28 Sep 2026.
Builds on
10 of 17 references.
Closest ancestors · 2
Same direction, newest first
- 2024AgentOps: Enabling Observability of LLM Agents8 Nov
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
Other references · 8
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
- 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Taskscited by 221
- 2025Evaluation and Benchmarking of LLM Agents: A Surveycited by 145
- 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
- 2025How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inferencecited by 73
- 2025Beyond Black-Box Benchmarking: Observability, Analytics, and Optimization of Agentic Systemscited by 14
- 2025Navigating the Edge-Cloud Continuum: A State-of-Practice Surveycited by 3
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