Agentic Workflows are structured pipelines in which one or more AI agents execute sequences of actions autonomously to complete complex, multi-step tasks. Each step may involve tool use, memory retrieval, reasoning, or interaction with external systems, with the agent adapting its behavior based on intermediate outputs. Common in applications such as automated research, code generation, and enterprise process automation, agentic workflows are an active area of study in areas including task decomposition, error recovery, and human-in-the-loop oversight.

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Agentic Placement of Microservices on the Computing Continuum

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Deploying microservices across the computing continuum (edge–cloud) requires placement decisions that adapt to workload variation and heterogeneous infrastructure, yet existing solutions often rely on static policies or opaque heuristics. We present Bellona a system for reliable and auditable Large Language Model (LLM)-driven workflow execution that combines a declarative specification language with a runtime that orchestrates tool calls, conditional control flow, and structured LLM reasoning. Using Bellona, we implement an agentic placement workflow that automatically recommends edge or cloud execution. The workflow uses structured prompts and verifiable tool interactions to (i) parse placement and latency-report instructions, (ii) update the latency log, and (iii) select placements based on measured latency improvement thresholds. We evaluate the resulting agent on two representative microservices-based video analytics applications (human-attributes detection and face recognition) over two days of varying workload. Across 1,440 placement decisions per service, the agent achieves accuracies of 94.66%/84.94% (human-attributes detection, Day1/Day2) and 80.91%/96.53% (face recognition, Day1/Day2) with GPT-4o; with GPT-5, accuracy increases to 98.82%/99.45% (human-attributes detection) and 99.31%/99.8% (face recognition). These results demonstrate that Bellona can support practical, self-improving agentic control for placement of microservices on the computing continuum.

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