Researchers have developed Intent Engine, a system that translates natural-language intents into Service-level Objectives (SLOs) for microservice placement in the compute continuum. This is done to overcome limitations of large language models (LLMs), which can misinterpret or fail to generate valid SLO artifacts due to unsupported constraints, incorrect values, and schema violations. Intent Engine uses a combination of schema-constrained extraction, retrieval-grounded value construction, and validation against supported constraints to produce accurate SLO artifacts. The system was evaluated using a 716-record intent-to-SLO dataset and outperformed prompting baselines and a non-LLM rule-based parser. --- Why it matters: This matters because it addresses the challenge of translating user intents into actionable Service-level Objectives (SLOs) for microservice placement, which can be error-prone with current large language models. This improvement has significant implications for efficient and reliable compute continuum service placement. Source: https://arxiv.org/abs/2608.20388