AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression
arXiv:2512.13956v4 Announce Type: replace-cross Abstract: Cloud-native systems have made operational work both more powerful and harder to automate: incidents unfold across microservices, logs and metrics arrive faster than operators can inspect them, and recovery actions must be coordinated without losing the causal context that makes them safe. We present AOI (AI-Oriented Operations), a context-aware multi-agent framework for autonomous IT operations. AOI separates operational responsibility across an Observer, a read-only Probe, and a guarded Executor, and connects them through dynamic sche
The increasing complexity of cloud-native systems and the rapid generation of operational data necessitate advanced automation solutions to maintain operational integrity and efficiency.
This development indicates a significant step towards autonomous IT operations, reducing human intervention and potential errors in complex system management, critical for scaling and resilience.
The operational paradigm for cloud-native systems will shift from human-intensive oversight to a multi-agent, context-aware autonomous framework, redefining IT workflows and incident response.
- · Cloud infrastructure providers
- · Enterprises with complex IT operations
- · AI/ML operations tool vendors
- · Traditional IT operations roles (manual)
- · Legacy monitoring solutions
- · Companies unable to adopt AI-driven operations
Reduced mean time to resolution (MTTR) for IT incidents and improved system uptime.
Increased demand for talent skilled in designing, deploying, and managing AI-driven operational frameworks.
The abstraction of IT infrastructure management could lead to entirely new business models focused on 'infrastructure as an outcome' rather than 'infrastructure as a service'.
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Read at arXiv cs.AI