newsRed Hat AITrust 88 · LabPublished 1mo agoLive · 1mo ago
Demystifying agentic AI: How to build production-ready AIOps with open source models
In many cases, using agentic AI for incident response automation means sending infrastructure logs to frontier AI models. Every job failure log (complete with hostnames, IP addresses, and system topology) would leave infrastructure the moment it hit a large language model (LLM) endpoint, which would raise huge red flags for compliance teams in heavily regulated industries like financial services and healthcare.As an alternative, these organizations could use open source models hosted on their ow
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repoagentuniverse-ai/agentUniverserepoRiccardoBiosas/awesome-MLSecOpsrepoai-infra-curriculum/ai-infra-mlops-learningrepoGloriaameng/Awesome-Agent-Harnessreposlm_engine/slm-pipeline-appsrepoleptonai/leptonairepoaice-lab/devlogrepoamdadulbari/ai-devops-roadmaprepoCyrax321/CONTINUUMrepoalikon-art/DeterminFlowrepoongridio/ongridpaperLLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applicationsreposimonasrazm/simon-factory-lights-outpaperAgents in the Wild: Where Research Meets DeploymentrepoJwuthri/Tracelyrepopreloop/prelooprepoidvcorreia/engineering-automationsrepoAlexli18/binexreponajeed/ai-agent-eval-harnesspaperAgentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation taskspaperTopological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log AnalysisrepoJwuthri/Tracely-aipaperApodex 1.1: Scaling Agentic Intelligence for Complex Work
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