Transforming IT Operations with Agentic AI: The Evolution from Reactive to Autonomous Infrastructure Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information technology operations transformation by artificial intelligence is a paradigm shift from responsive maintenance paradigms to proactive, self-operating infrastructure management systems. Agentic artificial intelligence brings to the table advanced capabilities to allow organizations to identify and fix system problems prior to user impact, automatically craft contextual resolution plans, and drive multifaceted remediation workflows in distributed computing environments. Advanced monitoring systems utilize machine learning algorithms and pattern detection mechanisms to scrutinize immense volumes of operational telemetry data, detecting faint anomalies before significant events. Self-healing resolution planning integrates historical event information, live environmental factors, and multi-objective optimization methodologies to devise customized remediation plans taking into account system load, resource availability, and business impact drivers. Risk assessment capabilities leverage digital twin technologies and predictive modeling to model possible outcomes prior to the instatement of infrastructure modifications, while automated rollback procedures guarantee service availability through the occurrence of unforeseen complications. Cross-functional workflow optimization dismantles organizational silos through the support of wise coordination between network operations, application development, security, and business functions. Implementation necessitates strong technical architectures that enable massive data processing capacities, enterprise-grade observability platforms, and secure communications between independent agents and managed systems. The transformation requires extensive transformation of the workforce, with a focus on collaboration between people and artificial intelligence, where technology performs routine operational tasks and people address strategic decision-making, ethical implications, and novel business situations demanding creativity and social skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it