---
title: "AIOps Architecture: From Detection to Remediation"
description: "AIOps automates the detect-investigate-analyze-recommend-execute-verify cycle by combining AI reasoning with infrastructure automation, keeping humans in control of credentials and final decisions."
---

# AIOps Architecture: From Detection to Remediation

**Type:** [Architecture](https://yomu.fyi/visual-explainers/category/architecture.md)

![Five-stage AIOps workflow showing detection, investigation, analysis, recommendation, and execution phases, with AI and infrastructure platforms separated by a control boundary.](https://yomufyi.s3.us-east-1.amazonaws.com/visual-explainers/aa364c52-7380-49ec-8e3e-c1d5e0e6a803/image-5f91629c-106f-45d7-b93c-d7e86ee69b4d.webp)

> AIOps automates the detect-investigate-analyze-recommend-execute-verify cycle by combining AI reasoning with infrastructure automation, keeping humans in control of credentials and final decisions.

## Explanation

AIOps integrates artificial intelligence into operations workflows to accelerate incident response from detection through remediation. The core pattern chains five stages: detection identifies anomalies or alerts, investigation gathers evidence through automated collection, analysis applies AI reasoning to root causes, recommendation proposes approved actions, and execution deploys changes through deterministic infrastructure-as-code tools. The architecture separates probabilistic reasoning (handled by AI) from deterministic execution (handled by infrastructure platforms like Ansible), ensuring that AI informs decisions but does not directly access credentials, inventory, or production systems. This separation preserves auditability, compliance, and human accountability while reducing mean time to resolution.

**Tags:** [AI](https://yomu.fyi/topic/ai.md), [AI Agents](https://yomu.fyi/topic/ai-agents.md), [AIOps](https://yomu.fyi/topic/aiops.md), [Incident Response](https://yomu.fyi/topic/incident-response.md), [Monitoring](https://yomu.fyi/topic/monitoring.md)

## Related reading

### [From incident to remediation: Building an AI-driven AIOps workflow with Red Hat Ansible Automation Platform](https://yomu.fyi/post/from-incident-to-remediation-building-an-ai-driven-aiops-workflow-with.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Neha Chugh
- Published: Sep 5, 2026
- Type: Tutorial

This tutorial builds an AIOps workflow for diagnosing and remediating a deliberately induced CPU incident in a product catalog application running on an AWS EC2 instance with RHEL. An Ansible investigation playbook collects CPU utilization, top processes, application status, and HTTP health evidence, which Claude Code analyzes through MCP to identify the Python process as the likely contributor and recommend the approved “AIOps - Remediate High CPU” Job Template. AI does not access the host directly: Ansible Automation Platform retains responsibility for credentials, inventory, execution, and remediation. The remediation changes the application from HTTP 503 and degraded to HTTP 200 and healthy, while a final health check confirms recovery and completes the detect-investigate-analyze-recommend-execute-verify sequence.


### [Build AI-assisted firewall workflows with Ansible Automation Platform](https://yomu.fyi/post/build-ai-assisted-firewall-workflows-with-ansible-automation-platform.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Michal Zdyb
- Published: Sep 4, 2026
- Type: Problem & solution

The workflow replaces a manual, fragmented process for turning a simple ServiceNow connectivity request into firewall changes. Event-Driven Ansible connects ServiceNow, GitHub, an LLM, and two Palo Alto Networks firewalls, checking drift, gathering topology data, and coordinating structured rule proposals, pull requests, and workflow records. Ansible Automation Platform keeps credentials away from the AI and deploys approved rules through a deterministic, idempotent path after engineer and CAB approvals. GitHub’s main branch records the last successful state, proposed changes are reviewed as Configuration as Code, and execution stops when live configuration differs from the intended state. The lab demonstrates partial coverage, no coverage, and an unresolvable request; failed deployments can roll back, while an undefined Palo Alto Networks App-ID stops the workflow for human intervention.


### [Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models](https://yomu.fyi/post/introducing-context-aware-vulnerability-discovery-and-remediation-with.md)
- Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md)
- Author: Ken Sanderson
- Published: Sep 3, 2026
- Type: Announcement

Cloudflare announces invitation-only early access to Vulnerability Discovery and Remediation, a Managed Defense service that investigates authorized codebases and helps prioritize and mitigate vulnerabilities. It combines source-code analysis with production route activity, traffic volume, security events, and active Web Application Firewall (WAF) protections to assess exposure. Reconnaissance maps request paths to code, hunter agents search relevant sections, and validation checks proposed patches and custom WAF rules before customer review. OpenAI Daybreak models, including GPT-5.6 Cyber, run reconnaissance, hunting, and validation through AI Gateway, while source code, logs, and request metadata are bounded by authorization, redaction, access policies, and external checks. Findings receive source-based risk ratings that network evidence can raise, with code patches and conservative WAF mitigations prepared for review; customers decide whether to test or deploy them.


### [Evaluate AI agents with IBM CLEAR & EvalHub on OpenShift AI](https://yomu.fyi/post/evaluate-ai-agents-with-ibm-clear-evalhub-on-openshift-ai.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Hema Veeradhi, Surya Pathak
- Published: Sep 3, 2026
- Type: Tutorial

Reliable AI agents can fail in production through incorrect tool use, off-topic reasoning, or plausible but incorrect answers that unit tests, static benchmarks, and logs may not expose. This tutorial builds an eval-driven trace-evaluation pipeline on Red Hat OpenShift AI 3.4 or later, using MLflow to store structured agent traces and evaluation artifacts, IBM CLEAR to score reasoning quality, relevance, and tool usage with an LLM judge, and EvalHub to orchestrate OpenShift jobs. It uploads eight prerecorded multistep research-agent traces, each containing at least 30 spans, then verifies them in MLflow before deploying EvalHub, registering the ibm-clear provider, and configuring judge-model credentials. Standard mode evaluates steps holistically, while SPARC mode gives tool calls separate scoring rows; the resulting workflow produces critiques and ranked failure patterns with frequency, severity, and step or agent attribution.