By Saad Ahmed, Associate Specialist Solutions Architect at Red Hat and Francisco P Hernandez, Associate Principal Account Solution Architect – Telco at Red Hat
The telecommunications industry is accelerating its digital transformation, driven by the increasing complexity of modern networks and the demand for faster, more reliable services rollout. To meet these demands, operators are turning to autonomous intelligent networks, designed to ingest massive amounts of data and autonomously execute actions at high speed.
The journey to autonomous intelligent networks is not a technology project—it is a mandatory operational shift to protect margins and accelerate time-to-service. This has led to concepts such as a DarkNOC, a network operations center that can operate without direct human intervention, using technology to enhance network reliability, improve performance, and increase cost-efficiency.
There are two fundamental tenets for building autonomous networks: Better network AI insights and actionable automation.
For any AIOps solution to be effective, it must be built on a foundation of high quality, AI-driven insights. These insights are derived from capabilities like:
Red Hat empowers our partners to build powerful AIOps workflows by providing a robust, unified platform. Our portfolio, including Hybrid cloud infrastructure, Cloud-native development, Artificial intelligence (AI), IT automation and management and Edge computing represented by several technologies such as Red Hat OpenShift, Red Hat OpenShift AI, Llama Stack Agent Framework, Red Hat Enterprise Linux, and Red Hat Runtimes offers the essential CaaS (Container-as-a-Service) and AI platforms that AIOps solutions require.
Gaining insights is only half the battle. You also need the ability to act on them quickly and reliably. You need actionable automation. Red Hat’s involvement in concepts like the TM Forum DarkNOC Catalyst project highlighted the need for a unified approach to automation, such as one centered around Red Hat Ansible Automation Platform, that could overcome the fragmented landscape of proprietary tools and scripts.
The true power of this integrated approach is its ability to create a closed-loop system that moves from problem to resolution faster than any human-driven process. This agentic system autonomously detects an issue, determines the fix, generates the code, and remediates the problem—all in an auditable and policy-governed way. This means issues that once required a full team and hours of work are resolved automatically, maximizing network uptime.
The key primitives for an effective, multi-domain automation strategy include:
Today, we are evolving this vision beyond just generating code with generative AI services using Red Hat Ansible Lightspeed, a feature native to Red Hat Ansible Automation Platform. We are now adding agentic AI and model context protocol (MCP) to close the automation loop.
Agentic AI represents a significant leap forward, because these are autonomous systems that can plan and execute complex tasks. By integrating agentic AI with Red Hat Ansible Automation Platform, we enable a system that can not only generate remediation code, but also intelligently orchestrate its execution, governed by AI guardrails through automated policy as code.
We’ve built a comprehensive demonstration that illustrates how Red Hat’s integrated and optimized portfolio combines better AI insights with actionable automation, using both generative and agentic AI to build the intelligent, autonomous networks of the future.
Here’s how the workflow of the demonstration unfolds:
Event takes place: An intentional service failure is created.
Alert and trigger: Kafka transports the event in a large and distributed environment. Then it gets picked up by Event-Driven Ansible which automatically triggers a rulebook workflow in Ansible Automation Platform.
AI analysis and insight: The workflow sends error logs to an agent, powered by Llama Stack, for root cause analysis (RCA). Based on this analysis, a prompt is generated (to be used for creating a remediation playbook). Simultaneously, the Network Operations team is notified with the error logs and the AI-generated RCA.
Red Hat technologies enhance AIOps solutions by optimizing event collection and efficiently running LLMs to improve root cause analysis.
Agent decision: Based on the analysis, the agent decides whether it can handle the issue on its own. It checks Ansible Automation Platform, using the third-party MCP, for an existing job template that could fix the problem. If it finds one, it runs the template automatically to resolve the issue. If not, the remediation is handled through a human-in-the-loop process, to ensure the correct action is taken.
Ansible code generation: A human operator provides the prompt to the AI agent. Using the third-party MCP and Ansible Automation Platform, the agent feeds this prompt to Red Hat Ansible Lightspeed to generate a new remediation playbook.
While this process could be fully automated, a human-in-the-loop is intentionally included to review and validate that the generated playbook is accurate and safe before it’s executed.
System configuration: After verifying that the generated playbook is accurate and safe, the human operator instructs the AI agent to trigger a workflow in Ansible Automation Platform. This workflow pushes the new Ansible playbook to a Git repository, syncs the project in Ansible Automation Platform, and creates a job template to run it.
Remediation: Finally, the AI agent calls Ansible Automation Platform to orchestrate execution of the Ansible Playbook that resolves the original issue that caused the service outage. Within Ansible Automation Platform, policy as code provides guardrails to the AI agent (for example, you may not changes to be made during a maintenance window).
Conclusion
The journey toward fully autonomous intelligent networks is complex, but the path is clear and can be broken down into smaller, practical steps. By combining better network AI insights with actionable automation, a service provider can overcome the challenges of fragmentation and build unified, intelligent, and self-healing systems.
Red Hat’s unified set of solutions provides the essential foundation to build these self healing systems Learn more about Red Hat’s integrated set of technologies TM Forum Initiatives and relevant Catalyst projects.
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