From automated response to reasoning: Building confidence in AI-driven NetOps
Network World ·
The concept of probabilistic automation can be off-putting, even divisive. Consider self-driving cars. They’re part of the progression of vehicle automation that includes parallel parking assist, driver assistance and hands-free driving. However, when it comes to self-driving cars, getting on board requires a huge leap of faith that many people aren’t willing to take. Probabilistic automation is the ability to reason, plan and execute actions autonomously within defined governance boundaries. In other words, this is conditional autonomy. These systems use AI to create nuanced, multi-step automations based on specific content, context and learnings. It isn’t instantaneous, and it shouldn’t be mysterious. User visibility and control make it trustworthy—something self-driving cars lack . Probabilistic automation is a powerful tool for solving complex challenges at scale, such as keeping modern networks up and running. It’s time to demystify it for network infrastructure owners and their teams, who need it to be governed, repeatable and safe in production. No leap of faith required. A reasonable evolution Whether keeping lines of communication open, medical equipment online, financial transactions flowing or the lights on, network engineers are responsible for maintaining network resilience. Given the scale and diversity of devices NetOps teams need to manage, and the average cost of downtime reaching $15,000 per minute , the urgency to automate tasks to reduce errors, save time and costs, and strengthen resilience has never been greater. So, many NetOps teams have already embraced deterministic network automation to perform predefined, atomic tasks in a prescriptive way. Backups and updates are two common examples. Think about running an automation to back up a fleet of network devices on your network, or to update the OS version for continuous maintenance. Best practice calls for validating the backup once it is complete or backing up a device before you make a change so that you can roll back if there’s an issue. Those additional steps are important but can be time-consuming to automate and vary by device specifications and network environments. So, teams rely on a library of pre-built, tested automations to ensure they run reliably. For more complex and dynamic activities, AI-driven automation offers the opportunity to take automation to the next level by combining probabilistic reasoning with deterministic execution. Chaining together discrete automations is now a practical option, with AI serving as a trusted assistant to support automation activities. AI draws on a library of previously used automations, identifying the discrete tasks that, when combined, closely match the request, and adapting them to the specific situation. It enriches the data used by referencing specific context and incorporating prior experience, and composes the smaller units of automation into a broader automated process. Vulnerability prioritization and remediation is a timely example, as Mythos and other AI-capable models amplify the noise around software flaws and exploits. Probabilistic automation enables teams to take a pragmatic approach to address the challenge. AI can: Quickly map which vulnerabilities apply to your specific devices and configurations Identify vulnerabilities that are being exploited in the wild Prioritize remediation based on the actual risk to your network Recommend a remediation or a workaround without introducing new risks Provide and execute remediation steps Details matter Here’s where nuance and governance come in. Every NetOps team has its own standard operating procedures. Additionally, network devices have nuances, network environments differ and the time it takes to complete activities varies. Having a detailed understanding of the mechanics of the activity and the automation helps you determine whether each step in the activity aligns with your internal practices, meets device requirements and is set up for success or could create an issue. Notifications and boundaries put people in control, enabling them to get ahead of issues, investigate and make decisions. Chains give you a clear, digestible way to do this without the need for scripting knowledge. As the expert, you validate each step to ensure it aligns with your practices. You set exception-based alerts and designated checkpoints based on your parameters. You then test each discrete automation in your lab environment before adding it to the chain. Finally, you test the entire chain before implementing it into production. This transparency, auditability and human-in-the-loop process allows you to up-level automation and achieve an outcome you can trust. Enjoy the ride Self-driving cars face a myriad of situations that they were never trained to handle. Even worse, when cars are in trouble, they don’t always know to ask for help, and there’s no way for a passenger to intervene and stop the car. Ultimately, humans are accountable for actions taken, not machines, so we must treat AI as an assistant and rely on humans as experts. Combining probabilistic reasoning with deterministic execution helps teams manage complex network environments at scale. It also delivers AI-driven automation that NetOps teams can trust in production. It’s the right next step in the evolution of network automation and allows network engineers to enjoy the ride.
The concept of probabilistic automation can be off-putting, even divisive. Consider self-driving cars. They’re part of the progression of vehicle automation that includes parallel parking assist, driver assistance and hands-free driving. However, when it comes to self-driving cars, getting on board requires a huge leap of faith that many people aren’t willing to take. Probabilistic automation is the ability to reason, plan and execute actions autonomously within defined governance boundaries. In other words, this is conditional autonomy. These systems use AI to create nuanced, multi-step automations based on specific content, context and learnings. It isn’t instantaneous, and it shouldn’t be mysterious. User visibility and control make it trustworthy—something self-driving cars lack . Probabilistic automation is a powerful tool for solving complex challenges at scale, such as keeping modern networks up and running. It’s time to demystify it for network infrastructure owners and their teams, who need it to be governed, repeatable and safe in production. No leap of faith required. A reasonable evolution Whether keeping lines of communication open, medical equipment online, financial transactions flowing or the lights on, network engineers are responsible for maintaining network resilience. Given the scale and diversity of devices NetOps teams need to manage, and the average cost of downtime reaching $15,000 per minute , the urgency to automate tasks to reduce errors, save time and costs, and strengthen resilience has never been greater. So, many NetOps teams have already embraced deterministic network automation to perform predefined, atomic tasks in a prescriptive way. Backups and updates are two common examples. Think about running an automation to back up a fleet of network devices on your network, or to update the OS version for continuous maintenance. Best practice calls for validating the backup once it is complete or backing up a device before you make a change so that you can roll back if there’s an issue. Those additional steps are important but can be time-consuming to automate and vary by device specifications and network environments. So, teams rely on a library of pre-built, tested automations to ensure they run reliably. For more complex and dynamic activities, AI-driven automation offers the opportunity to take automation to the next level by combining probabilistic reasoning with deterministic execution. Chaining together discrete automations is now a practical option, with AI serving as a trusted assistant to support automation activities. AI draws on a library of previously used automations, identifying the discrete tasks that, when combined, closely match the request, and adapting them to the specific situation. It enriches the data used by referencing specific context and incorporating prior experience, and composes the smaller units of automation into a broader automated process. Vulnerability prioritization and remediation is a timely example, as Mythos and other AI-capable models amplify the noise around software flaws and exploits. Probabilistic automation enables teams to take a pragmatic approach to address the challenge. AI can: Quickly map which vulnerabilities apply to your specific devices and configurations Identify vulnerabilities that are being exploited in the wild Prioritize remediation based on the actual risk to your network Recommend a remediation or a workaround without introducing new risks Provide and execute remediation steps Details matter Here’s where nuance and governance come in. Every NetOps team has its own standard operating procedures. Additionally, network devices have nuances, network environments differ and the time it takes to complete activities varies. Having a detailed understanding of the mechanics of the activity and the automation helps you determine whether each step in the activity aligns with your internal practices, meets device requirements and is set up for success or could create an issue. Notifications and boundaries put people in control, enabling them to get ahead of issues, investigate and make decisions. Chains give you a clear, digestible way to do this without the need for scripting knowledge. As the expert, you validate each step to ensure it aligns with your practices. You set exception-based alerts and designated checkpoints based on your parameters. You then test each discrete automation in your lab environment before adding it to the chain. Finally, you test the entire chain before implementing it into production. This transparency, auditability and human-in-the-loop process allows you to up-level automation and achieve an outcome you can trust. Enjoy the ride Self-driving cars face a myriad of situations that they were never trained to handle. Even worse, when cars are in trouble, they don’t always know to ask for help, and there’s no way for a passenger to intervene and stop the car. Ultimately, humans are accountable for actions taken, not machines, so we must treat AI as an assistant and rely on humans as experts. Combining probabilistic reasoning with deterministic execution helps teams manage complex network environments at scale. It also delivers AI-driven automation that NetOps teams can trust in production. It’s the right next step in the evolution of network automation and allows network engineers to enjoy the ride.