Self-Healing Networks and Intent-Based Networking: Building the Autonomous Enterprise Network

For enterprise leaders, network operations are rapidly evolving from a connectivity management function into a critical enabler of business resilience, digital experience, and AI-driven transformation. As organizations expand across hybrid cloud environments, edge infrastructures, distributed workplaces, and increasingly complex digital ecosystems, traditional network operating models based on manual monitoring and reactive troubleshooting are becoming increasingly unsustainable.

The challenge is no longer simply maintaining network availability. Enterprises must continuously ensure application performance, user experience, security, and operational agility across highly dynamic environments. Yet fragmented infrastructure, siloed operational tools, and growing operational complexity continue to limit visibility, automation, and responsiveness. These constraints create a widening gap between business expectations and the ability of traditional network operations teams to deliver at scale. Avasant Senior Director Mark Gaffney expounds on this in Advanced Networking RadarView 2026™. Gaffney said,

“Fragmented infrastructure and siloed tools remain the biggest roadblocks to building secure, automated enterprise networks. Enterprises are on a journey to develop more comprehensive service operating model visions to leverage the most from AI-driven operations, with a roadmap appropriate for their current level of maturity.”

These factors have created a dependency on advanced network infrastructure, which was also highlighted as one of the key investment priorities for enterprises. According to Avasant Worldwide IT Spending and Staffing Outlook for 2024, advanced networking ranks among the top 10 IT spending initiatives for enterprises. Simultaneously, 68% of organizations are increasing AI investments, particularly in analytics and automation, with generative AI accounting for nearly 40% of total AI spending. These trends underscore the need for networks that can not only support increasingly complex digital environments but also operate with intelligence, automation, and self-healing capabilities.

Given this fragmented infrastructure, siloed operational tools, and the limitations of traditional network operations, how can organizations create autonomous networks that continuously align with business objectives, proactively prevent disruptions, autonomously remediate issues, and deliver resilient service experiences at scale?

To address this challenge, enterprises are increasingly embracing two closely connected paradigms: intent-based networking and self-healing network operations.

Intent-Based Networking and Self-Healing: Two Sides of Autonomous Operations

Intent-based networking and self-healing networking are essentially two complementary capabilities that together form the foundation of autonomous networks. Intent-based networking establishes the desired business outcomes and continuously validates whether network behavior aligns with those objectives. Self-healing capabilities leverage AI, observability, closed-loop automation, and analytics to predict, detect, diagnose, and remediate issues that threaten those desired network outcomes. Together, they form the foundation of autonomous networking, enabling organizations to move from reactive operations toward continuously optimized, business-aware network infrastructure.

In simple terms, intent-based networking answers what the network should achieve, while self-healing networking explores how the network can automatically ensure that the outcome is maintained. Together, these capabilities create a network that can understand intent, monitor conditions, identify deviations, and autonomously take corrective action.

How the Autonomous Network Operates

An autonomous network combines intent-based networking and self-healing operations to continuously align network behavior with business objectives while minimizing human intervention.

The process of intent-based networking begins with defining business intent. Instead of direct manual configuration of devices, network administrators specify desired outcomes such as application performance targets, security requirements, user experience expectations, or compliance policies. Intent-based networking platforms, such as Cisco Catalyst Center (formerly DNA Center) and Juniper Mist AI, translate these business objectives into network-wide policies and configurations that can be automatically deployed across the environment.

To support these objectives, the network continuously collects real-time and historical telemetry from network devices, applications, endpoints, cloud environments, and user experience monitoring tools. Platforms such as Cisco Catalyst Center (formerly DNA Center), Juniper Mist AI, and HPE Aruba Central provide the observability foundation required to understand network behavior and verify whether business intent is being met.

Intent-based networking reduces errors and risk while improving operational efficiencies in the following ways:

1. Validates intent objects before applying them to the network. Intent objects are high-level representations of the desired properties or outcomes to be achieved with the network. Validation is syntactic and includes semantic checks against networkwide policy.
2. Instantaneous roll-back or roll-forward. Operators simply apply the appropriate versioned intent object to return to a known good state if something goes wrong during a deployment push.
3. Limits the impact and scope of failures during new intent rollout through a well-defined policy.
4. Intent-based fallback. As the system knows the desired outcomes for a specific configuration, it can maintain those outcomes even in the face of outages or device errors by reconfiguring other network elements or using different mechanisms to achieve the same results.

When deviations occur, self-healing mechanisms automatically execute remediation actions such as rerouting traffic, adjusting configurations, modifying policies, or performing other forms of traffic engineering. This closed-loop automation reduces reliance on manual intervention and significantly accelerates issue resolution.

A self-healing network relies on a collection of real-time and historical data on the health and performance of the network and uses it to gain visibility into the network’s operations in order to automate remediation of network problems. Once data is collected, AI and ML techniques are used to analyze it. AI/ML engines analyze the collected information to establish operational baselines and recognize abnormal patterns. Organizations often create digital twins, virtual representations of the network that allow potential changes to be tested before implementation.

Once baseline performance thresholds are established, continuous monitoring enables the identification of emerging issues, degradations, or policy violations. With this, self-healing networks can automatically remediate issues by rerouting traffic, changing configurations, or other forms of traffic engineering. Self-healing networks also include advanced correlation to reduce duplication of tickets from multiple endpoints around the same issue.

AI/ML engines analyze the collected information to establish operational baselines and recognize abnormal patterns. Organizations often create digital twins, virtual representations of the network that allow potential changes to be tested before implementation. Once baseline performance thresholds are established, continuous monitoring enables the identification of emerging issues, degradations, or policy violations.

The following image highlights that successful self-healing networks require more than technology adoption; they demand organizational transformation, standardized operations, AI augmentation, and process reengineering to achieve true autonomous operations.

While organizations increasingly position self-healing networks as a path toward fully autonomous “NoOps” environments, most enterprises remain in the automated or AI-assisted stages of maturity. Human oversight continues to play a critical role in policy definition, change governance, exception handling, and risk management, making true autonomous networking an aspirational goal rather than a near-term reality.

Market Investments and Provider Innovation

Self-Healing Networks

A growing ecosystem of service providers and platform vendors is investing heavily in these capabilities through AI-driven assurance, autonomous remediation, predictive operations, digital twins, and agentic AI-powered network management. Key examples include:

1. Tech Mahindra’s Autonomous Network Operations Platform (ANOP) focuses on intent-based orchestration, policy automation, closed-loop assurance, zero-touch provisioning, and agentic remediation to enable autonomous network operations.
2. HCLTech iAutomate NetBot emphasizes automated remediation, configuration drift detection, topology-aware root cause analysis, and autonomous assurance to improve operational efficiency and resiliency.
   

   1. A US media organization operating more than 1,200 sites and over 5,800 network devices struggled with visibility challenges and network downtime. By deploying HCLTech iAutomate NetBot, it introduced proactive monitoring, event correlation, and automated remediation capabilities that reduced MTTR by 20%–25% and improved operational efficiency.

3. Capgemini PRiSM supports AI-first dark network operation centers (NOC) through predictive failure detection, advanced observability, and self-remediation across SD-WAN, SASE, multicloud, and data center environments.
   

   1. Capgemini PRiSM deployments have demonstrated the value of AI-driven incident detection, predictive management, and closed-loop remediation, reducing human intervention while accelerating recovery times.

4. Microland intelligeni leverages agentic AI and Systems of Autonomous Action (SoAA) to transition network operations from passive automation to real-time self-healing and self-optimizing execution, advancing self-driving LAN and WAN capabilities.
5. Infosys Intelligent Network Platform (IINP) combines predictive analytics, anomaly detection, topology-aware analysis, policy drift identification, and automated remediation through closed-loop workflows to accelerate autonomous network operations.
   

   1. Meanwhile, Infosys IINP has enabled customers to automate troubleshooting, device management, configuration updates, and remediation workflows, improving assurance and supporting proactive self-healing operations.

6. Among the platform vendors, AWS utilizes monitoring, anomaly detection, and event-driven automation across services such as Amazon CloudWatch, EventBridge, Lambda, and Systems Manager to support autonomous remediation of network operations. AWS reports that these capabilities can autonomously remediate more than 96% of network and operational events.
   

   1. A notable example is Telkomsel, which developed a generative AI-powered incident analysis system using Amazon Bedrock and AWS Lambda. The solution reduced network diagnostic times from more than an hour to under a minute and improved incident resolution by 83%, demonstrating the operational value of AI-driven self-healing approaches.

7. Cisco has integrated self-healing capabilities across campus, SD-WAN, data center, and IoT environments through continuous telemetry, machine learning, predictive analytics, and closed-loop automation. Solutions such as Cisco Predictive Networks, Cisco Crosswork, and Cisco Catalyst Center help organizations identify performance issues, perform root cause analysis, and trigger automated remediation workflows.
   

   1. Enhanced global connectivity, network automation, and self-healing capabilities for a telecommunication and network provider by deploying Cisco Routed Optical Networking, Cisco 8000 Series Routers, Crosswork Assurance, and Crosswork Network Automation across its worldwide internet backbone. The solution provided IP-optical convergence, AI-driven analytics, proactive assurance, and advanced automation to improve visibility, optimize traffic management, and support the organization’s journey toward self-healing networks. It also reduced CapEx by up to 66% and OpEx by up to 95%, while enabling scalable, low-latency, and energy-efficient connectivity.

8. HPE is pursuing a similar strategy through Aruba Central, Mist AI, Marvis AI, and Marvis Minis. These platforms provide AI-native networking, digital experience twins, proactive monitoring, autonomous troubleshooting, and intelligent network operations.
   

   1. HPE implemented an AI-native networking solution for Aberdeen City Council using HPE Mist AI and Marvis AI to enhance digital learning experiences for over 26,500 students. The self-driving network leveraged AI-driven automation and proactive troubleshooting to simplify IT operations while doubling Wi‑Fi coverage and capacity. This improved network reliability, reduced operational complexity, and enabled a better user experience across the education environment.

Intent-based Networking

1. Capgemini helped Vodafone progress toward autonomous networking using AI-driven observability, closed-loop automation, and predictive operations. The initiative focused on reducing manual intervention, improving service assurance, and automating incident resolution. This demonstrates how networks can continuously align with operational intent while moving toward Level 4 autonomous operations.
2. Openreach partners with Infosys to drive intent-driven operational automation and large-scale fiber transformation. This collaboration leverages intelligent platforms, automated OSS solutions, and digital tools to streamline network rollouts, optimize field logistics, and transition legacy architecture into autonomous networks.
3. Cisco deployed Catalyst Center across its own global network, containing thousands of network devices. The platform enabled policy-based automation, automated provisioning, compliance validation, and continuous assurance. AI-driven analytics and automation reduced manual effort while improving visibility and troubleshooting efficiency.

The Road Ahead: From Automation to Network Autonomy

According to Avasant, the future of enterprise networking will be defined not by incremental automation, but by the evolution toward autonomous networks capable of continuously sensing, deciding, and acting with minimal human intervention. As AI adoption accelerates and digital ecosystems become increasingly distributed, enterprises will require networks that can dynamically align with business intent, predict disruptions before they occur, and autonomously optimize performance across cloud, edge, campus, and data center environments.

For enterprise leaders, the mandate is clear: begin building the foundations for autonomous networking today. This includes investing in intent-driven architecture, unified observability platforms, automation-first operating models, and robust data ecosystems that can support AI-driven decision-making. Organizations that successfully make this transition will be better positioned to reduce operational complexity, improve resilience, accelerate innovation, and deliver the reliable digital experiences required in an increasingly AI-powered world.

By Siddharth Mehta, Research Leader