By 2026, collaborative AI agents will shift network automation from scripted playbooks to self-operating systems that continuously optimize networks that learn, reason, and act with minimal human intervention.

From scripts and playbooks to autonomous operations

For most of the last decade, network automation meant scripts, templates, and intent-based configuration tools. Teams codified routine tasks such as VLAN provisioning, route updates, and firewall changes, then stitched them together with workflow engines. It reduced toil, but human engineers still had to define every decision branch. As networks have expanded across data centers, SaaS, multiple public clouds, and emerging edge sites, that brittle model is starting to buckle.

Analyst firms are now clear about the pivot. Gartner estimates that by 2026, 30 percent of enterprises will automate more than half of their network activities, up from under 10 percent in mid-2023, driven by complexity, skills gaps, and the need to respond in near real time. Gartner Static playbooks cannot keep pace with bursty traffic patterns, AI workloads, and constantly shifting security postures. That is where agentic AI systems enter the picture.

Agentic AI relies on multiple specialized agents that can sense, reason, and act together rather than a single monolithic model. Gartner’s latest strategic technology trends highlight multi-agent systems as a key pattern for 2026, with modular agents collaborating on complex tasks to improve automation and scalability. Gartner In the network domain, that means separate agents for topology discovery, anomaly detection, policy reasoning, remediation planning, and execution, all negotiating in real time.

How agentic AI rewires the network operations center

The heart of this shift is the network operations center. Instead of operators spelunking through dashboards, logs, and CLI outputs, AI agents continuously harvest telemetry, enrich it with topology and business context, and propose or even execute actions. Telecom and enterprise vendors are already piloting agentic models designed to approach Level 4 autonomous networks, in which systems handle most decisions without human intervention while still respecting guardrails. Persistent Systems

Network-specific AI agents can be embedded in management platforms, service orchestrators, and even at the radio access network edge. For example, recent work in fixed-access and mobile networks shows AI agents that reason over multi-layer telemetry, correlate faults, and act on policies, turning rule-based automation into adaptive, self-managing systems. Telecompetitor This recasts the NOC as a supervisory environment where humans define intent and constraints while agents execute thousands of micro-decisions every hour.

Vendors such as Ciena are combining service assurance, planning, and control into agentic frameworks where specialized agents collaborate to synchronize IP and optical layers, tune traffic engineering, and coordinate maintenance windows. Ciena. The result is not just faster mean time to repair, but also continuous optimization of latency, jitter, and energy consumption across multi-vendor infrastructures.

Real-world use cases emerging by 2026

By 2026, the most advanced network automation platforms are expected to use agentic AI for several high-impact scenarios. One is autonomous change management, where an agentic system takes a high-level intent such as “minimize packet loss for real-time collaboration traffic during peak hours” and decomposes it into device-level changes across QoS policies, traffic steering, and path selection.

Another is closed-loop SLO management for critical applications. Agents monitor latency and loss thresholds for telepresence, contact centers, and AI inferencing workloads, then proactively reshape traffic, spin up additional paths, or invoke edge resources when thresholds are at risk. For telecom service providers, similar constructs can automate slice orchestration, dynamically adjusting slice bandwidth and priority for enterprise customers based on real-time analytics. Omdia

Agentic AI is also emerging in root-cause analysis. Some platforms already use multiple agents to triangulate on configuration changes, telemetry anomalies, and external events, automatically constructing narratives such as “a misconfigured BGP community on edge router X caused a cascading route flap.” In 2026-era systems, that narrative will be linked to auto-generated remediation plans and simulations that test the blast radius before changes are pushed.

Governance, trust, and human control

As networks become more autonomous, governance and AI safety move to the forefront. Multi-agent systems can generate powerful emergent behaviors, some of which operators may not anticipate. That is why early adopters are pairing agentic AI with policy engines that encode non-negotiable rules such as regulatory constraints, security controls, and safety thresholds.

Gartner’s broader guidance on AI security emphasizes unified platforms that centralize visibility, enforce usage policies, and protect against AI-specific risks such as rogue agents or policy drift. Gartner For network automation, that translates into strong identity and access management for agents, cryptographic attestation for agent code, and continuous validation of decisions against known-good baselines. Human-in-the-loop controls—like mandatory approvals for high-risk actions—will remain essential even as lower-risk changes become fully automated.

The human role will evolve from manual configuration toward supervising AI systems, curating training data, and tuning policies. Network engineers will need new skills in prompt design, agent orchestration, and AI observability alongside their traditional protocol knowledge.

Closing thoughts and looking forward

Agentic AI represents the most profound shift in network automation since the move from manual CLI to programmatic APIs. By 2026, the combination of multi-agent systems, high-quality telemetry, and intent-driven policy engines will allow leading enterprises and service providers to transform their networks into self-optimizing infrastructures. The organizations that benefit most will be those that start building the data foundations, governance frameworks, and talent pipelines now rather than waiting for “perfect” platforms to arrive.

Done well, agentic AI can turn the NOC into a strategic control plane for digital business, compressing incident timelines from hours to minutes while unlocking new levels of reliability and agility. Done poorly, it can amplify fragile processes and opaque policies at machine speed. Over the next two years, the winning blueprint will be a hybrid one: AI agents doing most of the heavy lifting, guided by clear human intent, transparent guardrails, and relentless feedback loops.

Reference sites

Gartner says 30% of enterprises will automate more than half of their network activities by 2026 – Gartner – https://www.gartner.com/en/newsroom/press-releases/2024-09-18-gartner-says-30-percent-of-enterprises-will-automate-more-than-half-of-their-network-activities-by-2026

Gartner: 30% of Enterprises Will Automate More Than Half of Network Activities by 2026 – APMdigest – https://www.apmdigest.com/gartner-30-enterprises-will-automate-more-half-network-activities-2026

Transforming network management with agentic AI – Persistent Systems – https://www.persistent.com/blogs/toward-self-operating-networks-transforming-network-management-with-agentic-ai/

Automate network operations smarter and faster with agentic AI – Ciena – https://www.ciena.com/insights/blog/2025/automate-network-operations-smarter-and-faster-with-agentic-ai

From automation to autonomy: How AI agents are redefining network operations in fixed access networks – Telecompetitor – https://www.telecompetitor.com/from-automation-to-autonomy-how-ai-agents-are-redefining-network-operations-in-fixed-access-networks/

Co-Editor, Benoit Tremblay, IT Security Management, Montreal, Quebec; Co-Editor,
Peter Jonathan Wilcheck, Miami, Florida.

#NetworkAutomation #AgenticAI #AutonomousNetworks #AIOps #TelecomCloud #NetOps #MultiAgentSystems #ZeroTouchOperations #SelfHealingNetworks #AIInfrastructure