2026 Omdia Research Report: Network Observability in the Agentic AI Era
How AI-powered network observability is becoming essential for modern operations—and why NetOps, SecOps, and Cloud teams are converging around it.
As AI-powered applications move into production, the network has become a critical dependency and a growing risk. At the same time, hybrid, multicloud, and containerized environments have made visibility more fragmented and harder to manage.
Traditional monitoring tools are no longer enough. Organizations are turning to network observability, enhanced by AI and emerging agentic architectures, to deliver the context, intelligence, and automation required to keep digital operations running.
As a result, network observability is no longer just a monitoring capability. It is becoming a foundational requirement for operating AI-driven systems reliably and at scale. Network observability goes beyond traditional monitoring by correlating performance, traffic, and dependency data to explain not just what is happening in the network, but why.
What the research reveals
This research explores how enterprises are responding and where gaps still remain. The research highlights strong engagement from Security Operations, Cloud Operations, DevOps, and Platform Engineering, reinforcing observability as a shared foundation for cross-team collaboration. Many organizations now view network observability as a key pillar of broader NetSecOps initiatives, supporting faster incident response and improved security posture.
Key research signals
80%+
expect increased
network observability
spending over the next two years
85%
say AI is meeting or exceeding expectations in observability
use cases
70%
say sharing observability data between NetOps and SecOps
is essential
These signals point to a clear conclusion: network observability is no longer optional infrastructure.
It is becoming a strategic capability.
Key findings:
1. AI is changing what observability must deliver
The research shows that AI is already widely used in network observability, with organizations applying it to performance optimization, security threat identification, and operational efficiency. Expectations are high, and in most cases, AI is delivering measurable value.
Agentic AI, while still emerging, is gaining real traction. More than half of organizations report active use today, with broader adoption expected as teams look to simplify integrations, close skills gaps, and move toward more autonomous operations.
2. Observability is driving cross-team convergence
Network observability data is no longer confined to networking teams. The research highlights increasing collaboration between NetOps and SecOps, with observability insights shared to reduce risk, improve response times, and support coordinated decision-making.
This convergence reflects a broader shift toward integrated operational models, where networking, security, and cloud teams rely on shared visibility to manage complex environments.
3. Complexity remains the biggest barrier
Despite increased investment, most enterprises still rely on three or more network observability tools. Tool sprawl, data fragmentation, and integration challenges continue to limit the effectiveness of observability initiatives.
The research makes clear that success depends not just on collecting more data, but on correlating it effectively and delivering insights teams can act on.
Chart 1: Level of agreement with statements related to network environments.
As AI technologies move into broader adoption and deployment, nearly everyone agrees that networking is becoming more critical. Download report to read more.
Chart 2: Status of AI technologies within or in conjunction with network observability.
Network observability vendors have been working feverishly to add AI technologies into their products, while those using open source or building their own tools have increasingly ready access to AI componentry. Download report to read more.
What this means
The findings point to a future where network observability must:
- Provide comprehensive visibility across cloud, WAN, data center, and container environments
- Support AI-driven analysis and automation without increasing operational burden
- Enable collaboration across networking, security, and cloud teams
- Scale as AI workloads and digital dependencies grow
Organizations that treat observability as a foundational capability, rather than a set of disconnected tools, will be better positioned to support AI-driven operations.
Get more insights on network
observability’s AI future!
Download the report to understand how AI, and agentic AI in particular, is redefining
what network observability must deliver.
What you’ll learn from this report:
- Why network observability has reached “essential” status for modern IT operations
- The fastest-growing AI use cases in network observability, from performance
optimization to threat detection - How agentic AI is expected to simplify integrations and close skills gaps