General AI and Agentic AI (systems that can make decisions and take actions autonomously) can help automate the identification of needed patches, scheduling of updates, and orchestration of deployments, thereby reducing manual effort and human error in the patching process.
However, neither AI nor Agentic AI tools can solve the fundamental problem of delivering patches efficiently and reliably at scale to millions of endpoints without disrupting network operations, which still requires a peer-to-peer (P2P) edge-cloud delivery architecture.
Keep reading to explore why scripts and automation, even when powered by AI, aren’t enough to solve patching at scale, and why intelligent delivery is the missing piece to turning decisions into real-world actions.
Why AI Can’t Fix the Script Problem
In endpoint management, scripts are used widely to automate tasks like patching, configuration, and compliance. While traditional AI and emerging Agentic AI tools can orchestrate and trigger scripts for intelligent, adaptive task execution, those scripts are still hard-coded for specific use cases. Even with AI-driven orchestration, they remain brittle when infrastructure or application versions change.
For example, a script that works in one OS environment may break in another, forcing IT teams to constantly re-write or troubleshoot. So, while AI can enhance the intelligence of automation, it doesn’t address the fundamental weaknesses of script-based approaches.
The Forgotten Half of Patching: Delivery
Delivery is just as important as detection and decision-making. Even if AI scripts can determine what needs patching, every update still needs to reach every endpoint promptly as they are released.
Most patch management solutions rely on each machine pulling large volumes of content from the cloud, which can flood bandwidth and slow down critical business applications.
The real challenge lies in delivery at scale. To avoid network bottlenecks and ensure timely patching, organizations need solutions that go beyond traditional centralized models. P2P delivery, for example, enables endpoints to share updates in real time, reducing strain on central servers and the cloud while accelerating the overall distribution process.
AI Will Amplify, Not Replace, Delivery
AI, and particularly Agentic AI, can optimize what gets patched, when, and why. But without an intelligent delivery engine, AI is not as effective as it could be. Patches still need to move reliably across massive, distributed enterprise environments. Effective solutions combine orchestration with delivery, ensuring that AI-driven insights are immediately translated into real-world action, at scale.
As such, AI amplifies the power of delivery rather than replacing it. Decision-making and execution must work hand in hand; one without the other leaves organizations vulnerable to delayed updates, failed patches, and network congestion.
What to Watch for in "Autonomous" Solutions
Not all solutions labeled “autonomous” are built the same. Some might depend heavily on scripting or centralized delivery architectures that struggle with reliability and scale. Instead, the most effective tools embed autonomous intelligence within a robust delivery framework.
As noted in Adaptiva’s recent Agentic AI blog, true Agentic AI systems act autonomously: interpreting events, making decisions, and executing actions proactively, rather than simply responding to static scripts.
- Beware of solutions that stop at orchestration; AI-driven decision-making is only half the story. Without an underlying architecture that supports resilient, decentralized content delivery, autonomous intentions become bottlenecked in practice.
- Truly scalable delivery requires a peer-to-peer (P2P) edge-cloud architecture, where endpoints themselves share updates in real time to reduce reliance on central servers and cloud bandwidth.
- In contrast to legacy centralized models, this distributed approach, which incorporates features like memory-pipeline delivery, adaptive peer selection, and zero-touch operation, ensures patches flow swiftly even across massive, distributed environments
Here is the criteria to use as a guide when evaluating autonomous endpoint solutions:
- Does the system provide true autonomous decision-making (Agentic AI) rather than just scripted responses?
- Is the delivery architecture scalable and resilient, using P2P/edge techniques rather than centralized pull models?
- Can it handle network latency, bandwidth constraints, and distributed environments reliably, without degrading performance or requiring costly infrastructure?
Thinking Beyond AI: Delivery Matters Too
AI is undoubtedly a powerful enabler in endpoint management, providing intelligence, automation, and insight. But it is not a silver bullet, and even the most intelligent AI cannot compensate for weaknesses in the delivery infrastructure.
Organizations must recognize that the conversation about endpoint management cannot stop at detection and decision-making. How updates, patches, and content actually reach endpoints is just as critical. Without a scalable delivery network, even the most advanced automation can cause delays, strain network bandwidth, and lead to failed updates.
IT teams must think holistically about the end-to-end process, from detection to delivery. Success at scale requires both intelligent automation and a resilient delivery architecture. By addressing both sides of the equation, organizations can fully leverage AI while avoiding pitfalls that can undermine endpoint performance and security.
