Blog post
Just when you’d finally wrapped your head around AI, here comes its autonomous cousin, Agentic AI. Think of it as AI that doesn’t just assist, but acts. It makes decisions, handles tasks, and communicates with other systems on its own. While it’s revolutionizing supply chains and customer experiences, there’s a catch. These autonomous agents rely on a plethora of third-party services, and when one fails, everything stops.
A study of eCommerce companies revealed that 88% of respondents lost more than $100,000 in a month due to Internet disruptions. The risk grows as Agentic AI expands, with each dependency multiplying the chance of downtime. When AI fails, operations halt, revenues drop, and reputations suffer.
So, how do you harness the power of agentic AI without the risk of it all crumbling when one link in the chain breaks? The answer is simple: visibility. Let’s break down why knowing what’s happening behind the scenes is crucial.
The promise of autonomy
Traditional AI systems often rely on human oversight for decision-making. Agentic AI goes a step further—these autonomous agents handle tasks, make decisions, and interact with external systems on their own. From automating supply chains to personalizing customer service, the potential is huge. But here's the catch: these agents depend on a network of external services, and even a small disruption in one service can bring everything to a halt. When the chain breaks, the fallout is immediate and far-reaching.
The hidden pitfalls of Agentic AI
Recent AI outages have shown us just how fragile interconnected technology can be. Agentic AI agents pull data from multiple external services, each of which introduces a new point of failure. When something goes wrong, pinpointing the issue isn’t simple—it requires end-to-end visibility, which most monitoring tools simply can’t provide. Without it, you’re left in the dark, trying to diagnose the problem as everything grinds to a halt.
Here’s where teams get stuck:
- They know their AI-driven service is failing or running slowly.
- They don’t know whether the problem is internal, with the AI provider, or buried somewhere in the network path.
Consider a financial services firm relying on AI-powered agents to handle customer inquiries about transactions and investments. These agents depend on several critical components, as shown in the diagram below.
When an agent requests data from an external service, it triggers a chain of events—each action between agents and systems increasing the complexity. If one of these services fails, the entire process can grind to a halt, leaving customers without support. For businesses, especially in sensitive industries like finance, this can quickly erode trust and push clients toward competitors.
This is why visibility matters: if you can't pinpoint where the failure occurred—whether inside your infrastructure, with the AI provider, or within the network path—you can't fix it quickly. And in industries like finance, every minute of downtime can result in lost revenue and significant reputational damage.
Without a unified view of AI agent dependencies, recovery is slow and costly, leading to inefficient war-room sessions and increasing frustration—both internally and among customers who rely on seamless service.
Building resilient Agentic AI: Key capabilities and practical steps
To protect your agentic AI systems from disruptions, it’s essential to have the right capabilities in place for proactive monitoring. This includes understanding your AI dependencies and being able to identify where failures might emerge across the entire technology stack. Without this visibility, teams may struggle to pinpoint the root cause of issues, leading to slower recovery times and greater customer impact.
Here’s how you can build these capabilities and put them into action:
- Map your AI dependencies
Start by mapping all the dependencies your AI agents rely on. Ideally, you want to visualize every microservice, API, content delivery network (CDN), and DNS route in a simple, interactive, real-time map. This gives you a live view of the entire ecosystem your AI depends on, enabling you to immediately identify issues that impact performance. By having a comprehensive view of these external services, you can quickly diagnose problems, improving both Mean Time to Identify (MTTI) and Mean Time to Repair (MTTR).
- Monitor continuously
Ensure your AI systems perform seamlessly by implementing continuous internet performance monitoring. With real-time, proactive monitoring, you can simulate user journeys and detect anomalies before they escalate into major issues. This allows you to monitor every layer of your internet stack, ensuring uninterrupted performance.
By staying ahead of potential disruptions, you can quickly pinpoint problems and maintain optimal service availability, minimizing downtime and improving the user experience.
- Leverage automation tools for end-to-end workflow testing
Use automation tools like Playwright to simulate real user interactions across complete workflows. This includes tasks such as adding products to the cart, checking out, and engaging with AI-powered agents. By scripting these processes, you can simulate user behavior, ensuring that the AI performs as expected. This capability allows you to identify friction points or performance issues before they impact the user experience, helping you address potential disruptions proactively.
- Plan for Failover
In case a critical AI service fails, do you have a fallback plan in place? Whether it’s switching to a backup model or queueing tasks until service is restored, having a well-defined failover strategy is essential for minimizing the impact of outages.
- Review Performance Data Regularly
Don’t wait for an outage to force your hand. Schedule routine performance reviews of your AI dependencies to spot patterns or subtle issues—such as increasing response times or occasional timeouts—that may indicate underlying problems.
By combining these capabilities with actionable steps, you can ensure your Agentic AI systems remain resilient, proactive, and capable of minimizing downtime. With a clear understanding of your AI workflows and ongoing monitoring in place, you’ll be prepared to manage disruptions and keep your services running smoothly—staying ahead of the next AI meltdown.
Ready to safeguard your agentic AI?
Get a clear view of your AI dependencies with Internet Stack Map and start monitoring what matters.
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