The rapid adoption of AI agents is creating a new growth opportunity for software companies that monitor applications, cloud infrastructure and automated workflows. As businesses deploy AI systems capable of carrying out multi-step tasks with limited human intervention, demand is increasing for tools that can track performance, detect errors and help maintain reliability.
Investment bank Jefferies is highlighting the observability sector as a potential beneficiary of this shift, with Dynatrace and Datadog among the companies attracting investor attention. The investment case centres on a simple change: as software becomes more autonomous and complex, businesses need greater visibility into what their systems are doing and whether they are operating correctly.
Why AI Agents Need Monitoring
Traditional software monitoring focuses on metrics such as response times, system availability, error rates and infrastructure usage. AI agents introduce additional layers of complexity because they can make decisions, call external tools, interact with databases and execute sequences of actions.
When an AI agent produces an incorrect result, the problem may not be a conventional software failure. It could originate in the model’s reasoning, the data it receives, a tool it invokes or the way several steps interact.
Observability platforms help companies trace these processes, identify bottlenecks and investigate failures. They can also support monitoring of model performance, token consumption, response quality and operational costs.
As AI agents move from experiments into business-critical applications, these capabilities may become increasingly important for enterprise technology teams.
Dynatrace Targets Complex Enterprise Systems
Dynatrace provides observability and security software designed to help organisations understand the performance of applications and cloud environments. Its platform uses automation and AI-assisted analysis to identify problems across interconnected systems.
The growth of AI agents could increase the amount of activity enterprises need to monitor. A single agent may interact with several applications and services, creating more dependencies and potential failure points than a conventional software process.
For Dynatrace, this creates an opportunity to expand its role in enterprise IT operations. The key question is whether customers will increase spending on monitoring tools as they scale their use of AI agents.
Datadog Benefits From Cloud-Native Workloads
Datadog offers monitoring, security and analytics tools for cloud applications and infrastructure. Its platform combines operational data from different systems, helping engineering teams investigate incidents and manage performance.
AI applications can generate substantial computing activity and require coordination between models, databases, APIs and external services. Monitoring these interactions may create additional demand for platforms that provide a unified view of the technology stack.
Datadog has also been expanding its AI-related capabilities. However, its ability to convert growing usage into durable revenue and profits will depend on customer adoption, competition and the cost of serving increasingly demanding workloads.
A Growing Market, but Not Without Risks
The observability opportunity extends beyond traditional application monitoring. As businesses introduce AI agents, they may need new tools to assess reliability, control costs, detect unusual behaviour and maintain security.
However, several uncertainties remain. Technology companies could develop more monitoring capabilities internally, while cloud providers and AI-platform vendors may bundle observability features into existing services. Competition could therefore limit pricing power even as overall demand expands.
Investors must also consider valuation. A growing market does not automatically make every company in that market an attractive investment. Revenue growth, operating margins, customer retention, cash flow and the price paid for future earnings remain important considerations.
What Investors Should Watch
The expansion of AI agents could create a long-term demand driver for observability software, particularly as enterprises move from limited testing to large-scale deployment.
Investors should monitor how quickly customers adopt these tools, whether AI-related products generate additional spending and whether vendors can maintain strong margins amid competition. The strongest businesses will need to demonstrate not only that AI workloads are growing, but also that they can capture a sustainable share of the resulting expenditure.
The broader investment theme is clear: as AI systems become more autonomous, monitoring their behaviour becomes more important. The commercial opportunity will depend on how effectively observability providers turn that need into recurring revenue and profitable growth.
