By 2025, artificial intelligence has entered a new phase. The rise of AI agents evokes the world of science fiction: these systems no longer just respond to instructions but can independently perceive, plan, and act. With broad access and autonomous decision-making, Agentic AI can execute entire processes end-to-end and, at the enterprise level, may even replace certain job functions – though the social and labor market implications deserve a separate debate.
In this article, we focus exclusively on the technological, business, and security aspects of Agentic AI and explain why unified Observability is critical to mitigating risks.
What is AI automation?
For many organizations, the enterprise adoption of AI began with automation. In this stage, AI augments existing processes: accelerating data processing, supporting repetitive task execution, or enhancing classical RPA solutions with machine learning modules. Such systems operate on predefined rules: humans design the process logic, and AI executes with high accuracy. This provides stability and predictability but lacks autonomous decision-making. Here, AI is more of an “assistant” than an “independent actor”.
What is an AI agent?
An AI agent represents the next level in AI evolution. While automation executes predefined rules, agents pursue independent goals, make decisions, and act. To achieve this, they rely on three core capabilities:
- Perception: Monitor their environment and gather relevant information.
- Reasoning: Plan optimal steps to achieve goals, breaking tasks into sub-goals if needed.
- Action: Execute tasks using available tools, evaluate outcomes, and iterate as necessary.
This means that AI agents don’t just follow human instructions – they can adapt and find new solutions. For example, a customer service agent not only answers a query but also opens a support ticket, proposes a resolution, and, if authorized, implements the fix without human intervention.
What is Agentic AI?
Agentic AI refers to systems composed of multiple interconnected AI agents working together toward complex goals. While a single AI agent handles a specific task, an Agentic AI system links multiple agents into an adaptive network that can plan, coordinate, and learn collaboratively — enabling continuous, context-aware operation across dynamic environments.
What makes an enterprise AI agent effective — and how does Dynatrace help?
An AI agent delivers lasting business value — and remains low-risk — only if three key conditions are met.
Real-time, context-rich data
An agent can make good decisions only if it sees the current state of systems, customers, and processes. Inaccurate or delayed data leads to faulty conclusions.
For example, a customer support agent can provide the correct answer only if it accesses the live order management system, not just historical records.
Dynatrace ensures that the underlying systems powering AI agents remain stable, observable, and accessible, so agents can rely on up-to-date, accurate information when reasoning and acting.
Transparent operation and oversight
An enterprise AI agent cannot be a “black box.” Every decision, recommendation, and automated action must be traceable and explainable — not only to maintain trust, but also to comply with regulatory frameworks such as NIS2, and DORA.
The Dynatrace platform provides comprehensive logging and explainability features. It documents all events, dependencies, and interventions, allowing organizations to verify which contextual factors and system interactions led to a given decision or outcome. This enables auditable, transparent, and secure AI operations.
What Does an AI Agent Actually Do?
In digital environments, AI agents analyze data, make decisions, and orchestrate automated workflows. Examples include:
- Detecting financial fraud
- Managing customer service requests
- Diagnosing IT system issues
Their purpose is to handle complex digital challenges faster, more accurately, and with minimal human intervention.
In hybrid (physical + digital) environments, AI not only analyzes data but also acts in the real world — controlling machines, vehicles, or robots. Examples include autonomous vehicles, surgical robots, and agricultural drones, which make real-time decisions based on sensor data and backend information, bridging the physical and digital worlds.
What Can the Dynatrace Agent Do?
Dynatrace connects to the world of AI agents in two ways:
- It enhances the reliability and accuracy of any AI agent by ensuring the stability of the underlying systems that store and process real-time, context-rich data. This helps agents avoid making decisions based on incomplete or outdated information.
- It includes its own AI-powered agent, the Dynatrace OneAgent, which makes IT systems faster, more efficient, and self-healing by automating error detection, diagnosis, and remediation.
Key capabilities of Dynatrace OneAgent:
- Automatic data collection and topology mapping: Instantly maps applications, services, and dependencies without manual setup.
- Real-time performance monitoring and analysis: Unifies infrastructure, application, network, and security data for rapid issue resolution.
- Causal and predictive analysis (Davis® AI): Identifies root causes and predicts potential failures based on observed patterns.
- Automated anomaly detection and remediation: Detects anomalies in real time and initiates corrective actions based on defined policies.
- Built-in application security monitoring: The Application Security module identifies vulnerabilities in running code and minimizes risks through real-time analysis.
Conclusion
Dynatrace does not replace enterprise AI agents — it makes their operation more reliable, context-aware, and transparent, providing the foundation they need to function safely and effectively.
At the same time, Dynatrace’s own agent enables automated fault detection, diagnostics, and optimization within IT operations, creating a bridge between observability and autonomous intelligence.
Telvice Zrt. partners with organizations seeking more transparent and efficient IT operations, providing end-to-end support — from planning to implementation. Contact us for a free consultation to explore how AI and observability can work together securely and effectively.
