Common use cases for Agentic AI systems are scenarios where tasks require multiple steps, external data access, and decision-making over time. One common example is automation and operations. An agent can monitor system metrics, detect anomalies, retrieve related runbooks or historical incidents from a vector database such as Milvus or Zilliz Cloud, and suggest or execute remediation steps. This goes beyond alerting and into guided or autonomous response.
Another major use case is research and analysis workflows. Agentic AI can break down research questions, gather information from multiple sources, compare findings, and produce structured reports. For instance, an agent tasked with “analyze user feedback trends” can retrieve clustered feedback embeddings, identify recurring themes, and summarize actionable insights. This is much harder to achieve with a single prompt-based chatbot interaction.
Agentic AI is also used in software engineering tools, such as code maintenance, dependency upgrades, and documentation generation. An agent can scan repositories, identify outdated dependencies, check changelogs, and propose upgrades step by step. These use cases benefit from autonomy, memory, and the ability to reason about progress rather than just generating text.