Agentic AI: Frequently Asked Questions

Agentic AI builds on generative AI by enabling software agents to act with autonomy. IDC defines it as systems that can make decisions, adapt to new environments, and complete complex tasks with minimal human input. These systems reason, learn from experience, and interact with their environment—often coordinating across multiple agents—to continuously optimize outcomes.

Generative AI refers to AI systems trained to produce new content—like text, audio, video, or images—based on patterns learned from existing data. According to IDC, it involves unsupervised and semi-supervised learning techniques using deep neural networks. These systems are capable of replicating patterns and generating content that mimics what they’ve seen during training, with applications across content creation, customer service, gaming, and more.

Generative AI generates content. Agentic AI uses that content-generation capability to accomplish broader tasks with autonomy. While generative AI might write a paragraph or generate an image, agentic AI could perceive a problem, plan a multi-step response, generate required assets, execute actions, and adjust its plan based on results. It’s goal-oriented, dynamic, and capable of learning over time.

NVIDIA defines agentic AI with a four-phase framework: Perceive, Reason, Act, Learn.

• Perceive: Gather and interpret data from the environment or system.
• Reason: Use LLMs and possibly Retrieval Augmented Generation (RAG) to plan responses.
• Act: Integrate with external tools and software to execute actions.
• Learn: Use feedback loops to improve future decisions through reinforcement learning.

• IT operations: Autonomous issue resolution—from triaging alerts to root cause analysis and remediation.
• Proactive customer service: Detecting problems and offering solutions before customers notice.
• Software engineering: Writing, testing, and refining code based on natural language prompts.
• Automated procurement: Managing purchases, pricing, and compliance without manual intervention.
• Supply chain optimization: Real-time adjustments across logistics, inventory, and vendors based on live data.

Adoption is still early. Many organizations hesitate due to trust and transparency concerns, as agentic AI relies on probabilistic models and often operates in unpredictable environments. Legacy monitoring tools typically lack the visibility needed to observe and verify agentic workflows. Dynatrace addresses this by delivering AI-powered observability across every layer of these systems, helping teams understand, validate, and improve autonomous decisions.