Agentic AI
Definition
AI systems that can autonomously plan, reason, and take actions to accomplish complex goals with minimal human intervention.
Use Cases
- Klarna: Customer support assistant that handles user questions and resolves common issues without a human agent for many conversations. — Klarna deployed an AI assistant built on large language models and integrated it with internal knowledge sources and support workflows so it can answer questions, guide users through steps, and complete support-related actions when appropriate. (Klarna reported that the assistant handled a large share of customer service chats and reduced resolution time for many inquiries, improving support efficiency.)
- GitHub: Developer assistance that helps with multi-step coding tasks such as explaining code, generating changes, and suggesting fixes during development. — GitHub Copilot integrates LLM-based assistance into IDEs and developer workflows, using context from the codebase and developer prompts to propose code and guide iterative changes. (GitHub has reported productivity improvements for developers using Copilot, including faster completion of common coding tasks.)
- Duolingo: AI-powered tutoring experiences that adapt to learners and provide interactive practice. — Duolingo introduced AI-driven features that use large language models to create interactive learning experiences and personalized feedback within the app. (Duolingo has described improved engagement and expanded learning experiences through these AI features.)
Provider Equivalents
- AWS: Amazon Bedrock Agents
- Azure: Azure AI Agent Service
- GCP: Vertex AI Agent Builder
- OCI: OCI Generative AI Agents
Frequently Asked Questions
- What's the difference between Agentic AI and a chatbot?
- A chatbot mainly responds to messages in a conversation. Agentic AI goes further: it can plan a sequence of steps, call tools (like search, calendars, ticketing systems, or databases), and take actions to complete a goal—often across multiple systems—while keeping track of progress and constraints.
- When should I use Agentic AI?
- Use Agentic AI when tasks are multi-step, require decisions along the way, and involve taking actions in tools or systems (for example: triaging IT tickets, onboarding employees, generating and running reports, or planning and booking travel). If the task is simple Q&A with no actions, a standard RAG-based assistant or chatbot is usually safer and cheaper.
- How much does Agentic AI cost?
- Costs typically come from (1) LLM usage (input/output tokens), (2) tool calls (API requests, database queries, web search), (3) orchestration/agent runtime charges (if the platform bills for agent execution), and (4) supporting infrastructure (vector databases, logging, monitoring). Agentic workflows can cost more than basic chat because they often run multiple model calls per user request and may invoke several tools before finishing.
Category: ai-ml
Difficulty: advanced
Related Terms
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