Agentic AI
A paradigm of artificial intelligence where autonomous agents can reason, make decisions, and execute complex multi-step workflows without constant human intervention.
What is Agentic AI?
Agentic AI represents a fundamental shift from traditional automation and even standard AI applications. While conventional automation follows predetermined scripts and rules, agentic AI systems possess the ability to understand context, adapt to changing situations, handle exceptions, and make intelligent decisions based on goals rather than rigid instructions.
The term "agentic" comes from the concept of agency—the capacity to act independently and make choices. Agentic AI agents can break down complex tasks into subtasks, determine the best approach to achieve objectives, interact with multiple systems, and learn from outcomes to improve future performance.
Key Characteristics
Autonomous Decision-Making
Can evaluate situations, weigh options, and make decisions without requiring human approval for every action—while still operating within defined boundaries and escalating when necessary.
Contextual Understanding
Processes natural language, understands business context, interprets user intent, and adapts behavior based on situational factors rather than following rigid rules.
Multi-Step Workflow Execution
Plans and executes complex sequences of actions across multiple systems—like reading an email, extracting data, updating a CRM, generating a report, and sending a notification—all as a coordinated workflow.
Learning and Adaptation
Improves performance over time by learning from interactions, user feedback, and outcomes—becoming more accurate and efficient as it processes more tasks.
Agentic AI vs. Traditional Automation
| Capability | Traditional Automation | Agentic AI |
|---|---|---|
| Decision Making | Follows predetermined rules | Makes context-aware decisions |
| Exception Handling | Fails when unexpected occurs | Adapts and handles exceptions |
| Task Complexity | Simple, repetitive tasks | Complex, multi-step workflows |
| Learning | Static, requires reprogramming | Learns and improves over time |
| Natural Language | Limited or none | Understands and generates language |
Enterprise Use Cases
- Customer Support: Agents that understand customer queries, pull relevant data from multiple systems, resolve issues, and escalate complex cases to humans
- Document Processing: Intelligent extraction and routing of information from invoices, contracts, and forms across various formats and structures
- Sales Automation: Agents that qualify leads, schedule meetings, update CRM records, and generate personalized follow-up communications
- Data Analysis: Autonomous analysis of business metrics with automatic report generation and insight identification
- Workflow Coordination: Managing complex approval processes, cross-functional workflows, and system integrations without manual intervention
Related Terms
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