Agentic AI
AI systems capable of autonomous decision-making and action-taking to achieve complex goals. Unlike reactive AI that responds to inputs, agentic AI can plan multi-step workflows, make contextual decisions, and adapt behavior based on outcomes.
What Defines Agentic AI
Agentic AI represents a fundamental shift in artificial intelligence capabilities. Rather than simply processing inputs and returning outputs, agentic AI systems can set goals, create plans, take actions, and learn from results—all with minimal human intervention.
In customer service, this means AI that doesn't just answer questions but actively works to resolve complex issues: gathering information from multiple sources, making decisions based on business rules, executing actions like processing refunds, and adjusting its approach when initial attempts don't succeed.
Key Characteristics of Agentic Systems
- Goal-oriented behavior: Can work toward complex objectives across multiple interactions
- Autonomous planning: Creates multi-step action plans without explicit programming for each scenario
- Adaptive execution: Adjusts strategy based on outcomes and changing contexts
- Tool use: Can invoke external systems, APIs, and workflows to accomplish tasks
Agentic AI in Customer Service
Modern customer service platforms like Fin use agentic AI to handle end-to-end query resolution. When a customer asks to modify an order, the system autonomously: verifies the order details, checks modification eligibility, presents options, processes the customer's choice, updates backend systems, and confirms completion—all while maintaining natural conversation and handling exceptions gracefully.