Agentic AI
Agentic AI describes systems that pursue multi-step goals autonomously, taking actions and adapting to results rather than responding turn-by-turn.
Agentic AI describes software systems that pursue multi-step goals autonomously, planning sequences of actions, executing them through tools and APIs, observing outcomes, and adapting. The distinction from conversational AI is autonomy of action, not just understanding of language.
In context
Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029. The 2025-2026 reality is more constrained, Forrester's April 2026 analysis documents "zero appetite" for fully autonomous deployments in production today.
In customer service, agentic AI is the architectural pattern behind modern auto-resolution. Where a chatbot answers a question, an agentic system can: retrieve relevant policy, check the customer's account record, execute an action (refund, update, credit), and notify the customer, all without a script predefining each step.
The category is also where the failure modes concentrate. MIT's August 2025 research found 95% of generative AI pilots (many of them agentic) fail to deliver measurable P&L impact. The pattern across the 5% that survive: outcome-contracted, vendor-owned optimization, production-scope pilots.
How Auralis uses Agentic AI
Auralis Autopilot is an agentic-AI system: it plans the resolution path for each ticket, retrieves the relevant knowledge, executes the required action, and escalates only when confidence is below threshold. The optimization loop runs weekly to tune the thresholds.