Defined term
Agentic AI
AI systems that can plan, take multi-step actions, and use tools to complete tasks autonomously.
Agentic AI refers to systems that decompose a goal into sub-tasks, call tools (APIs, databases, code execution), inspect intermediate results, and iterate toward a completed outcome — with limited or no human intervention between steps. Agentic systems sit on top of LLMs but are shaped by orchestration logic, memory, and tool definitions. In production, agentic workflows are bounded by guardrails, evaluation harnesses, and human review queues for high-impact actions.
When it matters
When the work requires multiple decisions chained together (research → draft → validate → act). Below 3 steps, a single LLM call is cheaper and safer; above 3, agentic patterns earn their complexity cost.
Real example
An outbound sales agent that pulls account signals from HubSpot, drafts personalized outreach, validates against do-not-contact lists, schedules send via the email API, and logs touches back to CRM — all in a single goal-oriented loop with reviewer escalation on low-confidence cases.
KPIs to watch
Tool-call success rate (>95% target), end-to-end task completion (>80%), reviewer escalation rate (10-15% optimal).
Related terms
Autonomous agent
An AI agent that completes a defined task without per-step human input.
Tool use
An LLM's ability to call external functions, APIs, or services within a generation step.
ReAct
A reasoning pattern where the model alternates Thought → Action → Observation steps.
RAG (Retrieval-Augmented Generation)
Generation grounded in retrieved source documents rather than the model's parametric memory alone.
See it in action
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