What Agentic AI means in practice
Agentic AI is a design pattern where the LLM is the brain of a loop rather than the producer of a single output. The model decides which tool to call, observes the result, decides the next step, and continues until the goal is achieved or an exit condition is hit. The typical enterprise agentic workflow has 3 to 12 steps, blending LLM reasoning with deterministic tool calls (database lookups, API calls, RPA actions, document parsing). The hard engineering question is rarely "can we build an agent" but "can we build an agent that fails safely, logs every decision, and stays bounded inside an audit trail the regulator accepts". MindMap's Agentic Workflow Studio is the platform we use to ship this pattern in production.
Related terms
Multi-agent orchestration →
An architecture where multiple specialised LLM agents coordinate on a task, typically with a planner agent decomposing the work to executor agents.
Tool use (function calling) →
An LLM capability to emit a structured call to an external function or API based on the user's request, then continue the conversation with the function's result.
ReAct (Reason + Act) →
An agent design pattern where the LLM alternates between explicit reasoning steps ("thought") and tool-using action steps ("action"), looping until the goal is met.
Guardrails →
Runtime checks that intercept LLM inputs and outputs to enforce policy — blocking PII leakage, prompt-injection attempts, off-topic queries, unsafe responses.
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All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.