Agentic AI
Definition
Agentic AI refers to AI systems that operate autonomously over extended task sequences -- planning actions, invoking tools, observing results, and re-planning until a goal is complete without step-by-step human guidance. Unlike single-turn chatbots, agentic systems can execute workflows that span minutes or hours, touching multiple APIs, databases, and services.
The shift from conversational AI to agentic AI is the defining transition of 2024-2026. A conversational model answers questions. An agentic system executes jobs -- processing a batch of invoices, conducting competitive research, or managing a multi-step approval workflow from start to finish.
Agentic patterns in production
- ReAct (Reason + Act) -- model alternates between reasoning steps and tool calls
- Multi-agent -- specialized sub-agents (researcher, writer, reviewer) coordinated by an orchestrator
- Human-in-the-loop -- agent pauses at defined checkpoints for human approval before irreversible actions
Risk management for agentic systems
Agents that can take actions (send emails, write database records, make API calls) require explicit guardrails: action whitelists, dry-run modes, approval gates for high-stakes actions, and comprehensive logging. Never deploy an agentic system to production without an audit trail.
Related terms
AI Implementation
AI implementation is the end-to-end process of integrating artificial intelligence into a business's existing workflows, systems, and software -- from identifying high-ROI automation opportunities through deploying production-ready AI systems. Done well, it replaces manual, repetitive processes and can reduce operational labor cost by 30-60% within the first year.
LLM (Large Language Model)
A large language model (LLM) is a deep-learning model trained on billions of text tokens to predict and generate human-readable language. LLMs such as GPT-4, Claude, and Gemini power chatbots, document summarization, code generation, and AI workflow automation -- and serve as the reasoning engine inside RAG systems and AI agents.
AI Agent
An AI agent is an LLM-powered system that autonomously plans, selects tools, executes multi-step tasks, and loops until a goal is achieved -- without requiring step-by-step human instruction. AI agents extend a language model''s capability from answering questions to taking actions: writing code, querying APIs, browsing the web, and updating databases.
MCP (Model Context Protocol)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP lets an AI agent call database queries, web searches, file systems, and custom APIs using a single protocol instead of bespoke tool integrations for every data source.
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