Agentic AI: Autonomous Workflows & Multi-Agent Systems (2026)

An iterative process where an AI model plans, executes a step, reviews the result, and corrects itself until a final goal is reached.

An AI system designed to achieve a goal by independently choosing which tools to use and which actions to take without constant human input.

A framework where multiple specialized AI agents work together, often in different roles (e.g., a "Coder" agent and a "Reviewer" agent), to solve complex tasks.

The central logic or "manager" that coordinates different AI agents, ensuring they communicate and hand off tasks effectively.

The ability of an AI to break a high-level goal (e.g., "Research and book a 5-day trip") into a logical sequence of sub-tasks.

The capability of an AI to interact with external software, like a web search, a calculator, or a company’s API, to get real-world work done.

Trust, Risk, and Security Management; the framework used to ensure AI agents are ethical, safe, and compliant with regulations in 2026.

A safety requirement where a human must review or approve an AI agent’s decision before it executes a high-risk action (like spending money).

A technique where an agent critiques its own previous output and restarts the task if it finds an error or an incomplete answer.

Short-Term: The context of the current conversation. Long-Term: The agent's ability to store and retrieve info from past interactions using a database.

A sophisticated version of Retrieval-Augmented Generation where the agent decides which documents to search and how many times to search them.

One of the earliest examples of an autonomous agent that could string together multiple prompts to complete a complex objective.

The process an agent uses to split one large, vague user prompt into smaller, executable instructions it can handle one by one.

A phenomenon where an autonomous agent slowly moves away from its original goal or rules over a long, multi-step task.

The level of authority given to an AI agent to make decisions or change its environment (e.g., deleting a file or sending an email).

A training method where an AI learns from its past failures by comparing what it did with what it should have done.

An agent's ability to figure out how to use a brand-new tool it has never seen before, just by reading the tool's documentation.

An agent in a Multi-Agent system that handles a specific, narrow task (like data scraping) and reports back to a "Manager Agent."

The ability of a user to provide mid-task guidance to an agent to change its direction without starting the whole process over.

When multiple AI agents find a more efficient way to work together that was not explicitly programmed into their original orchestration.

A secure, isolated digital environment where an AI agent can test code or actions without risking the security of the main network.