Scripts and macros follow fixed steps every time with no variation. AI agents understand context, make judgment calls, and adapt their approach based on what they find. Scripts are rigid; agents are flexible and intelligent.
When an AI takes action, it goes beyond generating text and actually does something in your business systems — publishing a post, sending an email, updating a database, or scheduling an event through connected tools.
Yes. Building an AI agent today means writing clear instructions in plain English, not writing code. If you can explain a task step by step to a new hire, you can create an agent skill that handles that task automatically.
Every AI agent has four core components: a language model (the brain), tools (connections to your software), instructions (what to do), and memory (context from previous steps). Together, these let the agent understand, decide, and act.
A sub-agent is a specialist AI agent that gets called by a parent agent to handle a specific part of a larger task. It focuses on one job — like writing an email or analyzing a transcript — then returns its result to the parent.
An orchestration agent is a manager agent that coordinates other agents. Instead of doing tasks itself, it delegates work to specialist agents, passes data between them, and ensures the full workflow completes in the right order.
A tool-using AI agent is an AI that connects to external software to take real actions. Instead of just generating text, it can send emails through your CRM, publish posts to WordPress, check your calendar, and update databases.
An agent loop is the cycle an AI agent repeats: observe the situation, think about what to do, take an action, then check the result. It keeps looping until the task is complete, adjusting its approach at each step.
Agentic means the AI has agency — the ability to take independent action, make contextual decisions, and use tools to complete tasks. When AI is agentic, it goes beyond generating text to actually doing work in your systems.
Yes, within the boundaries you set. An AI agent reads data, evaluates conditions, and chooses what to do next — like skipping an irrelevant step or adjusting its output based on context. But it only operates within the scope you define.