Learn · AI fundamentals
An AI tool does one task when you ask: draft this, summarize that, answer a question. An AI agent chains many steps toward a goal and acts on its own, deciding what to do next and using other tools to get there. Tools assist a person. Agents complete a workflow.
Think of a tool as a very capable assistant that waits for each instruction, and an agent as one you hand an outcome to. "Summarize this call" is a tool. "Watch my inbox, triage support emails, draft replies, and escalate the hard ones" is an agent.
Most businesses start with tools because they're simpler, cheaper, and lower risk, then graduate specific workflows to agents once the value is proven and the guardrails are in place.
Updated July 2026
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An AI agent is software that uses an AI model to pursue a goal across multiple steps on its own: it plans, takes actions using tools (searching, calling an API, updating a record), checks the result, and adjusts. A plain chatbot waits for each instruction. An agent keeps going until the job is done.
Not wholesale, and that's usually the wrong goal. As of 2026, AI reliably takes over repetitive, well-defined tasks and speeds up skilled work, but it still needs people to set direction, judge quality, handle exceptions, and own the outcome. The teams that win use AI to do more, not to shrink.
A large language model (LLM) is an AI system trained on huge amounts of text to predict what words come next. That simple mechanism, scaled up, lets it write, summarize, translate, answer questions, and reason through problems. ChatGPT, Claude, and Gemini are all interfaces to LLMs.
An AI model breaks your input into tokens (word pieces), then predicts the most likely next token, adds it, and repeats, one token at a time, until the answer is complete. Every word it produces is a probability-weighted guess informed by patterns it learned during training.
Tokens are the chunks of text an AI model reads and writes. A token is roughly three-quarters of a word in English, so 1,000 tokens is about 750 words. They matter because AI pricing and limits are measured in tokens, not words or characters.
A context window is the maximum amount of text an AI model can hold in mind at once, counted in tokens. It includes your prompt, any documents you attach, the conversation so far, and the answer being written. Once you exceed it, the model loses track of the earliest content.