What's the difference between an AI agent and an LLM?
An LLM is the model. An agent is the orchestrated workflow that uses one or more LLMs to accomplish a defined task, with memory, tools, and refusal pathways. Aegis's 25+ modular agents are the latter.
The short answer.
An LLM is the model. An agent is the orchestrated workflow that uses one or more LLMs to accomplish a defined task, with memory, tools, and refusal pathways. Aegis's 25+ modular agents are the latter.
This is a question Aegis hears regularly during discovery. Here is the practical way to frame it.
How Aegis approaches this.
Aegis Boardroom's answer is shaped by three frameworks. Truth Architecture: recommendations are designed to be source-traced. Confidence Contract: recommendations are mapped to the canonical Aegis confidence states (I Know / I Think / I'm Inferring / I Don't Know). Life Integrity Engine: recommendations that may increase irreversible-harm risk are flagged for refusal or human review, not softened.
The fastest path is the AI Readiness Assessment: it returns a confidence-mapped band for your specific situation. From there, the Quick Win Plan or a deeper engagement scopes the right paid Aegis next step.
Frequently asked questions.
Is an AI agent just ChatGPT with a different name?
No. ChatGPT-style tools are LLMs: the model itself. An agent is an orchestrated workflow that uses one or more LLMs to do a defined task, with memory, tools, and refusal pathways.
What can an agent do that a raw LLM can't?
It can carry memory, use tools, and refuse when the evidence isn't there. A raw model just responds; an agent is built to complete a defined task.
Which one does Aegis actually build?
Agents. Aegis's 25-plus modular agents are orchestrated workflows, not bare models.