By Sandeep Bali · Founder
A copilot answers questions and drafts work using your own documents through retrieval-augmented generation (RAG) — it suggests, a human decides. An agent goes a step further and takes actions across systems to complete a task with limited supervision. Copilots reduce effort on knowledge work; agents reduce the number of steps a human has to perform at all.
// Key takeaways
A raw language model knows a great deal about the world in general and nothing about your business in particular. Ask it about your refund policy or last quarter's numbers and it will either decline or, worse, invent a plausible answer. For real work you need the model grounded in your facts.
That grounding is what the next two ideas — RAG and agents — provide, in increasing order of capability and risk.
Retrieval-augmented generation, or RAG, is the standard way to make an AI answer from your content. When a question comes in, the system first retrieves the most relevant passages from your documents — policies, manuals, tickets, contracts — and hands them to the model alongside the question.
The result is an answer based on your material, with citations you can check, rather than the model's general training. RAG is the single most important technique for making AI trustworthy inside a business, and it is the foundation under almost every useful copilot.
A copilot is an assistant that sits beside a person doing knowledge work. It answers questions from your documents, drafts replies, summarises long threads and surfaces the right information at the right moment — but the human reviews, edits and decides.
Because a person stays in the loop, copilots are low-risk and quick to adopt. They are the right first step for most organisations: real productivity gains without handing control to a machine.
An agent goes further than answering. Given a goal, it plans the steps, calls tools and systems, and carries out a task — booking, filing, updating records, moving work between systems — with limited human supervision.
Agents are powerful precisely because they remove steps a human would otherwise do by hand. They also carry more risk, because a mistake becomes an action rather than a suggestion. That is why they belong on workflows you already understand well, with clear boundaries and the ability to review what they did.
If the bottleneck is people spending time finding answers, writing drafts or reading long documents, you want a copilot. It is cheaper, faster to ship and lower-risk, and it proves value quickly.
If the bottleneck is the sheer number of repetitive steps in a well-defined process, an agent may be worth it — but earn your way there. The right sequence is almost always copilot first, agent second, once the workflow is trusted and the guardrails are clear.
// faq