What is an
AI agent?
An AI agent is a language model given tools and goals so it can take action, read your data, draft, send, update systems, not just answer in a chat box. With a digital brain behind it, it acts with your real context.
Agents do the work,
not just the talking.
A chatbot tells you what to do. An agent does it, triages the inbox, drafts the follow-up in your voice, flags the at-risk account. The leap from "answers" to "actions" is what makes AI feel like a coworker instead of a search box.
The catch is reliability: an ungrounded agent guesses. Wire it to your digital brain and give it clear tools and limits, and it becomes dependable. That's exactly what powers Sentinel, and what we build with you in a Sprint, connected through an MCP connector.
An agent with a
memory to consult.
An agent is only as good as what it can consult. The knowledge base is the broader memory it reads from, built on purpose to give the agent the right context instead of whatever it happens to guess. That is what stops it drifting: tools like deep-research assistants hallucinate exactly when they have nothing solid to ground on.
A small real example from my own setup: before a meeting, an agent checks my digital brain for what I know about the person, so I walk in prepared. I have built the same kind of agents for client work, including one for a legal team.
Model, tools,
goal, memory.
Strip an agent down and it's four things working together: a model to reason, tools it's allowed to use (send an email, update a record, run a search), a goal that defines what "done" looks like, and a memory it can consult, your digital brain. Take away the memory and you're left with a confident guesser. Add it, and the agent acts with your real context instead of a plausible average.
That's also the difference between a demo and something you'd trust with real work. The model is the easy part now; the memory and the limits are what make an agent dependable.
Connected,
not copy-pasted.
An agent reaches your data and tools through connectors. The cleanest path today is an MCP connector: it brings your digital brain straight into Claude (or ChatGPT, or your own agent) so the model can query it live, the same way I've wired my own brain into Claude. For taking action out in your stack, agents commonly run through Zapier or similar, so a single instruction can read the brain, draft, and push the result into the tools you already use.
None of this is exotic. It's plumbing: clear tools, a grounded memory, and limits on what the agent may do. Get those three right and the agent is reliable enough to hand real work to; get them wrong and it's a liability. Most of the craft is in the boundaries, not the model.
Before the
meeting.
Here's one of mine, start to finish. A calendar event fires. An agent reads my digital brain for everything I know about the person and their company, the last conversation, what they care about, what we agreed. It drafts a short prep note and the opening of a follow-up in my voice. I walk in ready; afterwards, what was said is captured back into the brain for next time. No prompt-wrangling, no searching five apps, the loop runs on its own.
Built once, that pattern repeats across a business: triage the inbox, flag the at-risk account, draft the proposal from your own playbook. It's the same shape behind client agents like Sentinel and the one I built for a legal team, and it's what we build with you, and hand you to own, in a Sprint.
Common questions.
Agent vs chatbot, what's the difference?
A chatbot answers; an agent is given tools and a goal, so it takes actions and decides the steps to get there.
Won't it hallucinate?
That's the risk without grounding. Wiring it to a digital brain and clear tools/limits is how you make it reliable enough to trust.
Build an agent that
actually works.
Bring the task you'd hand to a coworker. We'll build the agent, wired to your brain, together, and you'll own it.