The phrase "AI agent" has officially hit the mainstream. Every SaaS company is slapping it on their marketing pages. Investors are throwing money at anything with "agentic" in the pitch deck. And most teams are left wondering: is this actually different from the chatbot we've been ignoring since 2023?
It is. But the difference isn't what most people think.
First, Let's Kill Some Confusion
The terms "chatbot," "assistant," "copilot," and "agent" get used interchangeably. They shouldn't. They describe different things, and the distinctions matter when you're deciding what to actually deploy.
Chatbots are scripted. You ask a question that matches a pattern, you get a pre-written answer. The customer support widget that asks you to "describe your issue" and then routes you to an FAQ article? That's a chatbot. It doesn't understand anything. It matches keywords.
Assistants are responsive. Think early ChatGPT or Google's Gemini. You give them a prompt, they give you a thoughtful answer. They can draft emails, summarize documents, answer research questions. But they wait for you. Every interaction starts from scratch unless you manually provide context. They're powerful notepads, not coworkers.
Copilots are in-context. GitHub Copilot is the best-known example. It sits inside your development environment, sees what you're writing, and suggests the next line of code. It's reactive and context-aware within a single tool. Big improvement. But it still only acts when you're actively working — it doesn't go off and do things on its own.
Agents are autonomous. An AI agent takes actions, retains memory across interactions, understands the context of your team's work, and operates without someone hovering over it. You don't prompt it every time. You give it a goal or a trigger, and it figures out the steps.
The gap between a copilot and an agent is the gap between someone who answers your questions and someone who does the work.
What Makes an AI Agent an Agent
There's no ISO standard for this (thankfully), but after building in this space, we think four things separate real AI agents for work from everything else:
They take actions, not just produce text. An agent doesn't write a task description for you to copy-paste. It creates the task in your project board, assigns it, sets a due date. It posts the status update to your team channel directly. The output is work done, not text generated.
They have memory. You told the agent last week that the Q2 launch got pushed to April. It remembers. It factors that into future decisions without you re-explaining. This is surprisingly rare in practice. Most "AI agents" on the market today have the memory of a goldfish.
They understand shared context. This is the big one for teams. A useful agent doesn't just know what you told it. It knows what your teammate discussed in a different conversation, which tasks are blocked, what decisions were made in yesterday's thread. It works from the team's collective knowledge, not one person's prompt history.
They can work without constant prompting. You don't have to stand over them. An agent monitors a project channel for decisions that need task follow-ups. It notices a deadline approaching and flags it before you ask. It drafts a meeting summary the moment the conversation wraps. The whole point: it does things you'd otherwise forget or deprioritize.
If your "AI agent" requires you to write a detailed prompt every single time you want something done, it's an assistant with better branding.
What AI Agents in the Workplace Actually Do
Enough theory. Here are four workflows where agentic AI at work is producing real results right now.
Turning Conversations Into Tasks
Your team has a long discussion in a project channel. Decisions get made. Action items get mentioned. And then... nobody creates the tasks. A week later, everyone has a different memory of who's responsible for what.
An AI agent follows the conversation in real time, picks up on commitments and action items, and creates structured tasks with assignees, due dates, and links back to the original discussion. The context stays intact. Nothing slips.
Proactive Status Updates
Every Monday, someone spends 30 minutes pinging people for updates so they can compile a status report. It's tedious for everyone involved.
An agent pulls together what tasks moved, what's blocked, what shipped last week, and posts a summary without anyone being asked. People review and correct instead of writing from scratch. The Monday ping ritual just... goes away.
Document Drafting With Project Context
Writing a proposal or spec usually means switching between your task board, old documents, chat history, and a blank page. An agent that lives inside your workspace already has that context. It generates a first draft that references actual project decisions, task statuses, and team conversations instead of generic placeholder text.
You still edit and refine. But you start at 60% instead of 0%.
Onboarding Assistance
New team members ask the same ten questions. Where's the brand guide? What's the deploy process? Who owns the billing module? Instead of interrupting teammates or digging through old Slack threads, they ask the agent. It has access to the team's documentation, past conversations, and project history.
Not a static wiki. A coworker who has actually read everything and can answer follow-ups.
What This Looks Like in Practice: Sammy
We built Sammy as Trilo's AI coworker. Not a sidebar chat widget. An actual member of your workspace.
Sammy sits in your team's channels. It has access to your tasks, documents, and conversations. It can create tasks, update documents, search project history, run multi-step workflows. It has the same context your human teammates have, because it lives where the work happens.
The design decision we keep coming back to: Sammy isn't a personal assistant that each person configures separately. It's a shared team resource. When one person teaches Sammy something about the project, everyone benefits. That's the difference between personal AI agents and team AI workspaces, and it's why context compounds over time instead of staying siloed.
Sammy isn't the only example of autonomous AI agents in team software. The category is growing fast. But the line between useful and gimmick comes down to the same things we listed above: does it have actual access to team context? Can it take real actions? Does it remember what happened yesterday?
The Honest Limitations
We'd love to tell you agents are magic. They're not.
Agents still make mistakes. They misinterpret context, create duplicate tasks, assign things to the wrong person, or summarize a conversation and miss the most important point. This is improving fast, but it's not solved. If someone tells you their AI agent has a 99% accuracy rate, ask them how they measured it.
Guardrails matter more than capabilities. The most useful agent isn't the one that can do the most. It's the one that knows when to stop and ask. Good agents flag uncertainty instead of guessing. They let you review before taking irreversible actions. Confidently doing the wrong thing at scale is worse than doing nothing.
They work best in specific workflows. Task creation from conversations? Great use case. Nuanced strategic planning? Not there yet. The teams getting real value from agentic AI at work are the ones picking repetitive, context-rich workflows and pointing agents at those. Not the ones trying to replace half their headcount.
Adoption is a team effort. An AI agent is only as good as the context it can access. If your team does half its communication in DMs and half its task tracking in spreadsheets, the agent is working with scraps. The teams that benefit most centralize their work in one place. (Yes, this is what Trilo is built for. We're biased, but we're also right.)
Where this is going
We think AI agents for work become standard team infrastructure within two years. Not the hype-cycle kind of "standard" where everyone talks about it and nobody uses it. The boring kind, where it's just how teams operate, the same way Slack and Google Docs are just... there.
The companies figuring this out now will have a real head start. Not because the technology gets harder to adopt later, but because the context and habits compound. An agent that's been part of your team for six months has six months of project history, decisions, and patterns to draw from. That's not something you can shortcut.
The question isn't whether your team will work alongside AI agents. It's whether you'll have built up that context when everyone else is starting cold.
Trilo is a workspace where your team works alongside AI coworkers — with shared context, real-time collaboration, and structured workflows. Try it out or learn more about our AI coworkers.
