This article was originally published on LinkedIn.

You hear a lot these days about "AI agents" and "chatbots" - sometimes interchangeably, sometimes as if one is strictly superior to the other. But that framing misses the nuances and the real potential of agents, even when they aren't wrapped in a chat interface.

What exactly is a "bot" (chatbot)?

A chatbot is fundamentally a conversational interface: ask a question, it returns an answer (or prompts follow-up).

• It's reactive by nature: the user drives the interaction by asking, and the bot responds.
• It may be connected to backend systems (like ERP, CRM, etc.), but its scope is often limited to queries, lookups, or guided dialogs.

It's also worth remembering: a bot is only as good as the data it can reach and the functionality it can tap into. If the data is incomplete, outdated, or siloed — or if the bot isn't connected to meaningful actions — it can feel shallow and frustrating.

Chatbots are extremely useful for self-service, answering frequently asked questions, guiding users through decision trees, or surfacing data. But by themselves, they don't usually act autonomously.

What is an "agent"?

An AI agent is meant to do more than respond — it acts.

In a recent post, I described how AI in NetSuite is evolving from analysis to action:

"Agents don't just highlight anomalies or forecast trends; they take the next step — reconciling accounts, sending notifications, or drafting corrective actions."

Some attributes of agents (versus mere automations or bots):

• Proactive and autonomous: not just waiting for user prompts — they observe, decide, and act.
• Goal-oriented: designed to achieve outcomes, not just execute a fixed script.
• Context-aware: capable of monitoring state and adjusting as conditions change.
• Adaptive: can learn from prior cycles and refine how they behave over time.
• Action-driven: go beyond queries or guided flows; they perform real work and trigger downstream effects.

Agents don't require a chat layer to be valuable. Even without conversation, they can drive complexity, reduce manual toil, and provide measurable impact.

Why combining bots + agents makes sense — but don't undervalue agents by themselves

Yes, combining an agent's actions with a conversational layer is powerful. It gives users a "talk to me, and I will also act" experience.

But there are plenty of scenarios where agents alone are transformative:

• A reconciliation agent that daily monitors mismatches and posts draft journal entries
• A supply-chain monitoring agent that triggers restocking orders
• A collections agent that escalates based on smart rules, without direct user queries

In these cases, the agent's value is in doing — reducing manual effort, closing loops faster, catching anomalies earlier. The chat interface is a companion, not a prerequisite.

Moving toward autonomy — responsibly

Because agents act, not just answer, it's essential to build trust gradually:

• Auditability & explainability: every action should be traceable
• Human-in-the-middle (HITM): early stages often need approval workflows
• Boundaries & thresholds: agents should know when to escalate rather than act

Over time, as data and trust build, autonomy can safely increase — and that's when AI agents become truly game-changing.

The bottom line

Chatbots and agents each bring value, and together they can create powerful, human-friendly AI experiences.

But don't mistake conversation for capability. The real leap forward comes from agents that can act — safely, transparently, and increasingly on their own. As we move toward more autonomous systems, agents will take on meaningful work, free people from repetitive tasks, and give organizations new levels of speed and insight.

The chat interface is just one way to interact with that intelligence — not the source of its power.