LLM agents in the workplace: from chats to real work

How often do you find yourself, when collaborating with a younger or less experienced colleague, that no matter what the task is, you have to do all the work? They don’t have the knowledge of internal processes or experience with the nuances of how things are done. Sometimes it seems that it is just easier to do the actual task than to explain in great detail to the novice what has to be done.

This same hesitation often arises when people first start working with LLM agents. The agent might not “get it” right away — lacking context, misinterpreting intent, or offering overly generic responses. It can feel like talking to an intern on their first day who doesn’t yet speak your team’s language. The fear is that training the AI will take more time and mental energy than simply doing the work yourself. 

There's also a deeper psychological resistance: the uncertainty of relying on a system that seems powerful but unpredictable, especially when the stakes are high or the task is nuanced. That raises worries about losing control, being misunderstood, or about wasting time rephrasing prompts and correcting errors — all of which can feel like the digital equivalent of micromanaging. 

LLM agents do chat, but the smart ones can do real work

Just like with a promising new hire, the investment in onboarding an LLM agent can quickly pay off. Modern agents don't need so much micromanagement and can quickly evolve into proactive collaborators. The effort to teach them becomes a force multiplier rather than a chore.

As AI enters the mainstream, the burden of understanding is shifting. Agents are beginning to understand us on a more profound, psychological level: our goals, sentiments, professional domains, and communication styles. They can learn continuously from our preferences, feedback, and domain-specific language.

Good agents don’t just wait for perfect prompts; they observe patterns, remember context, and adjust over time. After a short learning curve, they start offering real value: drafting messages in your voice, anticipating follow-ups, writing documents, or even reminding you of risks and opportunities you might’ve missed. Advanced agents can even integrate with the software systems and are capable of doing tasks such as creating, reading, and updating contracts or orders.

Collaboration over prompting

Let's face it. People don’t always speak with precision — we imply, gesture, emote, and often speak in shorthand. Is that something that LLM agents will ever overcome? 

Consider Maria, a vegetable products sales rep managing relationships with grocery chains. Instead of crafting precise prompts every time, she interacts with her LLM assistant naturally: "Check if Marco confirmed the soup stock promo — if not, draft a polite nudge and offer some discount." Maria’s agent, whom she calls Ragu, knows the product line, past interactions, key contacts and aligns to her communication style with a nuanced approach for each contact. He writes accurate messages that don't sound odd because he understands the context of Maria’s previous communication with Marco, the grocery chain manager. Ragu will also remind Maria of follow-ups that require her attention. 

Moreover, advanced agents such as Ragu go beyond that. They are well capable of doing the boring but important real work, such as updating the database records to reflect new orders that the grocery store places. Maria's efforts paid off many times over. 

What a valuable peer her unseeming LLM agent has become. Learn about how you can start deploying LLM agents to your workflows today!