Inside WorkBuddy: How AI Agents Really Get Work Done

The Connector: Giving the Agent Hands and Feet

Start with the simplest piece. A large language model, left alone, can't touch anything. It can't read your Tencent Docs, see your inbox, or create a meeting link. To make an agent useful, you have to connect it to the systems you already use. That's the connector.

WorkBuddy's connector page lists options like QQ Mail, Tencent Docs, TAPD, and Tencent Meeting. When you add one, you're really doing three things: loading the plugin that knows how to call that service's API, authorizing the agent to act on your behalf, and giving the model a description of the tool so it knows when to reach for it.

For a user, it feels like one click and a QR code scan. But under the hood, the agent now has a tool card in its prompt that says, "I can create a meeting." If you ask for a meeting at 3 p.m. tomorrow with the topic "weekly report review," it will create that meeting using your account and hand back the details.

One practical note: don't check every tool for every conversation. Each tool description gets stuffed into the system prompt. The model decides which tool to call by matching your words against those descriptions. Too many tools, and the model can get confused about which one to pick.

Skill: Encapsulating How to Do a Thing

If the connector is the hands, the skill is the method. A skill bundles a workflow, a script, or even an API call into a repeatable task. Some skills can execute on their own; others depend on a connector being authorized first.

Say you want a weekly meeting review. A skill might contain six steps: list this week's meetings from Tencent Meeting, create a Tencent Doc to record them, pull each meeting's transcript, summarize each one into a 500-word brief with action items, log those summaries back into the doc, and finally generate a week-in-review message for you.

That's a complete method. It needs the Tencent Meeting and Tencent Docs connectors already connected and authorized. The skill says what to do; the connector says what systems I can touch.

Expert and Expert Team: Who Is Doing the Work

People often mix up skills and experts because both seem to make the agent smarter. But a skill describes how to do a task. An expert defines who is doing it—what role, what perspective, what professional approach.

Think of it as a hierarchy. The connector sits at the bottom, dealing with data access. Skills cover the middle layers: how to process information. Experts cover the higher layers: judgment, methodology, and identity. The top layer, the final decision, stays with a human or an organization.

When a task is big enough to need multiple perspectives, you bring in an expert team. Instead of forcing one agent to pretend to be a product manager, a designer, and an engineer at once, WorkBuddy spins up a team. A team lead breaks the task into pieces, assigns each to a specialist, and then merges the results into a single deliverable.

Inspiration: The Copy-What-Works Gallery

This one trips people up. Inspiration isn't a capability you load. It's a gallery of finished examples—someone else's best practice, already built and working.

Suppose you see a "product pricing comparison" page in the inspiration feed. You click "make one like this." WorkBuddy loads the prompt, the skill, and the expert configuration that produced that page. You swap in your own content, and you're done.

This is a clever design. A new user doesn't care about which skill or connector to install. They care about getting a market analysis report, and they'd rather copy a good one than learn the plumbing. Inspiration turns technical capability into a result. It's a template market, a case-study library, and a shortcut all at once.

How the Agent Actually Works

Strip away the product names, and you see what's happening. When you talk to an agent, the flow is: your input goes in, the agent assembles a prompt that includes your request plus all the context—what tools are available, what skills are loaded, what expert persona is active—and sends that to the model. The model replies, and the agent executes any actions and returns the result.

Most of WorkBuddy's features are about that middle step: assembling the prompt. The connector says, "Here are the tools I can use and the data I can access." The skill says, "Here's the method for this type of task." The expert says, "Here's who I am and how I should approach this." Inspiration is just a package that bundles all of those together for a specific scenario.

A Real Workflow, End to End

Let's walk through a complete example. You just left a client meeting about an AI project. Your goal is to turn that conversation into a concrete project plan.

First, you need the client's historical documents and notes from previous meetings. That's the connector—making sure the agent can read your Tencent Docs.

Second, you need to clean up the meeting transcript: strip out the filler, extract the client's goals, current problems, confirmed items, open questions, and next steps, then save that somewhere useful. That's a skill—a step-by-step method for processing the raw material.

Third, you want to figure out what the client actually needs, not just what they said. You bring in a solution consultant expert. It applies a consulting methodology to separate the stated requirement from the underlying business goal, and to flag which parts are ready for AI and which aren't.

Fourth, you need to build the full proposal—business analysis, product design, technical architecture, implementation roadmap. That's when you call in the expert team. The team lead breaks the work into pieces, assigns each to a specialist, and merges everything into a single deliverable.

Finally, you could save this whole process as a "client meeting to AI project plan" template. That becomes an inspiration. Next time, someone clicks "make one like this," swaps in their own meeting notes, and gets the same structured output.

What This Teaches Us About Agent Products

WorkBuddy is an attempt to translate engineering concepts into business language. API, OAuth, MCP? Those become connectors. Workflow, prompt engineering, tool calling? Those become skills. System prompts, domain knowledge, methodology? Those become experts. Multi-agent orchestration and task execution? That's an expert team.

And prompt templates, skill configs, and demo cases? Those become inspiration—because nobody wants to read a config file. They want to see someone's finished work and copy it.

That's the right instinct. Most users don't need to know the plumbing. They need to say, "I want a market analysis," and let the agent figure out the rest.

But if you're trying to understand agents, this is a great window. The underlying problems don't change: how to connect to data, how to encode a method, how to define a role, how to coordinate multiple roles, and how to package it all for reuse. Once you've seen one product organize those pieces, you'll recognize the pattern in the next one.

So go ahead and copy an inspiration. Then tweak a skill. Add a connector. Build an expert. And when the task gets too big for one persona, pull in the team. That's how you move from using an agent to understanding it.