Agentic Chat — When AI Does the Work

This is Article 2 in our series "3 Levels of Working with AI." Article 1 covered Chat — the art of conversation with AI. Now we explore what happens when conversation becomes creation.

There's a moment that happens to everyone who transitions from advisory AI to agentic AI.

You're talking to Claude or ChatGPT, discussing how to build something. You describe what you want. The AI responds with helpful advice, code snippets, step-by-step instructions. You copy the code, paste it into your editor, run it. It works. Or it doesn't. You go back to the chat. You iterate.

Then someone shows you Claude Code, or Cursor, or GitHub Copilot's agent mode.

"Watch this," they say. They describe what they want to build. The AI doesn't just respond with advice. It opens files. It searches the codebase. It writes code directly into multiple files. It runs tests. It catches errors and fixes them. It commits changes to git.

The conversation didn't produce advice. It produced the actual product.

That's the shift from chat to agentic chat. From "tell me how" to "make it happen."

The Harness of Tools

The difference isn't the AI model. It's the harness.

When you chat with Claude on claude.ai, you're talking to the same underlying model as Claude Code. The difference? Claude Code has tools:

• File search — Find relevant files in your codebase
• Read — Open and analyze file contents
• Write — Create new files or overwrite existing ones
• Edit — Make surgical changes to specific lines
• Bash — Execute commands, run tests, use git
• Grep — Search for patterns across files

These tools transform the AI from advisor to executor. Instead of telling you what to do, it does the work.

The same pattern exists across platforms:

The tools are what make it agentic. Give AI a hammer, and it can build. Keep it in a chat window, and it can only describe construction.

From Advisory to Execution

Here's what the shift looks like in practice.

Advisory mode (traditional chat):

You: "I need to build a landing page for a SaaS product with a hero section, feature grid, pricing table, and footer."

AI: "Here's a sample HTML structure you can use... provides code snippet. For the styling, you'll want to use Tailwind classes like... more code. For the pricing component, consider this approach... another snippet."

You: Copies code, creates files, pastes snippets, debugs issues, goes back to chat for clarification, repeats

Execution mode (agentic chat):

You: "I need to build a landing page for a SaaS product with a hero section, feature grid, pricing table, and footer."

AI: "I'll create a landing page for you. Let me start by setting up the structure..."

[Creates index.html]
[Creates styles.css]
[Writes hero section HTML]
[Implements feature grid]
[Builds pricing component]
[Adds footer]
[Runs local dev server to test]
[Reviews output, identifies spacing issue]
[Fixes padding in hero section]
[Commits changes to git]

AI: "Done. The landing page is live at localhost:3000. I adjusted the hero section spacing to better match modern SaaS aesthetics. Want me to add anything else?"

The input is the same. The output is fundamentally different. One produces instructions. The other produces the product.

The Real Power: The Feedback Loop

But here's what nobody tells you about agentic chat until you experience it yourself.

You don't actually know what you want until you see it.

This isn't a bug in your thinking. It's how humans learn. We're terrible at specification, excellent at evaluation. We can't perfectly describe what we want, but we know it when we see it.

In advisory mode, this creates friction. You describe what you want. AI gives you code. You implement it. You look at it. You realize it's not quite right. You go back to the chat. You try to articulate what's wrong. You get more code. You iterate. The loop is slow because the implementation step is manual.

In agentic mode, the loop is tight. The AI builds. You review. You say "that, but with more space here and bolder headlines." The AI immediately updates it. You see the change. "Better, but now the pricing section feels unbalanced." The AI adjusts. Three minutes later, you have something you couldn't have specified at the beginning.

The conversation isn't just producing the product. It's revealing what the product should be.

This is the unlock. You're not trying to get the specification perfect before building. You're building to discover the specification.

Let me show you what this looks like with a real example.

Building Through Discovery

Last week, I asked Claude Code to build a simple dashboard for tracking content performance. Here's how the conversation actually went:

Me: "I need a dashboard showing our top 10 blog posts by traffic."

Claude builds a basic table with post titles and pageview counts

Me: "Good start. Can you add trend indicators? Like whether traffic is up or down from last week?"

Claude adds arrows and percentage changes

Me: "Nice. Actually, can you make those percentages color-coded? Green for growth, red for decline."

Claude adds conditional coloring

Me: "Hmm, the table feels cramped. Can you space it out and make the titles clickable links to the actual posts?"

Claude adjusts spacing and adds links

Me: "You know what would be useful? Seeing the traffic source breakdown. Like how much is from Google vs. social vs. direct."

Claude adds a column with mini bar charts showing source distribution

Me: "That's perfect. Actually, one more thing — can we add a date range selector so I can compare different time periods?"

Claude implements date picker and updates the data fetching logic

Result: A dashboard I couldn't have specified at the beginning. I discovered what I needed through iteration.

Time elapsed: 12 minutes from first message to final product.

If I'd tried to write a complete specification upfront, I'd still be writing it. Or worse, I'd have specified the wrong thing and built it perfectly.

This is the superpower of agentic chat. Not just that AI can execute. But that execution is fast enough to support discovery.

The Infinite Process

Here's something that might sound like a problem but is actually a feature: You'll never finish.

Every time you review what the AI built, you'll think of improvements. Make the headlines bigger. Add animation. Change the color scheme. Add a search feature. Reorganize the layout. Integrate with an API. Add authentication.

In traditional development, this feels exhausting. Each new idea means more implementation work. More time. More effort. The backlog grows faster than you can clear it.

In agentic chat, this feels natural. Because implementation isn't the bottleneck anymore. Ideas flow directly into execution.

You think: "What if we added a dark mode toggle?"

You say: "Add a dark mode toggle."

Thirty seconds later, it exists.

You think: "The mobile layout could be better."

You say: "Improve the mobile responsiveness."

It's done.

The process becomes infinite because the cost of iteration approaches zero.

This isn't a bug. This is how creative work actually happens. You build, you evaluate, you refine. The difference is speed. In traditional development, the loop takes hours or days. In agentic chat, it takes seconds or minutes.

The product never feels "done" because you keep discovering what it could be. But that's not chaos — that's craftsmanship enabled by speed.

What This Actually Feels Like

Let me be honest about what working this way is actually like, because the marketing materials make it sound perfect.

It's not perfect.

The AI will misunderstand you sometimes. It will make changes you didn't want. It will break things that were working. It will hallucinate file paths or function names that don't exist. You'll need to correct it.

But here's what surprised me: correction is faster than creation.

When Claude Code writes a function with a bug, I can say "fix the error on line 47" and it's corrected immediately. When it misunderstands what I wanted, I can say "actually, put the button on the left side" and it adjusts. The feedback loop is so tight that mistakes are cheap.

Compare that to traditional development where I write code, run it, see an error, find the bug, fix it, test again. Or worse, where I implement the wrong thing and have to rewrite it.

With agentic chat, course correction costs seconds. With manual coding, it costs minutes or hours.

This changes the psychology of building. Instead of trying to be perfect upfront (which slows you down), you can be fast and iterative. Build, review, refine. Build, review, refine.

The speed of iteration matters more than the accuracy of the first attempt.

When to Use Agentic Chat

Agentic chat isn't the right tool for everything. Here's when it shines:

Use agentic chat when:

• Building new things from scratch (websites, tools, documents, presentations)
• Making changes across multiple files
• Exploring what you want through iteration
• Learning new technologies or frameworks
• Prototyping ideas quickly
• Repetitive tasks that require file manipulation

Don't use agentic chat when:

• You need deep, surgical precision in complex systems
• The stakes are high and mistakes are expensive (financial systems, medical devices)
• You're working in heavily regulated environments without approval processes
• The task is primarily strategic thinking, not execution
• You need human judgment more than execution speed

The pattern: agentic chat excels at creation and iteration. It struggles with high-stakes precision and strategic judgment.

For most knowledge work — building prototypes, creating content, assembling reports, exploring ideas — it's transformative.

The Bridge to Autonomous Agents

Agentic chat sits between two worlds.

In Article 1, we talked about chat as the foundation — how conversation lets you work with AI through natural language. Agentic chat takes that conversation and gives it execution power. You're still in the conversation, but now the conversation produces real work.

In Article 3, we'll explore autonomous agents — where AI operates without continuous human supervision. Where you define goals and AI figures out how to achieve them over hours, days, or weeks.

Agentic chat is the bridge. It teaches you how to work with AI that takes action. It shows you what happens when conversation becomes creation. It prepares you for the final shift: from conversation to delegation.

The conversation still matters. You're still there, guiding, evaluating, course-correcting. But the work happens at AI speed.

That's the unlock. Not just advice. Execution. Not just planning. Building.

When you give AI tools, conversation becomes creation.

Try Agentic Chat Yourself

Ready to experience the shift from advisory to execution?

Start with these platforms:

• Claude Code — Terminal-based agentic coding (free tier available)
• Cursor — IDE with built-in agentic capabilities
• TeamDay — Agentic AI for business users (multi-agent orchestration)

Part of the "3 Levels of Working with AI" series: Chat | Agentic Chat | Autonomous Agents