Six months ago, Claude could chat. It could write code, explain concepts, draft emails. But when the conversation ended, nothing happened. The AI lived inside a text box.
Today, Claude can read your GitHub repo, check open issues, write code, open a pull request, and message your team on Slack — in a single conversation. Not because Claude got smarter. Because someone built the tools that let it act.
That shift — from chatbot to agent — is the most important change happening in AI right now. And the developers building the tools that make it possible are in an incredibly valuable position.
Three eras of AI development
Era 1: Chat (2022–2023)
You type a prompt. The model responds. You copy-paste the output somewhere useful. The AI has no context beyond what you paste into the conversation. Every interaction starts from zero.
Era 2: RAG and retrieval (2023–2024)
Systems start feeding documents into the model's context. Vector databases, embeddings, retrieval pipelines. The AI can "know" things about your data — but it still can't do anything with that knowledge. It reads, but it doesn't write.
Era 3: Agentic (2024–now)
The model can call functions. It can read data, make decisions, and take actions. It can chain multiple steps together. It's not just answering questions — it's completing tasks.
We're in Era 3. The models are capable enough. The protocols exist. The infrastructure is being built right now. The bottleneck isn't intelligence — it's tooling.
What "agentic" actually means
An AI agent isn't a new kind of model. It's a regular language model with three additions:
Tools — functions the model can call (check weather, query a database, send an email)
A decision loop — the model looks at the result of each tool call and decides what to do next
Context — the model maintains state across multiple steps
Here's what that looks like in practice.
You say to Claude: "How does AAPL look today?"
Without tools, Claude says: "I don't have access to real-time stock data, but as of my last update..."
With tools, Claude:
- Calls
get_stock_quote("AAPL")— gets today's price - Calls
get_stock_financials("AAPL")— gets P/E ratio, margins, revenue - Calls
get_stock_news("AAPL")— gets recent headlines - Synthesizes everything into a clear analysis
Same model. Completely different capability. The difference is the tools.
The tool layer is where value accrues
Here's the thing most people miss: the model layer is commoditizing. GPT-4, Claude, Gemini, Llama — they're all getting better, faster, cheaper. You can't build a lasting advantage on which model you use.
But the tool layer? That's where the value is.
When you build a tool that connects Claude to a stock API, you're not just writing a function. You're creating a capability that didn't exist before. Every developer who installs your tool gives Claude (or any MCP-compatible agent) a new superpower.
Think about it like smartphone apps. The iPhone was powerful, but the App Store is what made it indispensable. The models are the phone. The tools are the apps. We're in the App Store moment for AI agents.
Why this matters for you
If you're a developer, you already have the skills to build these tools. They're TypeScript functions with a specific interface. The hard part isn't the code. It's understanding what to build and how to ship it.
The developers who move now have a real advantage:
The ecosystem is early. There are entire verticals with zero MCP tool coverage. DevOps, healthcare, legal, finance, education — wide open.
The protocol is stabilizing. MCP (Model Context Protocol) is emerging as the standard. Tools you build today will work tomorrow.
Distribution is built in. Publish an MCP tool to npm and every Claude Desktop user can install it with one line. No app store approval. No marketing budget required.
This isn't theoretical. In the next post, I'll show you exactly what MCP is — with working code.
This is Part 1 of a free 3-part series on MCP.
I'm building a full course — **Build & Ship MCP Tools* — that takes you from your first tool to a published npm package to a paid product. 7 modules, 33 lessons.*
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