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Most people think building AI agents is the hard part.
They’re overthinking it.
The real bottleneck is data access.
You can have the best prompts in the world.
But if your agent can’t talk to your systems, it’s stuck.
Teams lose weeks wiring “just one more” API.
Then they do it again next sprint.
That integration tax is why most agent demos die in production.
I noticed something interesting from Agoda’s open-source APIAgent.
You point it at a REST or GraphQL API.
It reads the schema automatically.
Then it turns that API into an MCP toolset.
No extra glue code.
No new deployment.
⚡ It can even pull large responses and filter locally with DuckDB.
That matters when an API returns thousands of rows.
You don’t want to “prompt” your way through that.
The part that feels like a cheat code is recipe learning.
When a tool sequence works, it saves the steps.
Next time, it runs faster.
Less trial and error.
And there’s a guardrail many teams forget.
It blocks POST, PUT, and DELETE by default.
So you start read-only.
That’s how you ship safely.
↓ If you’re building agents, steal this approach.
↳ Start with read-only tools.
↳ Let schemas drive tool creation.
↳ Move heavy filtering close to the data.
↳ Save successful workflows as reusable recipes.
What’s the biggest thing slowing your agents down today: tool wiring, data quality, or risk controls?
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