I’ve been reading about orchestrating multiple AI agents to handle different parts of a workflow. One agent fetches the data with puppeteer, another cleans it, a third exports it. They work together to handle the whole pipeline.
It sounds good in theory, but I’m wondering if this is real or just marketing. How does coordination actually work? What happens when one agent fails—do the others just hang? How do you debug something like this when it breaks?
I have a project where I need to extract data from multiple sources, validate it, and create a report. Right now it’s all manual scripts and spreadsheets. If multiple agents could actually handle this without constant babysitting, that would change everything.
Has anyone actually tried orchestrating multiple agents on something like this? What was your experience?
This is absolutely real, not marketing. I use autonomous AI teams for exactly this—data extraction, validation, and reporting all in one workflow.
Here’s how it works. You define agents with specific roles. One handles puppeteer-based data extraction, another validates and cleans the data, a third generates reports. They communicate through a workflow, so one agent doesn’t start until the previous one finishes. If something fails, you get clear error messages about which agent broke and why.
The key difference from manual scripts is that agents can adapt. If a data format changes slightly, the validation agent can handle it without you rewriting code. That’s the real power.
Error handling is built in. Failed workflows don’t cascade—they stop and report exactly where they failed.
I set this up for a supplier data pipeline. The puppeteer agent handles extraction, a second agent does data validation against our schema, and a third pushes to our database. Coordination is handled through the workflow system, so each agent waits for input from the previous one. Debugging is easier than I expected because each agent logs what it does.
The orchestration part is simpler than managing multiple manual scripts. Each agent has defined inputs and outputs, so failures are isolated. One agent failing doesn’t cascade to the next unless you explicitly configure it that way. For reporting workflows, this setup cuts down operational overhead significantly because validation and export are automated instead of manual checks.
Yeah it works. Used it for data pipeline. Each agent handles one part. If one fails, workflow stops and tells u where. Way cleaner than writing seperate scripts.