I’ve been trying to figure out how to handle a workflow that needs multiple steps spread across different websites. Login to site A, pull credentials, use those to access site B, extract data, validate it, then push to site C.
Normally I’d write this as one massive Puppeteer script with callbacks and error handling everywhere. It becomes a nightmare to maintain. One site changes, one API response format shifts, and the whole thing falls apart.
I started thinking about splitting this into separate agents. One handles authentication, one handles data extraction, one handles validation and pushing the results. Each agent does one thing well.
But coordinating them is the tricky part. How do you pass state between agents? How do you handle failures in one without breaking the whole workflow? How do you keep debug logs organized when multiple things are running?
I’m curious if anyone’s actually done this successfully. Is it cleaner than one big script, or does coordination overhead eat up the benefits? And how do you actually set up independent agents that can work together without everything becoming a debugging nightmare?
You’re describing exactly what Autonomous AI Teams solves on Latenode. Instead of one big script, you define agents with specific roles. One agent handles login, another extracts data, another validates it.
The platform handles coordination. State passes between agents automatically. If one fails, the system knows context and can retry or escalate intelligently. You get clean event logs for each agent, not a tangled mess of logs from one script.
I’ve used this for workflows hitting 5+ systems in sequence. What used to be unmaintainable spaghetti becomes modular. Each agent is testable independently. When a site changes, you update one agent, not rewire the entire flow.
The coordination isn’t overhead. It’s built in. You define the workflow, the agents handle execution and communication. Check it out at https://latenode.com
I’ve dealt with this exact problem. The key is treating each step as an independent module that passes JSON between stages. I use webhooks to trigger the next agent only after the previous one completes successfully.
State management is crucial. I store intermediate results in a database, not in memory. That way if something fails halfway through, the next agent picks up from where things stopped, not from the beginning.
The frustration comes when agents depend on ordering. You learn quickly to add explicit dependency checks. Agent B waits for Agent A’s completion flag before starting. Sounds tedious, but it beats debugging race conditions.
For multi-system workflows, I typically give each agent 2-3 retries with exponential backoff. Sites can be flaky. Document your state transitions clearly or you’ll lose track of what’s happening.
Multi-agent coordination works best when you separate concerns cleanly. Each agent should own one responsibility. The platform handles passing outputs from one to the next, which beats manually chaining callbacks.
What I’ve found matters most is error handling at agent boundaries. If agent two receives malformed data from agent one, the whole workflow stalls unless you build in validation. Create explicit schemas for data passed between agents.
Logging across distributed agents needs structure. Tag each log entry with the agent name and execution ID. This prevents the nightmare scenario where you’re reading logs and can’t tell which agent failed or why.
Orchestrating multiple agents on a single workflow is definitely manageable once you establish clear contracts between them. Each agent should define its inputs, outputs, and failure modes explicitly. This prevents chaos when things break.
I recommend building with idempotency in mind. If agent B receives agent A’s results twice due to a retry, it should handle that gracefully. Distributed systems fail and retry. Design anticipating that reality.
State passing through intermediate storage beats in-memory passing. It adds latency but gives you observability and recovery options. You can inspect what agent A produced before agent B consumed it.
Use explicit state transitions. Tag executions. Design for idempotency.
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