Stop Overengineering AI Agent Pipelines
I originally wrote this observation in Finnish on LinkedIn, because I was frustrated after seeing yet another celebrated architecture diagram that chained together eight external AI services with an orchestration layer on top. The post resonated, so here is the argument in English.
The AI industry right now is aggressively rewarding complexity. Scroll through any tech community and you will find posts celebrating incredibly complex agent workflows where five, eight, twelve external AI services each handle a slice of the problem, with yet another service – usually something like n8n or Make – whose entire job is to coordinate the others into one coherent workflow. The architecture diagrams look impressive. They make great conference slides. But they hide a fundamental engineering problem that anyone who has run distributed systems already knows.
Your chain is only as strong as its weakest link. When your workflow depends on six external services, the availability of your entire system becomes the product of all six availabilities. If each service delivers 99.5% uptime, your pipeline is at 97%. Add a few more and you are looking at outages every week. One service having a bad day means your whole pipeline falls over. And in the AI space, where services are young and APIs change constantly, “a bad day” happens more often than anyone wants to admit.
Then there is the debugging nightmare. When something goes wrong in a twelve-step agent pipeline – and things always go wrong – figuring out where the failure occurred, what data was malformed, and which service introduced the error becomes an exercise in distributed systems archaeology. You end up spending more time instrumenting and debugging the orchestration layer than the actual business logic. Here is the part that really gets me: many of these complex workflows, when you actually trace the data flow, are fully linear from input to output. No branching, no state management, no conditional logic. They could be collapsed into a single well-designed prompt. The complexity is not solving a hard problem. It is signaling sophistication.
What I have found building production AI systems is that fewer moving parts with more thought invested in getting each interaction right consistently outperforms the complex alternative. This does not mean avoiding external services entirely. It means being ruthless about which dependencies actually earn their place. Every external call should justify itself not just in capability but in reliability cost. If you can achieve 90% of the result with a single well-prompted model call instead of an orchestrated chain of five services, take the simpler path every time. Boring, reliable, and simple beats clever, fragile, and impressive – every single time.
