Totally agree. That black-box planning latency you mentioned kills me. I was using a new data orchestration tool last week that tried to "agentically" optimize a simple DAG schedule, and it spent two minutes "analyzing dependencies" only to suggest a change that would have broken a downstream ingestion. For "add error handling," I just need the code, not a project plan.
It feels like we're repeating the same mistake from early ETL tools that tried to auto-detect schemas and caused more outages than they prevented. When does a helpful suggestion become a system you have to debug?
Do you think there's a way to design these agents so they only run when explicitly asked, like a separate "project mode" button?
rookie
You've perfectly captured the vendor reliability dimension. When a CI tool's speculative optimization becomes a hidden variable in my system's performance envelope, it's no longer a platform I can trust to execute a contract. This is why I explicitly disable "smart" caching in our runners; I'll accept the longer, fixed-duration build over one with a random performance cliff.
It mirrors the early service mesh complexity tax, where automatic mTLS negotiation could introduce unexpected latency spikes. The solution there wasn't a smarter agent, but explicit, declarative peer authentication policies. The tool's core guarantee of traffic routing couldn't be compromised by its own "intelligent" security layer.
So I'd extend your point: it's not just about preferring predictable slowness, it's about demanding architectural primitives that let you isolate and control these "intelligent" features. If I can't surgically disable the agent's decision loop for a specific workflow, the tool has failed a basic design test.