The first time someone asked why a downstream action had already been executed before anyone reviewed the data behind it, I assumed there was a misunderstanding. That kind of thing usually traces back to timing issues, partial data, or an edge case where a late update slipped past a window. None of those explanations felt particularly alarming, and all of them had reasonable fixes. What made this different was that nothing appeared broken. Pipelines finished on schedule. Ingestion lag was within expected bounds. Jobs that had been stable for months continued to run without errors. There was no obvious place where something had “gone wrong.” That was precisely what made the question uncomfortable. If the system behaved correctly and still produced an outcome that surprised the people relying on it, then the problem was not failure. It was behaviour. Verifying the Obvious Before Trusting Anything Else I started where I always do, by validating the basics. I checked event timestamps, partition completeness, watermark progression, and late-arriving data handling. I walked through the transformations step by step, verifying filters, joins, and aggregations against the documented logic. Everything held up. At that point, it would have been easy to stop. Engineers are trained to move on once the data checks out. But the question that triggered this investigation was not about correctness. It was about the sequence. Something had happened earlier than expected, not incorrectly. So instead of tracing the data backwards, I followed the workflow forward, starting from the signal that initiated the downstream action and observing how it moved through the system in real time. That was when the shape of the problem started to emerge. Following the Execution Path Instead of the Dataset The execution path was surprisingly direct. A batch is completed, a condition is evaluated to true, and the next step is fired immediately. There were no intermediate holds, no approval stages, and no checkpoints designed to surface context before execution. The system did not pause because it had no reason to pause. What stood out was not any single step, but the absence of gaps between steps. At some point in the past, we had removed manual checkpoints to improve throughput. Then we shortened timeouts to reduce idle wait. Then we adjusted retry behaviour to keep workflows moving under partial failure. Each change made sense in isolation and improved operational metrics. Together, they created a straight-through execution path where evaluation flowed directly into action. The system had not suddenly become autonomous. It had gradually stopped waiting. This was not obvious from reading individual DAGs or configuration files. You had to trace the entire path end to end to see how little room remained for human review. Old Thresholds, New Consequences The thresholds that triggered the action were not new. They had been reviewed, debated, and approved months earlier. At the time, they controlled recommendations that were explicitly meant to be reviewed by someone before anything happened downstream. What had changed was not the numbers, but their role. As I dug through older documentation, the language made this clear in hindsight. Outputs were described as “informing decisions” or “supporting planning.” None of that language reflected how the system behaved now. The same thresholds that once guided decisions were now directly initiating them. No one had explicitly signed off on that shift. It happened gradually as execution paths were optimised and manual steps were removed. By the time anyone noticed, the system had already been operating that way for a while. That gap between intent and behaviour was the core issue, and it was not something you could catch by validating schemas or checking metrics. Where Responsibility Became Diffuse When questions came up about ownership, responsibility was scattered across teams. Engineering could point to approved configurations and stable pipelines. Operations could point to healthy systems and predictable runtimes. Business users could point to outcomes they had not seen coming. None of those perspectives was wrong. The problem was that no one owned the moment when the system crossed from analysis into execution. There was no single service labelled “decision engine,” and no obvious place to insert accountability. As I started looking for similar patterns elsewhere, I realised this was not an isolated case. Alerts escalated automatically instead of flagging context. Workflows reprioritised tasks based on thresholds without surfacing rationale. Checks blocked execution outright instead of exposing uncertainty. Each of these behaviours saved time and reduced manual effort. Each also removed an opportunity for someone to ask whether the system’s assumptions still held. Realising This Was Not a Technical Bug What made this difficult to address was that there was no bug to fix. The system was reliable, performant, and correct by every traditional measure. The issue was that it had crossed a boundary without anyone formally acknowledging it. We still talked about the platform as if it were neutral infrastructure, moving data from one place to another. In practice, it had become an execution layer that acted on data faster than humans could reasonably review it. That realisation forced me to rethink how I approached data engineering. Correctness and availability were no longer sufficient criteria. When systems act without waiting, defaults, ordering, and retry semantics become behavioural decisions, whether we label them that way or not. What I Took Away from This I did not set out to build a system that made decisions. Neither did anyone else involved. The platform evolved that way through a series of optimisations that prioritised speed and reliability, and none of those changes felt risky at the time. What I learned is that modern data platforms do not suddenly become decision systems. They drift into that role as friction is removed. Manual steps are eliminated. Reviews feel redundant. Execution paths are streamlined. By the time the shift becomes visible, the system has already internalised it. Since then, I no longer treat pipelines as neutral plumbing. I think about where they wait, where they do not, and what assumptions are embedded in those choices. If a system no longer pauses, then it needs explicit ownership and guardrails, not just better monitoring. The risk is not automation. The risk is pretending a platform is still reporting long after it has started acting.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

Forecast Outputs; Triggering Actions: Debugging a Silent Workflow Escalation

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

When 125 Million Item-Locations Lied To Us: What Retail Forecasting Taught Me About Data Truth

A Guide on The Future of ETL: EL(T) not ELT

A Quick Introduction to Machine Learning with Dagster

Array And Set Operations: What You Need to Know

Building a Serverless Data Pipeline with AWS S3 Lamba and DynamoDB

Build Resilient Data Pipelines by Empowering Non-Technical Teams to Detect and Resolve Bad Data

When 125 Million Item-Locations Lied To Us: What Retail Forecasting Taught Me About Data Truth

A Guide on The Future of ETL: EL(T) not ELT

A Quick Introduction to Machine Learning with Dagster

Array And Set Operations: What You Need to Know

Building a Serverless Data Pipeline with AWS S3 Lamba and DynamoDB

Build Resilient Data Pipelines by Empowering Non-Technical Teams to Detect and Resolve Bad Data

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps