XIV · Anti-PatternsAnti-pattern✕

Automating a Broken Process

also known as Agentifying Dysfunction, Automation Without Redesign

Anti-pattern: deploy agents on top of a workflow that is already dysfunctional, so the dysfunction is amplified at machine speed instead of resolved.

Context

An organisation identifies a slow, error-prone, or under-staffed business process and decides to bring in agents to handle it. The reasoning is throughput: if humans struggle with the process, agents will move faster and cheaper. The decision skips the prior step of asking whether the process itself is well-designed.

Problem

If the underlying process has unclear handoffs, ambiguous decision rules, undocumented exceptions, or contradictory policies, the agent inherits all of those defects and executes them at machine speed and scale. Errors that a human would catch by hesitation or by asking a colleague are now produced in seconds, sometimes faster than downstream systems can absorb. The team measures cycle-time reduction and declares success, while error rate, rework, and customer escalations climb. Both Nordic sources name the same shape independently: techsy.io warns that 'an agent will automate a broken process faster but will not fix it', and HiQ frames the maturity-stage skip ('precision, speed, scalability') as efficiency-first agent adoption on top of broken workflows.

Forces

  • Agents promise throughput; redesigning a process promises only delay.
  • Stakeholders see automation as a substitute for the harder organisational work of clarifying rules and ownership.
  • Cycle-time metrics improve immediately even when error rate and rework climb in the background.

Example

A claims-processing team deploys an agent to triage incoming claims, hoping to cut a four-day backlog. Cycle time drops from four days to forty minutes within a week, and leadership celebrates. Two months later, the customer-complaints team is drowning: the agent has been routing edge-case claims to the wrong queue, because the routing rules were never documented and the agent learned them from inconsistent historical examples. Postmortem: the human process tolerated the ambiguity by hesitation and informal escalation. The agent did not hesitate. The fix is a process-redesign pass — explicit routing rules, named exception path, documented handoffs — before re-enabling automation.

Diagram

Solution

Therefore:

Don't agentify dysfunction. Run a process-redesign pass first — name the handoffs, document the decision rules, surface the exceptions. Then decide what shape of automation fits: a linear deterministic flow may fit Zapier or workflow tooling; only genuinely judgment-bearing steps warrant an agent. See demo-to-production-cliff for the operational gates that catch dysfunction-amplification once an agent is live, and rigor-relocation for where review discipline should land when humans step out of the inner loop.

What this pattern forbids. No useful constraint; the missing constraint is a mandatory process-redesign pass before agent deployment.

And the patterns that stand alongside it, or against it —

  • complementsDemo-to-Production Cliff✕— Anti-pattern: ship a demo-validated agent straight into production without a frozen eval, cost ceiling, loop-detector, or named oncall, then act surprised when accuracy drops and cost runs away.
  • complementsAgentic Debt✕— Anti-pattern: deploy agents on top of an unconsolidated data foundation, weak governance, or missing MLOps infrastructure, so every subsequent capability — observability, retraining, compliance retrofit — pays compounding interest on the skipped foundational work.
  • complementsPerma-Beta✕— Anti-pattern: ship the agent in 'beta' indefinitely so that quality regressions are someone else's problem.
  • alternative-toRigor Relocation★— Relocate verification rigor from the model loop to surrounding scaffolding (evals, judges, decision logs, policy gates) so failures are caught by the wrapper rather than the agent.
  • complementsDemo-Production Cliff (Multi-Agent)✕— Anti-pattern: multi-agent pilot benchmarks at 95% accuracy / 2s latency on a curated demo set, then degrades to ~80% / 40s under realistic 10k-RPD load.
  • complementsHidden Validation-Work Amplification✕— Anti-pattern: an agent rollout shifts effort from doing the work to validating, monitoring, and recalibrating the agent — net productivity is negative because the hidden human evaluation burden exceeds the visible automation gain.
  • complementsMulti-Agent on Sequential Workloads✕— Anti-pattern: split a fundamentally sequential workload across multiple agents, degrading accuracy by 39–70% with no parallelization benefit.

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References

Provenance