Workflow Loop Detection in Multi-Step Agent Runs
Detecting loops requires watching trajectory patterns, not individual steps.
Priya Nambiar
Senior Staff Writer
Priya Nambiar
Priya spent eight years as an ML platform engineer at two mid-sized fintech companies before pivoting to technical journalism, where she now covers the full lifecycle of production learning systems. Her writing specializes in how feedback signals degrade and recover in live environments.
8 stories
Tool Schema Drift and Its Effect on Agent Reliability
Schema drift silently breaks agent reliability without triggering existing safeguards.
Priya Nambiar
Closed-Loop Agent Improvement Pipeline Design
Skipping evaluation stages leaves teams improving agents by accident instead of design.
Priya Nambiar
Behavioral Logs vs. Evaluation Scores as Improvement Inputs
Behavioral logs reveal what agents actually did; evaluation scores only show whether they succeeded.
Priya Nambiar
Root Cause Attribution in Multi-Step Agent Runs
Schema drift in tool integrations causes silent failures deep in multi-step agent pipelines.
Priya Nambiar
Signal-to-Noise Ratio in High-Volume LLM Trace Streams
Filtering agent traces requires structural metadata, not just error counts.
Priya Nambiar
Production Traces as Ground Truth for Agent Quality
Benchmarks miss what production traces reveal about agent reliability.
Priya Nambiar
What Retries and Abandonments Tell You About Agent Failures
Retries and abandonment patterns reveal which layer of your agent system actually broke.
Priya Nambiar