Eval Metric Selection for Multi-Step Agent Task Quality
Measure agent traces at the layer where they break, not at the final response.
Priya Subramaniam
Senior Staff Writer
Priya Subramaniam
Priya spent eight years as a reliability engineer at a mid-sized fintech before moving into technical journalism, where she now covers the systems and signals behind AI agent breakdowns. Her writing focuses on diagnosing root causes in production environments, with a particular emphasis on observable failure patterns.
7 stories
Trace-Driven Debugging vs Log-Based Debugging for LLM Agents
Traces preserve the causal chain that flat logs miss entirely.
Priya Subramaniam
Prompt Injection Detection in Agentic Pipelines
Prompt injection succeeds because agents can't separate instructions from retrieved data.
Priya Subramaniam
Distinguishing Model Errors From Harness Errors in Agent Traces
Attribution determines whether to fix the model or its surrounding systems.
Priya Subramaniam
Categorizing Agent Failure Modes Across Harness Layers
Model failures usually stem from the harness scaffolding, not the AI itself.
Priya Subramaniam
Workflow Loop Detection in Production Agents
Fingerprints in execution traces reveal why production agents get stuck in loops.
Priya Subramaniam
Memory Retrieval Failures in Long-Horizon Agents
Four pipeline layers, four distinct failures—misdiagnosis is why fixes don't work.
Priya Subramaniam