Priya Subramaniam

Test agent changes against real production failures before shipping to catch cascading errors.

Freezing upstream state isolates which agent step caused the failure.

LLMs lose reasoning ability before filling their context window, requiring intentional curation.

Framework choice determines whether your production agents survive failure or collapse under load.

Architectural flaws, not reasoning gaps, cause agent workflows to collapse into failed branches.

When agents claim success without finishing the task, standard monitoring misses it completely.

Concurrent agents hit provider quotas faster than teams realize, masking the real problem.

Slow tool calls trigger cascading reasoning failures that standard monitoring can't detect.

Agent systems amplify retry costs because LLMs resubmit full context and spawn shadow retry loops.

Agents silently adapt to broken schemas where typed clients would crash.

Models silently truncate context and ship confident wrong answers.