Your AI agent failed. The model might not be the problem.
As AI agents move into production, the path between a request and its result is becoming less predictable. An agent The post Your AI agent failed. The model might not be the problem. appeared first on The New Stack .
AI agents are becoming more complex and unpredictable, leading to failures that are harder to diagnose. Nvidia's VP of Product Adel el Hallak emphasizes the need for greater visibility into agents as they take on more intricate tasks. An industry effort, the Secure Agent Findings Exchange (SAFE), aims to share learnings from agent failures across 140 companies.
However, failures may not always trace back to a single component, making it challenging to pinpoint the root cause of an issue. Unlike traditional software, where failures are often tied to an exception or service outage, AI agents can continue running even when going in the wrong direction, carrying earlier mistakes through the task without producing a conventional failure.
Debugging an agent failure requires more than just examining logs or inputs and outputs; developers must delve into the reasoning traces, tools utilized, and points where the agent got stuck or switched approaches. Nvidia's OpenShell agent runtime, which provides visibility into agent execution, is seen as a crucial point to capture this information.
Nvidia's stack for agents consists of three layers: the model, the harness, and the runtime. The model provides intelligence, the harness orchestrates work, and the runtime governs execution. When an agent fails, the model itself may not be the issue. Nvidia's research shows that changing the harness while keeping the model fixed can improve performance, highlighting the importance of a well-matched harness.
Traditional software safety practices, such as reproducing failures and passing all tests, are applied to AI agents, but the complexity of reproducing failures in agents requires more intricate instrumentation. Nvidia's approach includes governed harnesses, sandboxed runtimes, and confidential computing to protect models and user data.
SAFE extends this safety engineering to a shared infrastructure, enabling organizations to learn from each other's agent failures.
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