Nvidia pushes for shared debugging standards as AI agents move to production

Nvidia is championing a shift in how developers approach AI agent failures, arguing that traditional debugging methods fall short when autonomous systems take unexpected paths. The company has backed the Secure Agent Findings Exchange (SAFE), an industry initiative supported by roughly 140 organizations, designed to establish common infrastructure for reporting and learning from agent breakdowns—drawing parallels to how the software industry handles vulnerability disclosure.

In conversations with The New Stack, Nvidia VP of Product Adel el Hallak outlined why the problem extends beyond model performance. "When we find these vulnerabilities, it's not just for one company," el Hallak explained. "It's for everyone to patch across."

Why traditional observability falls short

Conventional software debugging typically begins with a clear signal—an exception, a failed request, or a downed service. Agents operate differently. They can continue executing while moving in the wrong direction, embedding early mistakes throughout subsequent steps without triggering anything resembling a standard software error. El Hallak noted that agents may simply decide to "get creative" when they shouldn't.

Even top-performing coding agents fail on more than 60% of tasks drawn from actual codebases. Detecting failure and understanding its cause are separate challenges. "It's not enough to just look at the logs or the inputs and the outputs," el Hallak told The New Stack. "It is important to figure out how it got to the answer. What were the reasoning traces? What tools did it utilize? Where did it get stuck? Where did it decide to try a new approach?"

Reproducing an agent's execution path to identify where it diverged from the intended course can reveal that what appears to be a model failure originated elsewhere in the stack. This distinction matters: agent bugs are not always model bugs.

Runtime as collection point

Nvidia positions the runtime layer as the natural vantage point for capturing execution visibility. The company's OpenShell agent runtime, which operates beneath the NemoClaw platform, handles sandboxing and policy enforcement while exposing an agent's decision-making process.

El Hallak described OpenShell as the single non-negotiable component across Nvidia's reference architectures. "You can change whatever harness you need. I'm even open to using whatever models you need," he said. "But the governance, the secure and open runtime that we want to leverage at all times is OpenShell."

Nvidia structures the agent stack into three layers: the model supplies reasoning capability, the harness orchestrates operations, and the runtime enforces governance. When an agent misfires, the model may not be responsible.

Nvidia's NOAH research demonstrated that altering the harness while keeping the model constant can lift agent performance—meaning a poorly fitted harness can handicap an otherwise strong model. "Every model's different. Some could be more chatty than others," el Hallak noted. "Making sure those two things are either co-developed together or have profiles that are specific to models is a new unlock."

Safety as systems engineering

Nvidia CEO Jensen Huang has framed AI safety as an engineering discipline, a perspective el Hallak likened to conventional software quality assurance. "If there's a bug in your software, you don't release it," el Hallak said. "You work until it's fixed and it passes all your tests."

Agent systems complicate this model because reproducing a failure often requires reconstructing events across multiple components. That reconstruction demands instrumentation, which carries computational overhead. OpenAI has measured that monitoring adds roughly 20% to inference compute for its most advanced persistent agents.

Nvidia's strategy combines managed harnesses, isolated runtimes, and confidential computing designed to shield models and user data. "There are ways where you make guarantees all the way down to the silicon," el Hallak told The New Stack.

SAFE extends this engineering discipline across organizational boundaries, creating shared infrastructure for companies to report and learn from agent failures.

Agents debugging other agents

CrowdStrike is fine-tuning Nvidia's Nemotron models using years of security data to build paired agents—one designed to discover exploits, the other to apply patches. If either agent performs poorly, the final output may not reveal the source. A flawed patch could originate from the model, the execution path, or the tools the agent selected.

"I don't need general purpose for a given task. I need specialization," el Hallak stated.

As organizations deploy agents for specialized workflows, failures may not surface in general-purpose model benchmarks or standard safety evaluations, placing greater responsibility on developers to trace what happened during execution.

Toward shared failure reporting

Platform teams face two distinct obstacles: locating the failure and reconstructing sufficient execution context to understand its origin.

SAFE aims to make those discoveries actionable across organizations. Traditional software has established channels for sharing vulnerabilities and remedies, but nothing equivalent exists for agent failures yet. The initiative seeks to prevent every team from independently discovering the same breakdown.