Agent reliability

Agent reliability is the degree to which an agent's behavior in production stays consistent with what it was built to do, across the inputs it actually receives and over time as everything around it changes. It's a property of the whole system: behavior is a function of the model, the prompts, the tools the agent can call, and the data it reads, and a change in any of these shifts it. Because agents are nondeterministic, the same input can produce different outputs on different runs, so reliability is a statement about the distribution of behavior across many runs.

It's a problem because agents ship fast and change often. Teams update them to add functionality, improve quality, or cut cost, and the underlying models change on their own schedules, sometimes without any deploy on the team's side. Every change is a chance for behavior to shift, and the agents themselves grow more complex as models and use cases expand.

Degradation is also quiet. A degraded agent usually keeps producing fluent text and completed tool calls, so a wrong decision looks the same on the surface as a right one. Quality declines tend to surface late, often through users. The more an agent is trusted with, the more a quiet decline costs before anyone knows it's happening.