To innovate quickly and safely with AI, Kavak treats evaluations as a first-class citizen. They invest equal engineering time and money into building robust evals as they do into the agents themselves. These "brakes" enable them to accelerate development while ensuring quality and business alignment, focusing on conversion, not vanity metrics.

While swapping an API endpoint for a new AI model is trivial, the real barrier is the extensive QA and re-benching required. Each new model has qualitatively different outputs, necessitating a full product testing cycle to ensure it doesn't degrade user experience, creating high practical switching costs.

Implementing AI safety guardrails is not cost-prohibitive. The most impactful step, having a second AI model review the primary agent's work, is also the cheapest, accounting for only about 3% of total API costs in the author's experience. This makes it the most efficient first step for improving reliability.

With AI dramatically increasing code velocity, maintenance shifts from stylistic debates to robust verification. Anthropic's approach is to build ~100x more testing infrastructure than is typical, including fixtures from production data and recordings of the AI using the feature it just built.

Unlike traditional software where scaling is about handling more concurrent users, scaling an AI agent is about maintaining accuracy as users explore a near-infinite number of requests. The biggest challenge is preventing quality degradation as the product's functional surface area expands with every new user and use case.

PMs often default to the most powerful, expensive models. However, comprehensive evaluations can prove that a significantly cheaper or smaller model can achieve the desired quality for a specific task, drastically reducing operational costs. The evals provide the confidence to make this trade-off.

OpenAI's effort to create 'SWE-bench-verified' demonstrates the immense cost of quality benchmarks, requiring millions of dollars and multiple human annotators per task. Despite this, a later audit revealed that 59% of the unsolved problems were actually impossible to solve due to inherent flaws.

Unlike traditional software, AI models are not static; providers can deprecate them with minimal notice. This instability means building a robust QA and monitoring framework is not optional—it is a critical, ongoing investment to ensure product quality and reliability.

While many AI agents produce impressive demos, their real-world utility hinges on reliability. Amazon's Nova Act team argues that for production use cases like UI automation, an agent that works only 60% of the time is effectively useless for business. The critical threshold for value is achieving over 90% reliability, making it the core engineering challenge.