Safe failures: verifiable agent framework for web data

Data collection on the open web faces challenges such as broken selectors, inconsistent schemas, and fragile dependencies. To overcome these limitations, verifiable agent frameworks propose a paradigm shift: instead of generating free code —prone to silent failures— a typed JSON configuration is defined that restricts the agent's actions to a predefined set of collectors, templates, and utilities. This approach, similar to what we implement in process automation, ensures that each step is verifiable and repeatable. By running on static DAGs (like Airflow) and applying structural quality rules, the need to invoke large language models at runtime is eliminated, reducing costs and latency. Verification not only detects schema errors or missing values but also feeds structured corrections back to the agent, allowing initial success rates to improve over time without sacrificing determinism. This idea of 'safe failure' —where the system always produces a validatable output— is critical for enterprise applications that require reliable AI for businesses. At Q2BSTUDIO, we apply these principles to custom application development and custom software, integrating AI agents capable of adapting to heterogeneous sources without losing traceability. Additionally, we support the deployment of these architectures on AWS and Azure cloud services, ensuring scalability and resilience. The combination of artificial intelligence with explicit verification rules allows building data pipelines that not only extract but also validate, correcting errors before they reach business intelligence systems like Power BI. Thus, a deterministic and low-cost process is achieved, ideal for repetitive open data capture tasks. This framework, which we have implemented in multiple projects, demonstrates that it is possible to move towards more responsible automation, where each failure is an opportunity for improvement and not an unknown.