Digma’s preemptive observability engine cuts code issues, streamlines AI

Digma, a company offering products designed to act on pre-production observability data, has announced the launch of its preemptive observability analysis (POA) engine. The engine is designed to check, identify, and provide ‘fix’ suggestions, helping to balance systems and reduce issues found in codebases as their complexity increases.

The application of preemptive observability in pre-production may be more important as AI code generators become more common , the company claims. For instance, a 2023 Stanford University study revealed that developers using AI coding assistants were more likely to introduce bugs to their code. Despite this, major companies like Google are increasing their reliance on AI-generated code, with over 25% of the company’s new code being AI-created.

Nir Shafrir, CEO and Co-founder of Digma, commented on the growing resources that are being dedicated to ensuring systems perform well, saying, “We’re seeing a lot of effort invested in assuring optimal system performance, but many issues are still being discovered in complex code bases late in production.”

“Beyond this, scaling has often remained a rough estimation in organisations anticipating growth, and many are hitting barriers in technology growth that arise precisely during periods of significant organisational expansion. It means that engineering teams may spend between 20-40% of their time addressing issues discovered late in production environments, with some organisations spending up to 50% of engineering resources on fixing production problems.”

Preemptive observability is expected to become a key factor helping companies gain competitive advantage. It has several potential benefits for AI-generated code, including speed increases and improvements to the reliability of human-written code. According to Digma, preemptive observability helps ensure manually written code is more trustworthy, and reduces risk in the final product.

As well as tackling bugs introduced by AI code generation, Digma’s preemptive observability analysis engine has been designed to combat common, long-established issues companies may have experienced with human-made code, which may result in service level agreement (SLA) violations and performance issues. For high transactional establishments, like retail, fintech, and e-commerce, this technology could become valuable.

Digma’s algorithm has been designed to use pattern matching and anomaly detection techniques to analyse data and find specific behaviours or issues. It is capable of predicting what an application’s response times and resource usage should be, identifying possible issues before they can cause any noticeable damage. Digma specifically detects the part of the code that is causing an issue by analysing tracing data.

Preemptive observability analysis prevents problems rather than dealing with the aftermath of the issues. Teams can monitor holistically, and address potential issues in areas that are frequently ignored once in production.

Roni Dover, CTO and Co-founder of Digma, highlighted what differentiates Digma’s preemptive observability analysis engine from others: “By understanding runtime behaviour and suggesting fixes for performance issues, scaling problems, and team conflicts, we’re helping enterprises prevent problems and reduce risks proactively rather than putting out fires in production.”

Application performance monitoring (APM) tools are used to identify service issues, monitor production statuses, and highlight SLA errors. APMs are practical for sending alerts when services fail or slow during production. But unlike preemptive observability, APMs are limited in non-production settings, and can’t provide analysis of problems’ sources.

By identifying performance and scaling issues early on in the production process, even when data volumes are low, preemptive observability helps prevent major problems and reduce cloud costs.

Digma recently completed a successful $6 million seed funding round, indicating a growing confidence in the technology.

Image source: “Till Bechtolsheimer’s – Alfa Romeo Giulia Sprint GT No.40 – 2013 Donington Historic Festival” by Motorsport in Pictures is licensed under CC BY-NC-SA 2.0.

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

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