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  • Writer's picture誉 段

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With the rise of the cloud, we are witnessing a major transformation in how software is developed and maintained. As a developer, microservice architecture is quite appealing. It enables us to decouple singular monolithic software into a smaller set of services, making it possible for small teams of developers to rapidly iterate and release new features faster. However, from a systems perspective, the tradeoff for velocity has been operational complexity. We have shifted from local complexity to distributed system level complexity. We have added various abstractions like infra-as-code, kubernetes, serverless, cloud, third party frameworks, and SaaS services to deploy and run these microservices. Imagine the permutations of failure modes across these abstractions and hundreds of micro-services. It dramatically increased the operational overhead causing a significant drag on developer productivity. As part of our developer productivity thesis at a16z, we believe there needs to be a new paradigm around tools and processes that eases this operational burden on the production side. The software industry has made excellent progress on instrumentation, collection of metrics, traces, logs, etc. Open source projects, like Prometheus, and standards, like Open Telemetry, have democratized the collection and storage layers of the observability stack. However, even with these improvements in instrumentation, developers are still inundated by an almost incomprehensible barrage of metrics. From those dreaded pages in the middle of the night, to logs, traces and dashboards that are created by other developers, the entire troubleshooting process is still very much static and archaic. We have done a good job of answering the “What is broken” question, but not the “Why is it broken” question. We have been looking for innovations in the “Why” layer, which we call the contextual intelligence layer, that leverages the underlying metrics or logs. Several attempts have been made in the past around anomaly detection and ML, which have failed because high rates of false positives resulted in developer alert fatigue. More than predictive analytics, a developer needs a contextual intelligence that aids them through the aggregated signals in a workbench format when they deal with an alert. We were thrilled when we met Manoj, the founder of, who had a similar thesis around contextual intelligence to drive observability forward. He is the ex-VP of engineering at AppDynamics where he built the core APM product line from scratch, and is deeply aware of the troubleshooting nuances. He and his team tried various root cause analysis (RCA) approaches and eventually came to the conclusion that we needed an assertions-based approach. Manoj and his team are on a mission to achieve true developer productivity on the production side by reducing the time from an alert to an RCA. Similar to the pre-production side, where developers write tests to assert their business logic, Asserts is bringing an assertions approach to the production side that is contextually shown to a developer as part of their regular troubleshooting process. We are happy to back Manoj and his team as they launch their next generation contextual intelligence product,, to make developers more productive. If you are an engineering leader who cares about developer productivity and reliable systems, check them out. *** The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. In addition, this content may include third-party advertisements; a16z has not reviewed such advertisements and does not endorse any advertising content contained therein. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments for which the issuer has not provided permission for a16z to disclose publicly as well as unannounced investments in publicly traded digital assets) is available at Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see for additional important information.

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