InfoQ has introduced the InfoQ Certified AI-Assisted Engineering Program, an online certification. The program is facilitated by senior practitioners Zichuan Xiong and Premanand Chandrasekaran. The first cohort is scheduled from September 18 to October 16, 2026, with sessions on Fridays at 9:00 AM PDT, priced at $1,470. A second cohort will run from October 19 to November 16, with sessions on Mondays at 9:00 AM PDT.
This certification is intended for senior engineers and architects who regularly use coding agents on production codebases with established histories. The curriculum addresses critical aspects of AI-assisted engineering, including defining agent permissions, methods for identifying errors in AI-generated code, and strategies for validating the quality, scalability, and maintainability of such code within an organizational context.
The program spans five weeks, with each week focusing on a specific area. Topics include onboarding agents with least-privilege permissions, translating issue tickets into verifiable requirements, separating code generation from review processes, integrating verification into CI pipelines, and codifying review findings into reusable rules.
Participants will watch a QCon talk each week in advance, followed by a four-hour live session. During these live sessions, attendees will apply the week's framework to a shared brownfield codebase, receiving feedback from senior engineers from other companies.
Zichuan Xiong, Head of AIOps at Thoughtworks, and Premanand Chandrasekaran, Head of Technology at Thoughtworks, will lead the program. Both instructors have extensive experience in engineering and AI systems, and will also conduct the Effective AI-Native Development with Harness Engineering training at QCon San Francisco in November.
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InfoQ has opened enrollment for its new InfoQ Certified AI-Assisted Engineering Program, an online certification designed for senior engineers and architects using coding agents on production code. The program focuses on agent permissions, error detection, and validating AI-generated code for scalability and maintainability within existing codebases.