Summary
more autonomous agents require decisions about control and verification;
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Production AI and Engineering Team Design: InfoQ’s November Certification Cohorts
Oct 26, 2026
2
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InfoQ has published details of three online certification cohorts starting in November 2026: AI Engineering with Adi Polak on November 12 (PST timezone), AI Engineering with Lan Chu on November 13 (CET timezone), and High-Performing Teams with Olimpiu Pop on November 17. The programs examine different parts of engineering work, from reliable retrieval and agent orchestration to delivery dependencies, human-AI responsibilities, and measures of team performance.
Production AI: Retrieval, Agents, and Operational Evidence
AI applications involve more than model selection. Retrieval can produce stale or incomplete context; more autonomous agents require decisions about control and verification; and shared AI infrastructure introduces questions about ownership and inference costs. The InfoQ Certified AI Engineering Program covers these decisions through work on retrieval-augmented generation (RAG), context and memory pipelines, agent design, AI infrastructure, evaluations, and reliability.
Adi Polak, author of Scaling Machine Learning with Spark and an engineering leader who has built machine learning and real-time streaming pipelines, facilitates the November 12 cohort. Her syllabus uses QCon material on context engineering to compare vector retrieval with Graph RAG and considers how agents share state and context. Later weeks address orchestration, control, inference gateways, and rollout-readiness evaluations.
Lan Chu, a principal engineer with more than seven years of experience building production data and machine learning systems, facilitates the November 13 cohort. Her syllabus draws on her QCon London case study of a RAG system built on more than 10,000 documents and used by more than 300 people. The work includes tracing retrieval and indexing decisions, comparing vector retrieval with Graph RAG, evaluating single-agent against multi-agent designs, and selecting production-readiness checks. Each cohort follows its facilitator's published syllabus rather than an identical set of exercises.
Team Design When AI Changes the Flow of Work
Increasing the speed of code production does not necessarily shorten delivery time. Review, approvals, infrastructure dependencies, or rework may become the limiting step. Introducing AI agents also changes who prepares context, reviews output, and remains accountable for decisions. The InfoQ Certified High-Performing Teams Program examines these interactions as parts of one team operating model.
The November 17 cohort is facilitated by Olimpiu Pop, an InfoQ Podcast co-host with 20 years of experience across software architecture, engineering management, and technology leadership. Participants examine team boundaries and delivery flow, map dependencies, consider tasks delegated to AI alongside human judgment, and assess whether familiar output metrics remain informative. The group capstone develops and presents a target operating model connecting responsibilities, delivery flow, AI roles, and measurement.
Working Through Decisions with Practitioners from Other Companies
The cohorts bring together senior engineers and architects from other companies to apply material from QCon talks to current engineering decisions. Participants work with their facilitator in live online sessions, discuss the constraints behind different approaches, and return to the group with what happened when they applied the material. Sessions are not recorded. Each cohort includes four live hours per week plus up to two hours of independent work. Certification depends on participation and successful completion of the relevant capstone; selected work may be considered for InfoQ publication, but publication is not automatic.
Full dates, facilitator-specific syllabuses, and participation details are available on the InfoQ Online Certification Programs website.
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Artenisa Chatziou
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Source: InfoQ
