August 20, 2026
Yesterday, we spent the day with 180 engineering and technology leaders at the Sydney Engineering & DevOps Summit discussing one of the biggest questions facing software teams today:
As AI accelerates software development, how do we maintain the quality, control, and trust required to deploy it at enterprise scale?
Across roundtables and panel discussions, one thing became clear: AI has removed some of the traditional bottlenecks in software development, only to expose new ones.
Read MoreAugust 13, 2026
AI is not making quality assurance less important. It is expanding the QA mandate.
For decades, quality teams have focused on a familiar set of questions: Did the software meet its requirements? Did the integration work? Did a new release introduce a regression? Did the system produce the expected result?
Those questions still matter. But as organizations begin deploying generative AI and autonomous agents into production, quality leaders face an additional challenge: assuring systems whose behavior cannot always be specified in advance.
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August 10, 2026
For years, the Forward Deployed Engineer (FDE) has occupied a unique position between technology vendors and their customers. These engineers work close to the customer, translating complex business requirements into working software, integrations, configurations, and production outcomes.
Now, as generative AI and autonomous agents move from experimentation into business-critical systems, a related deployment challenge is emerging: putting frontier AI safely and reliably into production.
Some organizations refer to engineers working at this boundary as Frontier Deployment Engineers. Others use titles such as AI Deployment Engineer, Forward Deployed AI Engineer, or AI Forward Deployed Engineer.
The terminology is still evolving. The underlying engineering challenge is not.
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