Why your AI product needs a different development lifecycle

Aishwarya Reganti and Kiriti Badam introduce the Continuous Calibration/Continuous Development (CC/CD) framework for building AI products. They argue AI systems are non-deterministic and require negotiating a tradeoff between agency and control, so teams should scope capabilities in low-agency, high-control versions and earn autonomy through repeated evaluation and calibration loops.

01Key takeaways

• AI products are non-deterministic on both the user input side and the system output side, so they need different development processes than traditional software.
• Every increase in an AI system's agency reduces human control, so autonomy should be earned gradually rather than granted at launch.
• Scope AI products as a ladder of versions, starting with simple, easily verified, easily overridden decisions before adding autonomy.
• Build a reference dataset and evals early, then use live interaction logs, control handoffs, and manual error-pattern review to calibrate the system after deployment.
• Let the problem and evals guide architecture changes rather than leading with tooling, and expect to revise evals as real user behavior diverges from expectations.

02Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.