In the early 2010s, releasing software once a quarter was considered ambitious. Back then, even major analysts encouraged limited release cycles to maintain stability. Fast forward to today, and releasing code quarterly feels archaic in an era defined by continuous integration and continuous deployment (CI/CD).
At AWS re:Invent, Edith Harbaugh, co-founder and CEO of LaunchDarkly, reflected on this shift while discussing the evolution of AI in the modern software stack. She highlighted how over 5,000 LaunchDarkly customers now release code dozens of times daily—an astonishing acceleration made possible by automation, distributed infrastructure, and data-driven workflows.
From Golden Discs to Continuous Deployment
The journey to today’s iterative release model has been one of unlearning old engineering traditions. In the era of physical software distribution, teams had to perfect each version before shipment.
“When you were shipping a physical disk and users installed it on their own machines, there was no room for failure,” Harbaugh explained. That constraint drove the pursuit of the so-called “golden disc”, where software had to be final before leaving the factory.
Even after cloud computing took over, traces of that mindset remained. Developers built systems around users manually updating software versions. Only slowly did the industry acknowledge the SaaS reality, where instant updates, feature flags, and experimentation replaced version numbers and manual installs.
Today, this thinking extends to AI-driven systems, where outcomes are nondeterministic and evolving—but the core challenge remains: how to manage change safely and continuously.
AI Agents: Just Another Backend
For Harbaugh, the rise of AI agents doesn’t represent a revolution but rather an incremental shift—another layer in backend evolution.
She views AI models as simply new compute surfaces to experiment with. “If you’re pushing features multiple times per week or even day, you gain a tighter feedback loop with customers,” she said. Many LaunchDarkly users already run A/B tests between different backends or throughput configurations, treating an AI model swap just like deploying a new API or microservice.
In that context, an AI is just another backend, one optimized for adaptability and learning rather than deterministic output. Developers still measure latency, accuracy, response time, and reliability—the same metrics used for any backend system.What changes, however, is the speed of iteration and the flexibility of decision-making, as AI agents can adapt dynamically to performance signals and user behavior.
The Shift Toward Self-Healing Software
While today’s systems detect errors and roll back releases automatically, Harbaugh envisions a near future where software detects, diagnoses, and self-corrects all without human input.
“The next step beyond detecting failures is generating a fix and committing it automatically,” she said. In this view, an AI agent could observe recurring errors tied to a specific iOS version, generate a patch, and deploy it safely through CI/CD all autonomously.
The same approach could extend to customer-driven updates. If feedback analysis reveals users requesting new languages or accessibility features, AI systems could localize software content or update features instantly.
“Localization has historically been very manual, but AI can make it cheaper, faster, and more accurate,” Harbaugh noted, emphasizing how automation is scaling developer productivity rather than replacing it.
Maintaining Control in an Agentic Era
As AI workflows expand across development pipelines, observability, testing, and control remain the pillars of LaunchDarkly’s philosophy. Harbaugh summarized it in three words: launch, measure, control.
Even with agentic AI—systems that can trigger actions autonomously—the core metrics stay the same: speed, precision, reliability, and business impact. The difference lies in how fast cycles close and how many layers of automation mediate human oversight.
Harbaugh pointed out that LaunchDarkly’s platform now enables customers to experiment with multiple large language models (LLMs) simultaneously, tracking their performance through practical KPIs. This experimentation helps teams understand real-world ROI rather than relying solely on benchmarks or hype.
The company is also addressing new challenges such as versioning AI data models during live releases—ensuring that updates are traceable and reversible, even when models evolve continuously.
AI Won’t Replace Developers—It Will Empower Them
Harbaugh dismisses the notion that AI-coded systems will eliminate engineering roles. Instead, she believes automation will elevate human creativity, much like spreadsheets empowered financial analysts rather than eliminating jobs.
“AI doesn’t make engineers obsolete,” she said. “Just as spreadsheets replaced slide rules but not Wall Street, AI will expand what developers can focus on.”
With AI handling more operational and repetitive aspects of deployment, developers can invest time in innovation, experimentation, and solving higher-order problems—factors essential for maintaining a competitive advantage in the fast-moving world of software delivery.
Conclusion: AI as the Invisible Backend
The convergence of feature management, AI automation, and continuous delivery marks the dawn of self-healing, self-optimizing software systems. In this new paradigm, AI agents quietly power reliability and adaptability behind the scenes, functioning as an intelligent backend that responds to change at machine speed.
For companies leading this transformation, like LaunchDarkly, the message is clear: AI isn’t replacing backend engineering—it’s becoming its next evolution.
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