Agentic AI solved coding — and exposed every other problem in software engineering

The impact of agentic AI on software engineering has been profound, driving massive execution leverage and enabling the generation of more code than ever before. However, this newfound efficiency has also highlighted a difficult question: if we're shipping code faster than ever, why aren't our products improving at the same rate? The reason lies in the fact that writing code was never the rate limiter.

Defining the right requirements, integrating with complex systems, and maintaining software under real-world conditions has always been the hard part. And when agents flood an organization with lots of new code, the hard part only gets harder. Agents compress execution time, but they do not compress ambiguity, accountability, or operational complexity.

As AI-generated code scales, human review is becoming a massive new bottleneck, and engineers are losing the context needed to catch agent mistakes. The companies that understand this will move forward deliberately and even create new roles because of AI. The ones that don't will default to a simpler, far more destructive conclusion: Reduce headcount and increase AI spend.

The playbook for navigating this chaos demands caution, precisely because the technology is moving so fast. Enterprise engineering leaders need a deliberate playbook to navigate the chaos. Here's how to start: Phase 1: Financial and risk governance Protect the downside — secure the infrastructure and cap the financial bleeding.

Treat governance as a tier-one risk: The pressure to integrate AI is real, but giving teams the freedom to experiment without a centralized structure creates fragmented processes, duplicated work, and runaway costs. Organizations will need to establish shared standards while still allowing teams to adapt and explore within defined boundaries. This means treating agent configuration like production infrastructure — versioning, reviewing, and testing prompts and skills before rolling them out gradually.

Enforce least privilege for non-human actors: Never allow an agent to simply inherit the full permissions of its human operator. Human engineers are granted broad access because they possess contextual judgment and bear ultimate accountability. Deploying agents with human-level access without careful consideration introduces an accountability gap into your systems.

Implement strict separation between read and write/execute access, and mandate human-in-the-loop approval gates for destructive or production-altering actions. As agents transition from suggesting code to autonomously executing tasks, they must be rigorously incorporated into your security model. Watch your wallet: Protect your overall AI budget by enforcing quotas and rate limits for both engineering and production.