The year 2025 will be remembered as the moment AI-assisted software development entered its acceleration era. Improvements in the capabilities of coding agents, copilots, and automated workflows allowed teams to move faster than ever.
But alongside that acceleration came a growing tension. Teams were shipping code at unprecedented velocity, yet trust in AI-generated changes didn’t grow at the same rate. Developers reported feeling both empowered and uneasy: they could produce more output, but they couldn’t always be certain that the output was correct.
Postmortems, operational incidents, and late-stage defects increasingly pointed to subtle logic errors, configuration oversights, and design misunderstandings introduced by AI. We recently wrote about how 2025 had an unprecedented number of incidents. And our recent State of AI vs. Human Code Generation Report found that AI code has 1.7x more issues and bugs in it.
That trust gap is now impossible to ignore and it sets the stage for what comes next. If 2025 was the year of speed, then 2026 will be the year of quality, the moment when engineering organizations shift their focus from just “how fast can we generate code?” to an equal focus on “how confident can we be in the code we ship?”
The industry is moving into a new phase, one defined not just by acceleration, but also by accountability, reliability, and correctness. We’ll share how we got here and the 4 shifts that companies should make to how they use AI in 2026.
2025: The year of speed
2025 was the year when “ship faster” crystallized into a core performance metric for engineering organizations. Leaders often emphasized velocity, tracking PR throughput, diff volume, cycle time, and the raw number of AI-assisted changes as measures of progress. Many companies positioned AI-generated code as a symbol of innovation and sometimes even as a badge of competitiveness.
Major players like Microsoft and Google highlighted how much of their code was now produced or assisted by AI, framing volume as the signal to watch. The focus was on scale: how much code AI could help generate, how quickly, and with how little human intervention.
Quality, consistency, and maintainability became secondary concerns in the conversation.
The hidden costs: Operational incidents and quality regressions
But the speed came with a cost. As teams pushed more AI-authored code into production, a surge of subtle defects began surfacing later in the release cycle. Issues that were once caught through careful review or design deliberation now slipped through.
SRE and operations teams bore much of the impact. Incident reports revealed misaligned assumptions between human-written components and AI-generated logic. Infrastructure configurations created by AI introduced fragility that wasn’t always immediately visible. Our recent report found that AI generated code had up to 75% more logic and correctness issues in areas that were more likely to contribute to downstream incidents.
As 2025 progressed, more production incidents and postmortems pointed to AI-generated code as a contributing factor.
Developers felt empowered by AI in 2025, but uneasy about the code produced
For developers, 2025 was both liberating and unsettling. Many described feeling genuinely empowered: able to build more, experiment more, and clear more tasks in less time.
Yet, alongside that empowerment came growing discomfort about the reliability of the code being produced. Developers increasingly reported moments where the AI-generated solution “looked right” but didn’t feel trustworthy. Reviewing AI-authored code often proved more cognitively demanding than writing it from scratch (something we wrote about here), and subtle errors could be easy to miss in large, machine-generated diffs.
Why quality became the pain point no one could ignore
By the end of 2025, the industry-wide trust gap in AI-generated code had become too large to ignore.
We heard this firsthand when we themed our booth at re:Invent around the Vibe Code Cleanup Specialist meme. That generated conversations with CTOs and other senior engineering leaders about how they felt like their jobs had become, in large part, focused on cleaning up AI mistakes. These conversations showed a pretty widespread consensus across industries and companies: it was time for a return to quality code.
AI had made coding faster, but it had not made correctness automatic. And without correctness, speed loses its value.
The economic reality set in
The final catalyst for the shift toward quality was financial. As more organizations embraced AI-first development, the downstream cost of defects became increasingly visible. Things like code reviews and testing took more time. Outages became more frequent, rollback rates increased, and teams were forced into unplanned refactoring cycles to correct issues introduced by generative tools.
Executives and finance leaders started to quantify the impact: operational incidents, missed SLAs, reliability regressions, and customer churn all carry a price. The cost savings promised by AI-generated code began eroding as teams spent more time debugging and recovering from AI-introduced errors.
Organizations started asking a different set of questions, not “how much code can AI produce?” but “what is the true cost of code that hasn’t been properly validated?”
2026: The year of quality
Organizations are entering 2026 with a different set of priorities. Speed is no longer the only metric that separates high-performing teams from struggling ones; quality has become the true competitive differentiator. Engineering leaders are beginning to shift their KPIs away from raw throughput and toward indicators of correctness and maintainability.
Defect density, review load, merge confidence scores, test coverage, and long-term maintainability metrics are likely to replace cycle time as the numbers that matter most this year. Teams are starting to optimize, not for how quickly code could be generated, but for how reliably it could be trusted. In this new environment, “correct code” will become the new definition of productivity.
Predictions: What 2026 will look like & how to adapt
The shift toward quality will reshape how engineering teams operate, evaluate tools, and measure success. By the end of 2026, several trends will become unmistakably clear.
Shift 1: Companies will track different AI-related metrics
First, companies will begin formally tracking AI-related defect metrics. Instead of treating AI-generated bugs as anecdotal, organizations will measure them with the same rigor used for security incidents or system reliability. Metrics such as AI-attributed regression rates, incident severity linked to AI-generated changes, and review confidence scores will become standard engineering dashboards.
Shift 2: Third party tools will be used to validate AI-code
Second, organizations will adopt more third-party tools designed specifically to validate their coding agents and protect production systems. These tools will act as independent safeguards, offering objective assessments of code quality and catching issues that the generating agent cannot reliably detect since they introduced them in the first place. Enterprises will increasingly view external third party tools for validation as essential risk mitigation rather than optional tooling.
Shift 3: Multi-agent workflows will be used to validate code
Multi-agent workflows will normalize continuous review and validation. Instead of a single agent generating code and hoping for correctness, multi-agent systems will create a layered workflow: one agent writes, another critiques, another tests, and another validates compliance or architectural alignment. These chains will reduce the cognitive burden on developers and raise the certainty that the code entering production is safe, stable, and coherent.
Shift 4: Companies will develop governance around how to use AI
As quality becomes the defining engineering priority, teams start building structured governance around how AI is used. Organizations introduce explicit policies on acceptable AI usage, documentation requirements, and review expectations.
Taken together, these shifts will signal a broader evolution: AI development is moving from experimentation to discipline, from speed to stability, and from novelty to operational maturity.
Conclusion: AI use will finally grow up this year
The story of 2025 was a story of speed. But it also revealed a harder truth: when speed is easy, quality is the real challenge.
In the coming year, the industry will grow up when it comes to their AI use. Engineering organizations that thrive will be the ones that design workflows around reliability, maintainability, and architectural clarity. They will be the companies that treat AI not as a shortcut, but as a system that demands robust validation, thoughtful oversight, and careful integration into existing processes.
The next wave of AI innovation will not be defined by how fast we can generate code. It will be defined by how confidently we can ship it. The future belongs to teams that prioritize correctness, trustworthiness, and long-term stability.
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