Agentic AI Is Shipping Code. Who Owns The Risk?

Not long ago, much of the discussion in software engineering circles was about whether developers should use AI to write code. Now, the focus has shifted to agentic AI and agents that run commands with minimal human direction. This technological shift is creating new challenges for accountability in software development.

Gartner has placed agentic AI at the top of its 2026 cybersecurity trends, warning that the technology is creating new attack surfaces as employees and developers adopt it. The research also highlights how no-code platforms and vibe coding are speeding up the spread of unmanaged agents, unsecured code and potential compliance violations.

Accountability Challenges

Before AI agents, development gave organizations a fairly clear path to accountability. A developer made the change, a reviewer checked it, and the service team owned what reached production. This chain is now more complicated when an AI agent takes a feature request and turns it into code for a developer to review.

For example, a product manager provides the instructions, the agent generates the change, and the developer approves it. If that change exposes customer data, who owns the decision to ship it? The most practical answer, according to experts, is the person who approves the merge.

Security Risks

While teams can now produce more software faster, they don't automatically gain the capacity to assess it all. More output means more changes to approve and less time to give each one proper attention.

Research from The Consortium for Information & Software Quality estimated that the cost of poor software quality in the U.S. was approximately $2.41 trillion in 2022, with accumulated technical debt tracked separately at around $1.52 trillion. As teams produce more code, even a low rate of flawed code can create more technical debt than they can keep up with.

Some of that debt is also security debt, as security vulnerabilities found in production can become entry points for attackers and cost more to fix in the long run.

Governance Approaches

Any approach to governing agentic AI has to start with knowing where it is being used. Gartner recommends identifying both sanctioned and unsanctioned AI agents, establishing controls for each, and preparing incident response plans for agent-related risks.

Agents should have only the permissions they need, with human approval reserved for higher-risk actions, such as merging changes to protected branches or modifying authentication and sensitive data. Routine changes can use the same security checks as human-authored code, with agent-generated changes clearly identified so teams can see where they came from.

Organizations can only take responsibility for agents if they know whether the controls around them are working and developers have the skills they need. The most useful measures come from the code itself, such as comparing vulnerability rates in agent-authored and human-authored changes, and tracking that difference to see if risk goes down.

Developing New Skills

Developers are at the center of securing both the agent itself and the software it produces. In many organizations today, agents operate with some of the same access as the developer using them, meaning developers often influence what agents can access without making a deliberate choice.

Leadership is responsible for setting boundaries for what agents can access and making sure developers have the skills to configure agents securely and review what they produce. The EU's Cyber Resilience Act makes this clear by placing responsibility on the organizations that put products on the market.

As agents take on more of the development process, organizations need to define where human responsibility lies. Waiting for an incident to establish those boundaries is a costly way to find out.