AI Agents Mark a True Turning Point
Gartner’s latest research points to a major disruption in how software gets built and maintained. Autonomous or agentic AI agents are software entities that can plan, act, and adapt independently, and they are now entering the mainstream. These agents don’t just assist humans. They operate on their own, collaborating with other agents or systems to drive forward tasks across the software development life cycle.
The appeal is clear: software that once took weeks can now be prototyped, tested, and pushed in a matter of days. Agentic AI systems can code, write documentation, generate tests, and even flag bugs or compliance issues. They reduce manual effort and allow engineers to focus on more creative and strategic work.
But there’s a catch.
The same autonomy that speeds up software delivery also introduces new kinds of risk, many of which security leaders have never had to plan for before. Traditional AppSec safeguards no longer offer full coverage when agents are writing, shipping, and learning in real time.
According to Gartner, AI agents represent more than just technical evolution. They signal a structural change in how engineering teams will operate, how business value will be created, and how risks must be managed. The time to get ahead of this shift is now.
Why This Shift Is Happening Now
There are four primary reasons autonomous AI agents are gaining real traction in software engineering:
- Generative AI has matured. Within minutes, it can now reliably generate production-grade code, complete test coverage, and create usable documentation.
- Cloud scalability allows orchestration. Developers can now deploy and scale multi-agent workflows with minimal infrastructure friction.
- Dev tooling is more open and flexible. IDEs, CI/CD systems, and infrastructure-as-code platforms support extensibility through APIs and plugins, making agent integration seamless.
- Organizations face pressure to innovate faster with fewer resources. Engineering leaders are under immense strain to deliver more, cut costs, and retain talent in a highly competitive market.
In Gartner’s 2024 Software Engineering Leader survey, over 50% of respondents said they are currently using or actively planning to adopt generative or autonomous AI tools.
The top reasons? Productivity, time-to-market, and team efficiency.
What these numbers reflect is a mindset shift: AI agents are not just “nice to have” tools, they are becoming foundational to how modern development gets done.
Gartner Report
How AI Agents Will Disrupt Software Engineering
Download the full Gartner report to explore how AI agents are reshaping software engineering, and what you can do to prepare.
Get the Report NowFrom Productivity to Developer Partnership
The promise of AI agents goes beyond time savings. They are also transforming the way developers experience their work.
When repetitive or boilerplate tasks like writing unit tests or managing code formatting are offloaded to agents, developers gain mental space. That space translates into higher creativity, more time for strategic thinking, and fewer interruptions. Teams that adopt agentic workflows report improved morale and stronger technical ownership because developers are no longer bogged down by repetitive work that doesn’t move the business forward.
At the same time, having well-governed agents in place can improve onboarding and collaboration. For instance, new hires can rely on AI agents to flag risks or recommend implementation patterns early, reducing their ramp-up time and enabling better knowledge transfer.
But this only works when the AI is aligned with business goals and secure by design.
Security Must Evolve with Agentic Speed
While AI agents bring immense efficiency, they also introduce new threats, often in places traditional AppSec strategies don’t cover.
Gartner outlines several critical risks that engineering and security leaders must prepare for:
- Prompt injection: Where agents are manipulated by malicious instructions embedded in input.
- Data leakage: Where sensitive internal data is unintentionally surfaced through generated output.
- Supply chain drift: When agents pull unverified packages or dependencies from public registries.
- Denial-of-wallet: When poorly scoped agent tasks spin out of control and rack up significant compute costs.
The common thread across these threats is the autonomy of the AI agents deployed. Once agents can act independently, security controls must move closer to the source. You can’t afford to catch vulnerabilities only after code reaches staging or production.
Forward-looking organizations are already implementing strategies to close these gaps, like:
- Real-time scanning inside IDEs: Ensuring insecure code never leaves the developer’s machine.
- Policy-as-code at every step: Enforcing security rules during merge requests and build pipelines.
- Agent behavior monitoring: Watching for abnormal activity, model drift, or resource spikes.
- Secure prompt engineering: Teaching developers how to frame instructions in ways that prevent misuse.
Security is no longer a final checkpoint. In the age of agents, it must be a continuous feedback system embedded across the entire life cycle.
The Skills and Mindset Shift That’s Required
Adopting AI agents isn’t only about tooling. It requires a cultural shift in how teams think about software creation, accountability, and risk.
Gartner emphasizes that teams must move from being AI “users” to AI “orchestrators.” This means developers need to understand not just how to prompt an agent, but how to guide, monitor, and evolve it over time. Skills like prompt engineering, human-agent collaboration, and governance modeling are rapidly becoming core competencies.
Checkmarx has seen success where teams invest in structured training, run internal experiments, and build playbooks for secure AI integration. This approach doesn’t just reduce mistakes. It increases developer confidence and improves the organization’s ability to scale AI adoption responsibly.
Psychological safety also plays a key role. Developers must feel empowered to test, fail, and iterate without fear. At the same time, security and compliance teams need to shift from enforcement to enablement, thereby becoming partners in innovation instead of blockers.
Five Strategic Moves to Make This Quarter
If your team is preparing to adopt or already piloting AI agents, here are five critical actions that align with both Gartner guidance and Checkmarx field experience:
1. Pilot a small, low-risk agent use case. Choose a workflow like test generation, documentation, or code linting. Measure the time, effort, and outcomes before and after.
2. Establish security baselines now. Know your current vulnerabilities, dependency maps, and deployment behaviors so you can measure the impact of agentic change.
3. Create a multidisciplinary governance team. Include engineering, AppSec, legal, and platform ops so decisions are informed and balanced.
4. Train developers in secure prompt design. Poorly phrased prompts are one of the most overlooked risk vectors in generative and agentic workflows.
5. Monitor everything agents do and act fast on anomalies. Logging, real-time alerts, and cost tracking are essential for sustainable operations.
These actions will set the foundation for a more secure, scalable adoption path and help you avoid expensive missteps down the road.
Download the Research That Helps You Lead This DevSecOps Shift
AI agents are here. They are reshaping how work gets done, how software is built, and how organizations compete.
Gartner’s latest research offers a practical, executive-ready view of what’s changing, what’s at risk, and what high-performing teams are doing to stay ahead. The frameworks, examples, and timelines inside this report can help you chart a smarter course through the noise.
Download the full Gartner report now and take the next step in building a secure, agent-ready engineering organization.