Redefining the future of software engineering

Robots performing software development tasks in a continuous integration and deployment cycle

Agentic AI changes the unit of automation from the task to the workflow. For engineering leaders, that means the first hard problem is no longer model accuracy in isolation, but how an agent fits into SDLC controls: source control, CI/CD, test environments, approval gates, and release orchestration. The architecture question is whether the agent is a helper inside existing pipelines or a new orchestrator that sits across them.

Integration is likely to be the main constraint before autonomy becomes the main opportunity. Software teams usually have heterogeneous repositories, build tools, ticketing systems, and deployment platforms. An agent that can reason across those systems needs stable APIs, clear permissions, and deterministic boundaries for what it may change versus what must remain human-approved. Without that, adoption can stall in pilot mode even if the underlying model is capable.

Operationally, the shift pushes teams toward stronger guardrails rather than fewer ones. If agents can create code, propose fixes, or trigger actions, then identity, secret handling, audit logging, and rollback design become central platform concerns. Security teams will need to decide how to isolate agent credentials, monitor anomalous actions, and preserve evidence for incidents or compliance reviews. That makes agentic AI as much a governance program as a productivity program.

There is also a cost and reliability trade-off that platform teams cannot ignore. If agent workflows increase compute consumption or require repeated inference calls across long-running development loops, usage costs can become part of delivery economics. The practical test is whether the organization can measure the end-to-end effect on lead time, defect escape, and release confidenceโ€”not just whether the agent can complete a coding step.

Software engineering has experienced two seismic shifts this century. First was the rise of the open source movement, which gradually made code accessible to developers and engineers everywhere. Second, the adoption of development operations (DevOps) and agile methodologies took software from siloed to collaborative development and from batch to continuous delivery. Now, a third such shift looks to be taking shape with the adoption of agentic AI in software engineering. Thus far, engineering teams have mainly used AI to assist with coding, testing, and other individual tasks, within tightly designed parameters. But with agentic capabilities, AI agents become reasoning, self-directing entities that can manage not just discrete tasks but entire software projectsโ€”and do so largely autonomously. If adopted and fully embraced by engineering teams, agentic AI will usher in end-to-end software process automation and, ultimately, agent-managed development and product lifecycle automation. This report, which is based on a survey of 300 engineering and technology executives, finds that software engineering teams are seeing the potential in agentic AI and are beginning to put it to use, but so far in a mainly limited fashion. Their ambitions for it are high, but most realize it will take time and effort to reduce the barriers to its full diffusion in software operations. As with DevOps and agile, reaping the full benefits of agentic AI in engineering will require sometimes difficult organizational and process change to accompany technology adoption. But the gains to be won in speed, efficiency, and quality promise to make any such pain well worthwhile. Key findings include the following: Adoption momentum is building. While half of organizations deem agentic AI a top investment priority for software engineering today, it will be a leading investment for over four-fifths in two years. That spending is driving accelerated adoption. Agentic AI is in (mostly limited) use by 51% of software teams today, and 45% have plans to adopt it within the next 12 months. Early gains will be incremental. It will take time for software teamsโ€™ investments in agentic AI to start bearing fruit. Over the next two years, most expect the improvements from agent use to be slight (14%) or at best moderate (52%). But around one-third (32%) have higher expectations, and 9% think the improvements will be game changing. Agents will accelerate time-to-market. The chief gains from agentic AI use over that two-year time frame will come from greater speed. Nearly all respondents (98%) expect their teamsโ€™ delivery of software projects from pilot to production to accelerate, with the anticipated increase in speed averaging 37% across the group. The goal for most is full agentic lifecycle management. Teamsโ€™ ambitions for scaling agentic AI are high. Most aim for AI agents to be managing the product development and software development lifecycles (PDLC and SDLC) end to end relatively quickly. At 41% of organizations, teams aim to achieve this for most or all products in 18 months. That figure will rise to 72% two years from now, if expectations are met. Compute costs and integration pose key early challenges. For all survey respondentsโ€”but especially in early-adopter verticals such as media and entertainment and technology hardwareโ€”integrating agents with existing applications and the cost of computing resources are the main challenges they face with agentic AI in software engineering. The experts we interviewed, meanwhile, emphasize the bigger change management difficulties teams will face in changing workflows. Download the report This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Reviewโ€™s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
https://www.technologyreview.com/2026/04/14/1134397/redefining-the-future-of-software-engineering/

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