Radically different
Background: Artificial intelligence–powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known about how users experience early-stage prototypes or which therapeutic processes contribute to clinical improvement. A transparent evaluation of emerging chatbot prototypes is needed to clarify if, how, and why artificial intelligence companions work and to guide their continued development. Objective: This mixed methods pilot study evaluated user experience, acceptability, and preliminary clinical signals for an early-stage mental wellness chatbot. We also examined whether baseline symptom severity moderated clinical improvement. Methods: Three sequential cohorts (n=125) completed a 2-week, incentivized chatbot exposure (approximately 60 min per week). Participants provided first-impression ratings, qualitative feedback, and pre–post assessments of depressive symptoms (PHQ-8 [Patient Health Questionnaire-8]), anxiety symptoms (GAD-7 [Generalized Anxiety Disorder-7]), psychological distress, well-being, and loneliness. Statistical models estimated symptom change and tested interactions with baseline symptom severity. Mixed methods analysis integrated quantitative outcomes with large language model–assisted qualitative content analysis of open-ended responses. Results: Participants described the chatbot as accessible, easy to use, and emotionally validating, while citing limitations in personalization and conversational depth. Qualitative responses consistently highlighted early therapeutic processes such as emotional validation, goal setting, and perceived attunement. Regression models showed significant pre–post reductions in depressive (Hedges =–0.32) and anxiety (=–0.32) symptoms, alongside modest improvements in distress and well-being. Baseline severity moderated improvement, with marginal effects indicating larger predicted reductions at higher PHQ-8 and GAD-7 baseline scores (eg, PHQ-8=15: =–0.84; GAD-7=15: =–0.62). Conclusions: This pilot provides a comprehensive view of early chatbot development and suggests promising user experiences and preliminary symptom improvements under structured pilot conditions. By integrating experiential and exploratory clinical data, the study identifies candidate process targets to inform ongoing refinement. Findings support continued development and demonstrate procedural feasibility for progression to larger, longer-term trials evaluating engagement and clinical outcomes under more naturalistic conditions.
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Background: Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings. Objective: This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program. Methods: A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service. Results: A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection. Conclusions: The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.
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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 projects—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 teams’ 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 teams’ 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. Teams’ 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 respondents—but especially in early-adopter verticals such as media and entertainment and technology hardware—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.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’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.
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