Google and Replit Reveal the Hidden Hurdles of Deploying AI Agents

Google Cloud and Replit, two prominent players in the AI agent space, have acknowledged the challenges of deploying these agents reliably. According to leaders from the two companies, who spoke at a recent VB Impact Series event, the capabilities of AI agents are not yet where they need to be. This constrained reality stems from difficulties with legacy workflows, fragmented data, and immature governance models. Amjad Masad, CEO and founder of Replit, noted that enterprises often build agents based on toy examples, which fail to work well when rolled out. "When enterprises are building agents to automate work, most of them are toy examples," Masad said. "They get excited, but when they start rolling it out, it's not really working very well." Masad emphasized that reliability and integration, rather than intelligence itself, are the primary barriers to AI agent success. Agents frequently fail when run for extended periods, accumulate errors, or lack access to clean, well-structured data. The issue with AI agents is not just a matter of technical complexity, but also a fundamental misunderstanding of how they work. "Agents are not like other technologies," Masad said. "They require a fundamental rethink and reworking of workflows and processes." This means that enterprises need to rethink their approach to building and deploying AI agents, rather than simply trying to integrate them into existing systems. Google Cloud has also been working on building out agentic tools, but leaders from the company have acknowledged the challenges of deploying these agents reliably. According to Masad, the problem is not just with the technology itself, but also with the way that enterprises approach the development and deployment of AI agents. "Most of the time, when we're building agents, we're building them based on what we think is the right thing to do, rather than what's actually going to work," Masad said. The struggles with AI agents have significant implications for society, particularly in the context of automation and job displacement. As AI agents become more prevalent, it is likely that they will displace certain jobs, particularly those that involve repetitive or routine tasks. However, this also creates opportunities for new industries and job types to emerge. In recent years, there has been a growing trend towards the development of AI agents, particularly in the context of the "vibe coding" movement. This movement, which emphasizes the importance of building software that is intuitive and user-friendly, has led to the development of a range of new tools and technologies, including AI agents. However, the challenges of deploying these agents reliably have highlighted the need for a more nuanced approach to their development and deployment. As the development of AI agents continues, it is likely that we will see significant advancements in the field. However, it is also clear that there are significant challenges to be overcome, particularly in terms of reliability and integration. By understanding these challenges and working to address them, developers and enterprises can build more effective and reliable AI agents that can have a positive impact on society.