Production AI agents are being built with a strong emphasis on reliability, control, and practicality, according to recent research based on a survey of 306 practitioners and 20 in-depth case studies across 26 domains. The findings reveal a clear pattern: real-world deployments favor simple, tightly bounded systems over fully autonomous or open-ended agents. A key trend is the shift toward tightly bounded autonomy, where AI agents are designed to perform a limited number of well-defined steps before requiring human input. In practice, 68% of agents complete at most 10 steps before a human intervenes, and 46.7% make fewer than five model calls before needing oversight. This approach minimizes the risk of uncontrolled errors, ethical missteps, or system drift, making it a preferred model for production environments. Deployment architectures prioritize structured, predefined workflows over open-ended planning. This design choice enhances predictability and makes it easier to debug, monitor, and validate agent behavior. The focus is on creating systems that are transparent, auditable, and maintainable—critical for enterprise adoption. Despite the growing interest in AI agents, most implementations rely on off-the-shelf large language models (LLMs) rather than custom-tuned or privately hosted models. A striking 70% of developers use prompting techniques with pre-existing models, avoiding the complexity and cost of fine-tuning. This plug-and-play strategy accelerates development and prototyping but raises concerns about long-term customization, data privacy, and model alignment. Human oversight remains a cornerstone of current AI agent workflows. While some view human-in-the-loop systems as a temporary stopgap, the data shows they are now a fundamental design principle. In fact, 74% of teams rely primarily on human evaluation to assess agent performance, and 52% use LLMs as judges to evaluate outputs. This hybrid model ensures accountability, allows for contextual refinement, and helps maintain quality in real-world applications. The most common use cases are internal tools rather than customer-facing products. Machine-to-machine agents, which enable automation between systems, account for just 7% of deployments, indicating that the vision of vast, interconnected AI agent networks is still far from reality. Instead, organizations are focusing on high-impact, low-risk internal workflows such as data processing, report generation, and code review. Another notable trend is the preference for building custom frameworks from the ground up. Rather than adopting third-party agent platforms, developers are creating their own tools. This approach offers greater control, avoids vendor lock-in, and allows for better alignment with specific workflows. However, it also increases the burden of maintenance, as teams must manage feature churn, versioning, and rapid model updates. Reliability is the top challenge in AI agent development, with teams struggling to ensure consistent correctness and evaluate performance at scale. The lack of formal benchmarks and standardized evaluation methods means that most assessments remain informal, anecdotal, and reliant on human judgment. This gap hinders the ability to compare systems, track progress, and scale solutions across organizations. In summary, the most successful AI agent deployments are not defined by complexity or ambition, but by simplicity, control, and human-in-the-loop design. The focus is on building systems that are dependable, interpretable, and aligned with real business needs—proving that the most effective AI agents are not the most autonomous, but the most trustworthy.