The Next Frontier of AI May Depend on Trusting Autonomous Agents to Act Reliably

The evolution of AI can be viewed as a progression from chat-based intelligence toward assisted agents and, increasingly, systems capable of acting with greater autonomy. A recent survey describes this transition as a move from single-turn language generation toward agents that can observe, reason, use tools, act, and adapt across multiple steps.

For Robert Hommes, founder of Moyai, a company focused on AI-agent reliability monitoring, this progression raises a practical question: How much autonomy can organizations comfortably give an agent when its behavior may change from one task to the next? In his view, the technology required to build increasingly autonomous agents already appears to be developing rapidly. The larger consideration may be whether organizations can verify an agent’s behavior well enough to trust it with greater responsibility.

“Scalability requires giving agents high capabilities and full autonomy, but reliability has to develop alongside that autonomy,” Hommes says. His perspective places verification alongside capability as a defining part of the next phase of AI engineering. A verification layer can examine an agent’s actions and help determine whether its behavior is consistent with the outcome it was intended to produce.

That question becomes more complex as agents interact with tools, APIs, databases, and changing environments. Hommes distinguishes between explicit failures and implicit ones. “Traditional software monitoring is good at catching obvious problems, like a system going down or a tool failing to respond,” he explains. “With AI agents, everything can appear to be working normally while the agent still ends up with the wrong answer. The real challenge is understanding whether the agent actually accomplished what it was supposed to do.”

He describes a hypothetical example involving a profile-search tool. The tool could successfully return a response indicating that a profile was unavailable. From a technical monitoring perspective, the request completed successfully. The agent could then interpret the incomplete information, fill the gap from its model-generated assumptions, and produce an inaccurate answer. The meaningful failure emerges through the interaction among the agent, its tool, its environment, and its goal.

That distinction suggests a different role for monitoring. Structural and semantic signals can help identify behavior that differs from an agent’s expected patterns, after which the deviation can be examined for its significance. Moyai’s concept follows this two-stage logic: first identify an unusual behavior, then assess whether it represents a meaningful failure.

“This distinction is important because developers may naturally build guardrails and evaluation sets around failures they already anticipate,” Hommes states. Production can introduce behaviors that were difficult to predict during development. Continuous monitoring therefore becomes a way to discover new patterns as agents operate in real environments.

The broader observability field is moving toward greater intelligence as AI becomes part of increasingly complex systems. A 2026 observability research report describes a growing role for AI-driven analysis, anomaly detection, root-cause analysis, and agent-assisted operational decisions. It also points to the importance of observability data when AI systems are entrusted with actions that can affect infrastructure and business operations.

For Hommes, agent behavior requires a related shift in engineering practice. Traditional software can often be tested against defined paths, with developers specifying how a system should respond to known conditions. Agents operate with greater variation because their decisions can depend on model outputs, context, tools, and previous steps. That makes production behavior an ongoing source of information about how the system actually works.

His own background helps explain this perspective. Hommes has worked across machine learning, cybersecurity, and fintech, areas where operational reliability can carry substantial importance. “In cybersecurity, I saw how non-deterministic models could generate large numbers of unusual signals, creating a need to distinguish meaningful incidents from ordinary variation,” he shares. Hommes later encountered teams manually reviewing multi-step agent traces and even organizing “watch parties” to observe agents operating in real time. Those experiences contributed to his decision to build Moyai.

The emerging discipline of agent reliability extends the idea of observability toward understanding whether an agent’s behavior is producing the intended result. Moyai is designed around production traces, behavioral anomaly detection, evaluation of those anomalies, and incident-level information that can help teams investigate and improve agent behavior.

This emphasis also connects with findings from an AI safety report, which describes current AI performance as “jagged,” with systems demonstrating strong capabilities in some complex tasks while still encountering unexpected difficulties in simpler situations. The report also highlights monitoring and post-deployment safeguards as parts of managing increasingly capable AI systems.

For Hommes, the implication reaches beyond a particular monitoring product. As agents take on longer sequences of actions, engineering may increasingly include a continuous cycle of observing, analyzing, and improving behavior after deployment. Monitoring becomes part of the development process itself, providing evidence about execution paths that developers may not have anticipated.

That could gradually change the role of AI engineering. Teams may spend more time examining how agents behave in production, investigating recurring patterns, and refining systems based on those observations. The goal is greater confidence in granting agents responsibility, supported by verification that continues after deployment.

As autonomous systems become more capable, reliability may become an ongoing discipline. An agent’s behavior today may differ from its behavior tomorrow, and continuous monitoring offers a way to keep that changing behavior visible. Hommes states, “That visibility is a foundation for allowing AI systems to operate with greater autonomy while keeping human teams informed about how they are actually performing.”