At AI Generation, we use several AI agents every day for real work: market monitoring, research, reporting, communication, coding and automation of repeated processes.
We use OpenClaw-based agents such as Ash and Jarvis in our own environments. We are also experimenting with Hermes agents such as Sofia and Agent Cooper. The agents communicate with us in the same channels as human colleagues: WhatsApp, Discord, Teams and email.
An agent is not one finished AI. It is a combination of an agent platform, a language model and tools. The model provides intelligence; the platform handles memory, tool use, communication and task execution.
Tools can be connected almost without limit: calendars, email, voice, databases, trading systems and internal company systems.
The reality starts to matter here. The biggest agent problems do not come only from the model, but from how the whole system works together. When the model layer changes, the agent's behavior changes. The same task, platform and tools can produce very different results with a different model.
We have also seen concrete reliability challenges. Sometimes an agent says a task is impossible and a moment later the same thing works normally. The issue is not necessarily intelligence; it may be memory, the session, a plugin or a tool chain.
Our biggest lesson so far is simple: the next real competitive advantage in AI agents is not just intelligence. It is reliability and ease of use.