The capabilities of AI agents, such as their context windows and reasoning benchmarks, are often the focus of discussion. However, when AI products fail in production, it’s rarely due to the model’s capability.

After building and consulting on AI agents for over 18 months, common failure modes have been identified. One major issue is that users don’t interact with the agent because it requires a cognitive action, such as opening a browser tab, which doesn’t fit into their daily routine. Humans don’t change their behavior to accommodate new tools; instead, useful tools must adapt to existing behaviors.

Another problem is that many AI agents are reactive, waiting for users to ask questions, rather than being proactive and anticipating needs. This is not a model limitation, but rather an architecture choice that prioritizes sessions over relationships. Successful AI agents, on the other hand, live in messaging platforms like WhatsApp, iMessage, and Telegram, proactively reach out when relevant, and maintain a coherent memory of the user across conversations.

The tools to build these types of AI agents exist, including Agno, Langchain, Photon Codes, and Langfuse. However, the mindset of prioritizing channel and memory as primary constraints is still rare. The gap between what AI agents can theoretically do and what they actually do for people in their daily lives is largely a distribution and persistence problem, not a capability problem. We need to shift our focus from solving capability problems to addressing these real-world challenges.

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