AI agents are easy to get excited about.
A demo agent that plans, calls tools, and produces a coherent answer feels like a glimpse of the future. You wire together an LLM, a few tools, maybe some memory, and it works. At least at first.
The gap shows up when you try to run agents as part of a real system.
In production, agents stop being single prompts and start behaving like long-lived processes. They make multiple decisions over time, depend on external systems, and operate under constraints that are not obvious in demos. Small changes in prompts or tool behavior can cascade into unexpected outcomes.
One common mistake is treating agents as if they are autonomous systems that improve simply by adding more context or rules. In reality, most agents do not learn from failure unless the surrounding system is designed to capture and act on those failures. Without that, teams end up patching behavior with guardrails and exceptions, which works until complexity grows.
Another challenge is state. Multi-step agents need to reason over past actions, partial results, and external side effects. Managing that state reliably is harder than it looks, especially when agents interact with APIs, databases, or user-facing workflows.
What tends to work better is treating agents as components inside a larger system rather than as independent entities. Clear boundaries, explicit inputs and outputs, and strong observability matter more than clever prompting. The goal is not to make agents smarter in isolation, but to make their behavior predictable and debuggable over time.
AI agents are powerful, but they are not magic. The teams that succeed with them usually focus less on autonomy and more on system design. Once you do that, agents become easier to reason about, even as they grow more capable.
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