Don't let perfection stop progress when developing AI agents

Don't let perfection stop progress when developing AI agents The agent purist’s dilemma Progress over perfection = allow function over form API-wrapping agents Physical device agents Event-driven agents Data aggregation agents Chatbot orchestrators Specialized functional agents Why this matters Wrapping up How to get started Get started with AI Inference About the author Richard Naszcyniec More like this Blog post Blog post Blog post Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share The AI revolution has ignited a debate about what constitutes an " AI agent. " Using the term “AI agent” these days commonly implies autonomous, self-learning systems that pursue complex goals, adapting over time. A very impressive goal, but this purist vision can alienate traditional developers and slow innovation. It’s time to expand the definition, and embrace a broader perspective: AI agents don’t always need to self-learn or chase lofty goals. Functional agents —a new term—that connect large language models (LLMs) to APIs, physical devices, or event-driven systems can be just as impactful. By prioritizing function over form, we enable a broader pool of developers to engage in building AI agents, empower both AI and traditional developers to collaborate, and build practical solutions that drive real-world value. Let’s make progress without always demanding perfection. The traditional definition of an AI agent—rooted in significant AI research—demands autonomy, reasoning, learning, and goal-oriented behavior. These agents, like those powering autonomous vehicles or reinforcement learning models, are impressive but complex. They require deep expertise in machine learning (ML), which can feel like a barrier to traditional developers skilled in APIs, databases, or event-driven architectures. This purist stance risks gatekeeping, sidelining practical agents that don’t learn, but still solve critical problems. Why should an agent that wraps an API call or responds to a sensor be considered inferior? Not every challenge needs a self-evolving neural network—sometimes, a reliable, lightweight solution is enough.