Agentic AI Runs on Tools (simplicityissota.substack.com)

Agentic AI—systems that autonomously pursue goals through planning, multi-step reasoning, memory, and interaction with the external world—is gaining traction as a transformative concept in AI/ML. Unlike traditional chatbots that rely heavily on tight human oversight, agentic AI can dynamically manage tasks over extended loops with minimal human intervention, exemplified by coding agents that generate, test, and debug code independently. This shift marks a significant step toward more autonomous AI, where trust and control expand beyond simple text outputs to real-world actions. A core enabler of agentic AI is the integration of external tools via function calling, a paradigm where AI models invoke defined functions to access capabilities like up-to-date information, calculators, or web services. This approach overcomes limitations such as data cutoff and computational accuracy by letting language models interface with precise and current resources. Modern LLMs already demonstrate strong proficiency in correctly calling functions—with accuracy rates around 80% on out-of-sample tests—allowing them to orchestrate complex workflows by chaining function calls. Emerging frameworks like MCP and A2A facilitate standardized API discovery and interaction, embedding function calling as the foundational protocol for tool use in agentic systems. As the field matures, improved function calling accuracy combined with robust quality control will enhance agentic AI’s reliability and applicability across domains. While the term “agentic” might feel overused, the substance behind it—tools-enabled AI agents capable of goal-directed autonomous behavior—promises to become a central paradigm in building more capable, trustworthy, and interactive AI systems. The evolution from toolless LLMs toward functionally empowered agentic AI heralds a new era where AI not only advises but actively executes and adapts in the world.