thinkingML
Agentic AI: Systems That Decide What to Do Next
Imagine an AI system that doesn't just follow instructions; it decides how to solve a problem, chooses the tools it needs, adapts to new information, and executes a plan in real time. We're moving beyond scripted chatbots and clever autocompletes, towards systems that compose their own behaviour on the fly.
This is Agentic AI. A fundamental shift in how intelligent systems are built. These aren't just tools that answer questions; they are goal-driven, semi-autonomous systems that truly decide what to do next.
Agentic AI refers to software systems built from modular agents powered by large language models (LLMs), capable of planning, deciding, and acting to achieve complex goals. These agents are given a goal, a toolbox of functions or APIs, and the freedom to determine the best course of action.
A well-designed agent can interpret ambiguous tasks, select and invoke tools, reason over intermediate results, revise its plan, and adapt in real time, all without step-by-step instructions.
For example, a traditional customer service bot might retrieve an FAQ article. An agentic system, by contrast, could diagnose the issue, query internal databases, evaluate refund policies, and initiate a resolution, all within a single session, adapting as the conversation unfolds.
If your mind jumps to Jarvis, Alexa, or Siri, it's understandable but misleading. Those systems simulate intelligence through scripted flows. They lack autonomy, planning, and system-level awareness. Agentic AI isn't about personality or interface, it's about architecture.
So why now? Agent-based systems have existed for decades. But today's agentic AI is made possible by the reasoning capabilities of modern LLMs. These models interpret loosely-defined goals, understand tool descriptions in natural language, and generate actionable plans.
In agentic frameworks, the agent reads tool specs, builds a plan, and executes dynamically. No rules engine, no hardcoded workflow. Just reasoning, composition, and contextual adaptation.
Agentic coordination usually involves two modes: orchestration and choreography. Orchestration imposes a logical order (like a conductor), while choreography lets agents react to events independently. Used together, they offer both control and flexibility.
Many systems also maintain memory: tracking state during execution or remembering past interactions. This enables context-aware decision-making and, in some cases, learning over time through feedback or reinforcement.
The benefits are substantial: adaptability when conditions change; extensibility through modular capabilities; composability for testing and monitoring; and dynamic routing to reduce brittle control logic.
But agentic AI is not plug-and-play. Token costs, inconsistent outputs, lack of observability, and framework instability all demand engineering rigour. Without it, systems become opaque and brittle.
At thinkingML, we advise using agentic design only when it adds real value: when workflows are multi-step, dynamic, or evolving; when flexibility beats determinism; and when new capabilities must be rapidly integrated.
Our Perspective: The Dawn of Truly Adaptive AI
Agentic AI marks a pivotal practical step in the evolution of intelligent systems. It's about moving beyond what AI knows to what it can decide: transforming static automation into dynamic, goal-driven autonomy.
At thinkingML, we are at the forefront of this shift, helping organisations design and deploy agentic architectures built on robust engineering discipline. We empower businesses to harness this power responsibly, creating systems that are modular, observable, and precisely aligned with their strategic objectives.
The future of intelligent systems isn't just about bigger models; it's about smarter, more autonomous action. It's about enabling AI to choose its own path to value. Are you ready to build it?