Agentic workflows are AI-driven systems designed to reason, plan, and act autonomously across complex tasks and environments. Unlike prompt-based interactions or deterministic automation, they coordinate multiple AI agents, tools, and feedback loops to achieve high-level goals under uncertainty.
This article introduces the core components of an agentic workflow, explains how autonomous AI agents operate within them, and clarifies when agentic approaches are appropriate for production AI systems.
Imagine you are planning a vacation to Thailand. Like any vacation plans, you need to make sure many things are set beforehand: flight tickets, accommodation, and eventually a great itinerary of places to eat or sightsee. There are two approaches you could take: spending hours opening countless browser tabs and spreadsheets to set everything up. Or, using a travel application powered by an agentic workflow.
This article explains what agentic workflows are, how autonomous AI agents operate within them, and how to evaluate agentic AI frameworks depending on your project’s constraints, scale, and ecosystem. Agentic workflows are a core building block for modern AI agent workflows, enabling autonomous AI agents to reason, plan, and act across complex digital ecosystems.
In practice, the “right” agentic workflow isn’t decided by hype, it’s decided by constraints: reliability, security, latency, cost, and integration with your existing stack. These constraints become especially important in enterprise AI systems, where workflow orchestration, cost management, and observability directly impact production performance.
At ML6, we help teams design agentic workflows that move from prototype to production, working across leading AI models, cloud platforms, and enterprise ecosystems.
We collaborate closely with technology partners to ensure agentic workflows meet production-grade requirements around governance, observability, security, and scalability.
An agentic workflow is a process orchestrated by one or more AI agents, which are autonomous software entities capable of perception, decision-making, and action within a defined environment. These workflows are characterized by their ability to dynamically adapt their behavior in response to real-time data, feedback, and evolving conditions.
At the core of an agentic workflow is the AI agent itself. Broadly speaking, an AI agent is a system capable of performing tasks autonomously on behalf of a user or another rule-based system. These agents are typically powered by large language models (LLMs), enabling advanced reasoning, natural language understanding, and generation.
To perform complex tasks reliably, LLMs are often augmented with additional capabilities such as retrieval, tools, and memory. Depending on the task, an agent may:
Another key component is the Planning & Reasoning Engine. In most real-world applications, users provide a single prompt that represents a complex goal rather than a step-by-step instruction.
The planning component allows agents to decompose a high-level objective into smaller, manageable steps using structured reasoning techniques. Approaches inspired by Chain-of-Thought (CoT) prompting guide the agent to ‘think step-by-step’, highlighting an explicit internal monologue that breaks the complex task down into a sequence of logical, manageable sub-tasks.
As a result, agents can:
More advanced agentic AI systems also incorporate feedback mechanisms, either from users (human-in-the-loop/HITL) or from the environment itself, allowing continuous improvement over time.
To make this more concrete, let’s return to the travel application example and see how an agentic workflow actually helps you prepare for your best Thailand experience. The figure below should give you a big picture on what an agentic workflow could look like.
A simplified example of an agentic workflow used for a travel application.
First, you enter a single, complex prompt:
“Plan a 10-day trip to Thailand for two people in April, focusing on cultural experiences and great food, with a total budget of $5,000.”
Behind the scenes, here’s what happens:
The moment you submit your request, the Planning & Reasoning Engine works on understanding the high-level goal and breaks it down into a series of smaller, manageable subtasks for its team of agents:
The engine now dispatches specialist AI Agents to execute these tasks. These agents are powered by LLMs, allowing them to understand nuance and perform complex actions.
In the end, the system presents a complete, day-to-day itinerary that meets your wishes and is within the budget. The system asks for your feedback to improve the travel plan. You might say:
“I’d prefer to visit the floating market on a weekday to avoid the biggest crowds.”
The engine processes the new prompt, and prepares a new travel plan for you. This intelligent, and adaptive process highlights the power of an agentic workflow.
Instead of executing predefined steps, the system continuously reasons about goals, constraints, and feedback, adjusting its behavior as conditions change. This shift, from static automation to dynamic, goal-driven orchestration, is what enables agentic workflows to operate reliably in real-world, production environments.
Coming Next: When do you actually need an agentic workflow and how do you choose the right framework?