What are Agentic Workflows?

The rise of AI has shifted the paradigm of automation, moving beyond simple task execution to intelligent, autonomous decision-making. Traditional workflows, while effective for predefined sequences, struggle with dynamic, unpredictable environments. This limitation has paved the way for agentic workflows, a new frontier where AI agents perceive, reason, and act with minimal human intervention.

This article dives deep into agentic workflows, defining their core concepts, distinguishing them from traditional methods, and exploring their practical applications. We’ll examine how leading organizations like IBM and GitHub are leveraging this technology, and how Kestra provides a declarative platform to build, manage, and scale these intelligent automation systems.

Understanding Agentic Workflows: A Comprehensive Guide

What are Agentic Workflows?

Agentic workflows are advanced, AI-driven processes where autonomous software agents execute complex tasks, make decisions, and coordinate actions with minimal human oversight. Unlike traditional automation that follows a rigid, predefined path, agentic workflows empower AI agents to reason, plan, and use tools to achieve a specific goal. These agents can interact with their environment, process information, and adapt their strategy based on real-time data.

The core of an agentic workflow is the AI agent itself, which is designed to be proactive and goal-oriented. It breaks down high-level objectives into smaller, manageable steps, selects the appropriate tools for each step, and executes them in a logical sequence. This enables a level of automation that can handle ambiguity and complexity far beyond the scope of conventional scripts or rule-based systems. For a deeper look into building these systems, explore our AI automation resources. You can also browse our collection of AI orchestration resources for more insights.

Agentic vs. Non-Agentic Workflows: Key Differences

The primary distinction between agentic and non-agentic workflows lies in their autonomy and adaptability.

Non-agentic workflows are deterministic and rule-based. They follow a strict sequence of operations defined by a human. Each step is explicitly coded, and the workflow cannot deviate from its programmed path. Think of a standard ETL pipeline: data is extracted, transformed, and loaded according to a fixed schedule and logic. While reliable for repetitive tasks, these workflows are brittle; an unexpected change in data format or an API failure can cause the entire process to fail without manual intervention. These are typically defined by workflow components and initiated by triggers.

Agentic workflows, in contrast, are dynamic and goal-driven. The focus is on the “what” rather than the “how.” You provide the AI agent with an objective, and it determines the best course of action. If it encounters an obstacle, it can reason about the problem, try alternative solutions, or even use tools to gather more information before proceeding. This makes them resilient and capable of operating in complex, changing environments with little to no human input.

The 4 Stages of Agentic AI Development

An agentic AI system typically operates in a continuous loop, often described in four distinct stages:

1. Perceive: The agent gathers information from its environment. This can include data from APIs, files, databases, user inputs, or sensor readings. This stage is about building a current understanding of the state of the world relevant to its task.
2. Reason: Based on the perceived information and its overarching goal, the agent analyzes the situation and formulates a plan. This involves breaking down the goal into sub-tasks, evaluating potential strategies, and deciding on the next immediate action.
3. Act: The agent executes the chosen action. This often involves using a “tool,” which could be anything from calling an API, running a script, querying a database, or interacting with another software system. The action alters the state of the environment.
4. Learn: The agent observes the outcome of its action and updates its understanding. This feedback loop allows the agent to refine its strategy over time, improving its performance and adapting to new information. This cycle repeats until the final goal is achieved. Kestra’s architecture for AI agents is built around this powerful loop.

The Role of AI Agents in Modern Workflows

How AI Agents Automate Tasks and Decision-Making

AI agents are transforming automation by taking on tasks that previously required human cognition. They move beyond simple “if-this-then-that” logic to handle complex, multi-step processes that involve judgment and problem-solving. For example, an agent tasked with “resolving a customer support ticket” could autonomously read the ticket, query internal knowledge bases, access customer data, formulate a response, and update the CRM, all without a human operator guiding each step.

This capability is powered by Large Language Models (LLMs) combined with a structured framework that allows them to plan and execute. The LLM provides the reasoning engine, while the framework gives it access to tools and memory, enabling it to interact with the outside world and maintain context. This creates a system that can not only execute but also orchestrate, making decisions about which tasks to run, in what order, and how to handle exceptions. You can learn more about this in our blog post introducing AI agents.

Is ChatGPT an Agentic AI?

This is a common point of confusion. While ChatGPT is a powerful generative AI model, it is not, by itself, an agentic AI. ChatGPT excels at generating human-like text, code, and other content based on a user’s prompt. However, it is primarily reactive; it responds to input but does not autonomously take actions to achieve a goal in an external environment.

An agentic AI integrates a model like ChatGPT as its “brain” but adds critical components like planning capabilities, tool usage, and memory. It’s the difference between a writer who can draft an email (ChatGPT) and an executive assistant who can read a request, decide an email needs to be sent, draft it, find the recipient’s contact information, send it, and schedule a follow-up (agentic AI). Kestra’s AI Copilot uses generative AI to help you build workflows, while Kestra’s AI Agents execute them autonomously.

Core Patterns and Components of Agentic Systems

Effective agentic systems are built from several key components working in concert:

• Planning: The agent’s ability to decompose a high-level goal into a sequence of executable steps. This might involve techniques like Chain of Thought (CoT) or Tree of Thoughts.
• Memory: Agents need memory to maintain context. This includes short-term memory for the current task (like a scratchpad) and long-term memory for retaining information across tasks, often stored in a vector database or a simple key-value store.
• Tools: These are the functions or APIs that allow the agent to interact with the world. A tool could be a web search API, a database query function, or the ability to execute code in various programming languages. The agent must be able to select the right tool for a given sub-task.
• Reflection: The ability for an agent to critique its own work and learn from its mistakes. After generating a plan or taking an action, a reflection step can help identify flaws and improve future performance.

Practical Applications and Use Cases of Agentic Workflows

Reinventing Marketing Workflows with Agentic AI

In marketing, agentic AI can automate and optimize campaigns at a scale previously unimaginable. An agent could be tasked with “launching a new product campaign.” It could then autonomously conduct market research via web searches, analyze competitor positioning, generate ad copy and visuals, set up campaigns across multiple platforms, monitor their performance, and reallocate budget based on real-time results. This accelerates the entire campaign lifecycle and enables hyper-personalization by adapting messaging based on customer segment performance. This is particularly powerful when integrated with existing data pipelines.

Agentic Workflows in Software Development and Coding

For software engineers, agentic workflows are beginning to automate parts of the development lifecycle. An agent can be given a feature request, and it can then write the code, create unit tests, run them, and even open a pull request. If the tests fail, it can attempt to debug and fix the code itself. This paradigm extends to the entire CI/CD pipeline, where agents can manage deployments, monitor for issues, and automatically roll back if anomalies are detected.

Automating Repository Tasks with GitHub Agentic Workflows

GitHub is a prime example of a platform embracing agentic workflows. Their “GitHub Agentic Workflows” initiative allows developers to automate repository management using natural language. For instance, you could create a workflow that instructs an agent to “triage new issues by adding the ‘bug’ label if the description contains the word ‘error’, and assign it to the on-call engineer.” The agent can interact with the GitHub API to create issues, add labels, and manage projects. This offloads repetitive administrative tasks, allowing developers to focus on coding after they clone a repository.

Kestra’s Approach to Agentic Workflows

Declarative Orchestration for AI Agents

Building reliable agentic workflows requires a robust orchestration layer. Kestra provides this through a declarative, YAML-based approach. Instead of writing complex, imperative code to manage an agent’s lifecycle, you define the agent’s goals, tools, and constraints in a simple, auditable flow definition.

This declarative model serves as a critical set of guardrails. It ensures that even as the AI agent makes autonomous decisions, its actions are confined within a version-controlled, observable, and secure environment. This answers a key question in enterprise AI: how to grant autonomy without sacrificing control. For more on why Kestra is built this way, see our documentation.

Kestra’s AI Agents: Tools, Memory, and Human-in-the-Loop

Kestra’s platform includes native support for building and running AI agents. It provides a framework where agents can be equipped with tools, memory, and a reasoning engine.

• Tools: Any of Kestra’s 1,200+ plugins can be exposed to an AI agent as a tool. This gives the agent the ability to interact with virtually any system, from databases and cloud services to messaging platforms and business applications.
• Memory: Agents can use Kestra’s built-in KV Store to maintain state and persist information across executions, enabling long-term memory and learning.
• Human-in-the-Loop: For critical operations, Kestra allows you to insert a pause for human approval before an agent takes a sensitive action. This ensures that a human expert can verify the agent’s plan, providing a crucial safety layer.

This powerful combination is already in use at scale. For example, Apple’s ML team orchestrates large-scale data pipelines with Kestra, demonstrating its capability in complex, production AI environments.

Leveraging Kestra Copilot for Agentic Workflow Creation

Defining agentic workflows, while declarative, can still be complex. Kestra’s AI Copilot accelerates this process by translating natural language descriptions into fully-formed YAML workflows. A developer can simply describe the desired agentic process, and the Copilot will generate the necessary flow definition, including task configurations and agent settings. This lowers the barrier to entry and allows teams to rapidly prototype and deploy agentic automation, often starting from pre-built blueprints.

Benefits and Challenges of Agentic AI Implementation

Accelerating Campaigns and Personalization at Scale

The primary benefit of agentic AI is a massive increase in efficiency and adaptability. By automating decision-making, organizations can execute complex processes faster and more consistently than human teams. In marketing, this means faster campaign launches. In operations, it means quicker incident response. This speed, combined with the agent’s ability to process vast amounts of data, enables personalization and optimization at a granular level that is impossible to achieve manually. The performance benchmarks for orchestrated systems show significant gains.

Ensuring Guardrails and Controlled Environments for AI Agents

The greatest challenge of agentic AI is managing its autonomy. Ungoverned agents can produce unexpected or undesirable outcomes. Implementing strong guardrails is essential. This includes:

• Strict Access Control: Limiting the tools and data an agent can access.
• Auditability: Maintaining a clear, immutable record of every decision and action the agent takes. Kestra’s flow revisions and audit logs provide this visibility.
• Human Oversight: Incorporating human-in-the-loop approval steps for actions that have significant business impact.
• Controlled Environments: Testing agentic workflows thoroughly in sandboxed environments before deploying to production.

What Are the Four Types of Workflows?

While agentic workflows represent a new paradigm, they often build upon or interact with traditional workflow patterns. Understanding these foundational types helps place agentic systems in context:

1. Sequential Workflows: The simplest type, where tasks are executed one after another in a linear path.
2. State Machine Workflows: These are event-driven and move between different “states” based on external events or the completion of tasks. They are well-suited for processes with long-running steps or those that require waiting for external input.
3. Rules-Driven Workflows: These workflows use a rules engine to dynamically determine the next step based on business logic or data. The path is not fixed but changes based on conditions.
4. Parallel Workflows: These workflows execute multiple tasks simultaneously (fan-out) and often wait for all parallel branches to complete before proceeding (fan-in).

Agentic workflows can be seen as a highly advanced form of a rules-driven or state machine workflow, where the “rules” and “state transitions” are determined by an intelligent LLM rather than a predefined set of conditions. Kestra’s flowable tasks provide the building blocks to construct any of these patterns, offering a flexible foundation for both traditional and agentic automation.