Agentic AI workflows represent a significant evolution in automation technology. They leverage intelligent, autonomous systems to drive improved business outcomes.
Agentic AI workflows are systems where one or more autonomous AI agents dynamically execute a series of connected steps to achieve a specific goal. These agents can perceive their digital environment, make decisions and adapt to changing conditions with varying degrees of human intervention.
The primary goal of an agentic workflow is to automate complex, multistep processes, moving far beyond the scope of simple, repetitive task automation.
An agentic workflow can be powered by a single, versatile agent responsible for all steps. However, for more complex challenges, organizations often deploy multiagent workflows.
This advanced approach uses a team of specialized agents that collaborate to achieve a common goal. Each agent handles a different part of the process, much like an assembly line of experts. This allows organizations to tackle sophisticated problems that would be too difficult for a single agent to manage alone.
Explore how modern content services platforms unlock content intelligence to streamline workflows, strengthen governance and drive better business outcomes. Get practical insights on AI readiness, content intelligence adoption and workforce transition strategies.
Traditional automation is typically rule-based, linear and deterministic. It follows a predefined sequence of steps and struggles to handle unexpected situations, allowing only for a limited use of unstructured data.
In contrast, agentic workflows are dynamic, adaptive and goal-oriented. They are designed to handle complexity, make decisions and adjust their actions based on real-time data and context, making them far more powerful and flexible.
Simply using an AI model does not make a workflow agentic. A nonagentic AI workflow often involves a single, static call to a large language model (LLM) for a specific task, like summarizing text. The process is linear: Input is sent to a prompt and an output is generated.
An agentic AI workflow is an iterative process. An agent can plan, use tools and reflect on results to achieve a broader goal. It operates in a loop of continuous assessment and action, rather than a straight line.
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While often used interchangeably, these terms have a distinct meaning.
An agentic workflow refers to the series of steps an agent takes to achieve a goal.
An agentic architecture is the underlying technical framework that enables the workflow. It includes the agent itself, the tools it can use and the systems for memory and reasoning.
Agentic workflows function through the unique capabilities of the AI agents at their core. These components allow an agent to operate autonomously to solve complex problems.
Agentic workflows function through the unique capabilities of the AI agents at their core. An agent's effectiveness comes from three key components that enable specific patterns of behavior:
This is the agent's ability to "think." It manifests in key patterns that guide its actions:
An agent uses tools to interact with the real world and execute tasks. This tool use pattern allows agents to go beyond their training data by using APIs, web search functions, databases and code interpreters to get work done.
This is the agent's ability to retain information. Short-term memory stores context from the current task, while long-term memory stores knowledge across multiple sessions, allowing the agent to learn and improve its performance over time.
The classic MAPE control loop provides a simple conceptual model for how agents function autonomously. It is a continuous cycle of perception, decision-making and action.
The principles of agentic AI, particularly in multiagent systems, are being applied across industries to solve complex problems and drive new levels of efficiency.
In finance, a team of agents streamlines invoice processing. An intake agent captures an invoice, a contract agent verifies it against purchase orders and an approval agent routes it for payment, seeking human confirmation only for exceptions. This collaboration ensures accuracy and speed.
To illustrate this, let's break down how a multiagent team can autonomously process a vendor invoice from receipt to payment.
he workflow begins when an email with a PDF invoice arrives.
The structured data is passed to the next specialist agent.
The Verification Agent uses its reasoning capabilities to decide the next step.
Once an invoice is verified, it moves to the final agent for approval.
The Approval Agent completes the process based on the rules.
> Read more | How automation and AI can lead financial institutions to success
A multiagent team can automate the entire lifecycle of a support ticket. An intake agent reviews the ticket, a validation agent checks it against a knowledgebase and a communication agent interacts with the customer. The workflow only escalates to a human agent when necessary, providing a full summary and recommended actions.
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For proactive backorder management, a review agent notifies a customer, a replacement agent checks inventory for alternatives and suggests them, and a fulfilment agent coordinates shipping upon confirmation. This teamwork ensures seamless operations and keeps customers informed.
Building reliable and efficient agentic workflows requires a strategic approach. Following best practices ensures that workflows align with business objectives and deliver consistent value.
An agent needs a well-defined objective to guide its planning and actions. An ambiguous goal will lead to poor or unpredictable results.
Instead of using one generalist agent, it is often more effective to build workflows with multiple agents, each specializing in a specific task like data gathering or communications.
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Design workflows with points for human oversight, review and approval, especially for high-stakes decisions. This builds trust and ensures outcomes align with business goals.
For highly complex processes, design workflows where agents can share information and coordinate their actions. This allows them to solve problems more effectively than they could alone.
Ensure data privacy and compliance by tracking data interactions to create clear audit trails. Apply clear policies for how agents can access and use information.
Explore AIIM’s analysis of how organizations can leverage unstructured data for AI success.
Hyland provides a powerful solution for creating and deploying the agentic workflows that drive modern automation. Agent Builder enables you to design and manage enterprise-grade AI agents (Enterprise Agents) that can automate complex tasks. These Enterprise Agents can be embedded in Hyland Automate or content management workflows (such as Hyland OnBase or Hyland Alfresco), as well as other automation tools.
It connects directly to the best practices for building effective workflows.
Users can choose from a catalog of prebuilt AI agents or create highly customized agents tailored to specific tasks. The point-and-click interface allows business experts to select the underlying LLM, provide detailed instructions and define the desired output. Agent Builder then enables an iterative process of testing and reconfiguring the agent’s parameters until it consistently achieves the desired outcome.
Agent Builder is a no-code solution builder, which allows business process experts — not just developers — to build and deploy agents. This accessibility accelerates innovation and empowers those who know the processes best to improve them.
The solution enables multiagent collaboration to automatically execute complex workflows while still allowing for human supervision where needed. This aligns with the critical best practice of keeping humans in the loop.
Agent Builder provides comprehensive agent lifecycle management. It allows users to version, test, deploy, monitor and improve agents continuously to meet evolving business needs, ensuring workflows remain effective over time.
Hyland's AI-powered and AI-enabled products work together to deliver intelligent automation and unlock the full potential of enterprise content. Agent Builder can orchestrate powerful agentic workflows by leveraging the capabilities of Hyland’s AI-powered suite:
Agent Builder: An agent configuration and lifecycle management tool that enables organizations to use AI agents for specific content-driven tasks. It helps augment the human workforce and implement AI at scale.
Hyland Knowledge Discovery: An AI-powered search, information discovery and decision support application that unlocks access to relevant business insights. It uses AI agents to retrieve and generate information to accelerate decision-making.
Hyland Knowledge Enrichment: A data management tool that transforms raw, unstructured content into structured, high-quality, contextual content for AI-based automation and app development.
Hyland IDP: An AI-powered solution that automates document capture, extraction and classification to enable efficient and accurate data processing.
Hyland Automate: An AI-enabled automation solution designed to simplify complex processes, reduce manual tasks and boost operational efficiency.
Content Innovation Cloud is the future of enterprise content management. By leveraging a unified content, process and application intelligence platform, your organization can unlock profound insights from enterprise content and unstructured data — fueling innovation without disruption.
