Agent workflows with artificial intelligence

An agentic workflow, or agent workflow, is a workflow that is autonomously executed and operated by an agent.

What is an agent workflow

A new type of software program in which artificial intelligence-enabled agents autonomously iterate on tasks in a workflow or entire workflows. It expands on the traditional concept of rule-based workflows, allowing AI agents to autonomously perform tasks that would otherwise be human-driven or difficult.

For example, an agent workflow used in sales could act as a Sales Representative that reads incoming customer data, finds the data it needs, and creates a personalized contact or reply message to engage the lead. In recruiting, an AI could analyze uploaded resumes, compare them to the job description of an open position, and draft a message to send to qualified candidates.

Similarly, you can create many agents to autonomously execute a variety of tasks, including marketing, operations, customer support, development, data, and more.

Creating an agent used to require combining your own technology stack with artificial intelligence, developing step-by-step objectives and outcomes, and more. Now, innovative tools like Outcode are making it easier to create agent workflows.

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Enterprise trends in agent workflows

MANY GLOBAL ORGANIZATIONS ARE NOW CREATING AI AGENT WORKFLOWS TO IMPROVE PRODUCTIVITY AND STREAMLINE OPERATIONS. KEY OBJECTIVES INCLUDE

• Autonomous operations: Agents optimize operations, sometimes performing the entire process with no or minimal human intervention.
• Personalization at scale: Agents are delivering personalized experiences to tens of thousands of users, or actively leveraging agents in sales and marketing.
• Data-driven operations: Agents analyze the myriad of data generated by enterprise operations, summarizing, extracting, and generating insights to communicate or drive improvements.

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Structure and functionality of agent workflows

Agents are structured to run your business operations seamlessly and autonomously to maximize productivity and efficiency.

• Platform structure: The foundation on which agents operate. It ensures that the many AI agents developed across the web are always up and running, and allows users to create AI-powered workflows.
• Powerful integrations: Agents need robust data integration capabilities because AI is data-driven and autonomous. Provide the ability to integrate data from databases to enterprise applications.
• AI-Native Task: In an agent workflow, there are many tasks that AI can perform autonomously. For example, data extraction, summarization, creation, merging, deduplication, and more, as well as reflecting business logic.

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WHAT DO AI AGENTS MEAN FOR MEMBERS?

Agents need direction from a human - an architect - to execute and complete workflows. You create, iterate on, and improve the agents your team and company needs to work.

In other words, members create agents and delegate tasks that would otherwise have to be done by humans, freeing them up to focus on more important tasks and decisions.

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WHAT IS THE DIFFERENCE BETWEEN AN AI AGENT AND AN AI CHATBOT?

AI agents and AI chatbots have different purposes and capabilities. Chatbots, or assistants, interact with humans to help them learn, extract, and generate information that is difficult for humans to find.

Agents are created to complete workflows or tasks autonomously. The main difference is that they can complete tasks autonomously. Chatbots are designed for conversations with humans, so they are not typically developed to make autonomous decisions and actions; their purpose is to support humans.

ON THE OTHER HAND, AI AGENTS MIGHT NOT INTERACT WITH YOU EVERY TIME. IN SOME CASES, THEY MAY BE GIVEN A SET OF TASKS BY YOU AND PERFORM THEM INDEPENDENTLY.

At the same time, they also have similarities.

• Processing to understand, analyze, summarize, extract, and create text
• Based on a large language model that generates text or code generatively
• Vector databases to better understand text input in human interactions

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Elements of an agent workflow

The biggest difference from traditional workflows or automation tools is autonomy and completeness.

• Autonomy: Agent workflows perform a sequence of actions autonomously without human intervention. They can reflect complex business logic and don't require any actual coding for specific tasks.
• Adaptability: Flexibility to respond to changes in context, new problems, or data.
• Completeness: While we successfully automate unit tasks or single tasks, we run workflows, which are business flows, end-to-end, meaning you can expect the workflow to be complete.

Agentic workflows execute tasks in a series of steps to accomplish a business goal. These innovative workflows allow artificial intelligence to autonomously perform tasks that would otherwise require human intervention, judgment, or approval.

New technologies are making it easier and easier for anyone to create these AI-powered agents.

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