THE CONCEPT OF "AGENTS" HAS BEEN A HOT TOPIC IN THE AI INDUSTRY LATELY. FROM LLM-BASED CHATBOTS TO SYSTEMS THAT AUTOMATE SPECIFIC TASKS, A VARIETY OF SOLUTIONS ARE EMERGING UNDER THE "AGENT" BANNER. BUT CAN A SYSTEM THAT IS SIMPLY A COMBINATION OF FEATURES BE CALLED A "TRUE AGENT"? IN THIS ARTICLE, WE'LL REDEFINE WHAT AN AGENT REALLY IS AND EXPLORE HOW OUTCODE'S "AGENTS OF THE FUTURE" CAN REVOLUTIONIZE THE WAY BUSINESSES OPERATE.

🔀 The new definition of an agent: Why AI just got smarter

Many people refer to the system of assigning roles to LLMs and connecting multiple tools as an "agent". But that doesn't capture the full potential of an agent. A true agent isn't just an automated system that works according to rules; it's an intelligent system that has the ability to make decisions, act, learn, and achieve its goals in real-time and in response to the business environment.

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💬 A gents, more than just agents

Agents are no longer just automation tools - true agents are partners thatdeeply understand business goals, adapt to environmental changes, and make optimal decisions on their ownin unpredictable situations. Outcode makes this possible, empowering each department in the enterprise to make autonomous and efficient decisions.

Why Agents are needed in the enterprise

Most core tasks and processes in the enterprise are complex and dynamic, especially manufacturing and finance, which combine a variety of data and decisions that require agents to respond to real-time changes and make optimized decisions. These complex tasks are difficult for assistants to solve because they require autonomous judgment and flexible responses.

In manufacturing, production planning, materials management, quality control, and more are interconnected. These tasks rely on a variety of data sources and many variables. For example, monitoring inventory levels, production rates, machine uptime, and more in real time and making instant decisions based on them is a complex task.

What the Assistant can't fix:

• Assistants are just tools that perform actions based on set commands. For example, if you ask it to "tell me my inventory status," it will simply show you the current inventory numbers, but it can't make predictions and adjustments to account for fluctuating inventory demand or changes in production schedules.

The role of the agent:

Agents, on the other hand, are responsible for analyzing machine status, inventory levels, order demand, and more on the production line in real time and making predictions and decisions based on that information. For example, they can proactively react by automatically executing a reorder when an inventory shortage is expected, or immediately switching to a replacement machine when a machine breaks down. Agents autonomously take the necessary actions to achieve their goals, and can proactively solve foreseeable problems.

Finance departments require precise calculations and a variety of decisions, including budget management, expense tracking, and financial reporting. Financial data is not just numbers, but is affected by a variety of external variables (e.g., interest rate fluctuations, currency exchange rates, government policies) and internal changes (e.g., departmental budget usage). In this dynamic and changing environment, an agent's proactive role is essential for accurate financial forecasting and resource allocation.

Agents act autonomously, automatically analyzing the causes of budget overruns, suggesting ways to reduce costs, or adjusting future budget plans. For example, by monitoring project-specific expenses, they can automatically detect higher-than-expected spending and proactively manage the situation by sending notifications to the appropriate departments and suggesting alternative paths. They can also reflect real-time changes in interest rates or currency exchange rates to suggest budget rebalancing and help you make strategic decisions.

Agents for dynamic, complex tasks

Manufacturing and finance operations are both dynamic and complex, requiring decisions based on real-time data and analytics. While assistants are limited to carrying out commands based on set rules, agents have the ability to analyze situations in real time and actively react to changing data. By enabling autonomous decision-making in an automated process, agents can achieve both efficiency and accuracy.

Agents are essential for making autonomous and efficient decisions and optimizing work in complex and dynamic work environments. The ability to analyze real-time changing environments and data, make the right decisions, and react flexibly is a key capability of agents, whether in manufacturing or finance. While assistants can only perform basic tasks, agents can recognize complex situations in real time and propose solutions.

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🧩 Technical criteria for a real agent

The key technical criteria that define a true agent are

✅ Autonomous execution based on self-loops

Rather than acting on a predetermined number of occasions or conditions, an agent determines its own goals andmoves toward them by repeating the process of Act, Observe, Plan, and Reflect. As Anthropic defines it, a system that can decide for itself when to quit is an agent.

Google DeepMind's "Observe → Plan → Act → Reflect → (Loop)" structure clearly illustrates the core mechanism of this self-loop.

• Observe: Gather and recognize information from the external environment. Process various forms of information, including sensor data, API responses, user input, and more.
• Plan: Based on the information observed, create a plan of action to achieve the goal. Various strategies and algorithms can be utilized in this process.
• Act: Perform an actual behavior based on a developed plan. Interact in a variety of ways, including API calls, database manipulation, and control of external systems.
• Reflect: Analyze and evaluate the results of an action to inform the next plan of action. This process plays an important role in the agent's learning and improvement.
• (Loop): Repeat the above process until the goal is achieved. The experience gained in each iteration phase improves the agent's judgment and efficiency.

These self-loops allow agents to flexibly respond to unpredictable situations, learn from trial and error, and continually evolve toward long-term goals.

Assistant Systems :

When an accounting person asks, "Tell me if this month's spending is over budget," the traditional system follows these steps

1. View spending history for your department or overall
2. Compare to budget data
3. Communicate simple numbers like "over $100" or "normal"

This approach does not support judgment-based decision-making, such as identifying the cause of overages, predicting future spending trends, or suggesting adjustments.

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A true agent-based system:

If an accounting person asks, "Why did we go over budget this month, and how should we adjust for next month?", the agent works like this

Observe:

• Ask questions to learn that users want cause analysis + suggestions for future adjustments

Plan:

• Breakdown of spend by department (regular vs. unusual spend)
• Identify drivers of month-over-month and year-over-year spend growth
• Determine if costs are concentrated in specific projects
• Breakdown of labor, fixed, and variable costs by line item
• Projected income and fixed expenses for the next month
• Simulate the effects of different spend adjustment scenarios

Act:

• Explain that the cause is "unusual outsourcing expenses of $300K"
• "Propose a 15% adjustment to next month's meeting fees and marketing budget"
• Automatically generate budget sheets and reports based on reconciliations

Reflect:

• Record user choices and reactions to improve future responses
• Proactively perform "spend anomaly detection and suggestions" on the fly in similar situations

Loop:

• Real-time alerts for future increases in spending on specific items
• Learn about recurring types of budget overruns to refine your response

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✅ 'Judgment' skills beyond tool literacy

Agents need more than just the ability to call APIs or use external tools; they need the judgment to decide which tools to use, when to use them, and how to usethem. It's also important to be able to modify your strategy based on interim results and find solutions to unexpected problems.

FOR EXAMPLE, FOR THE SIMPLE REQUEST "TELL ME THE WEATHER IN SEOUL TODAY", A SYSTEM THAT SIMPLY CALLS A WEATHER API AND DISPLAYS THE RESULTS IS NOT AN AGENT. HOWEVER, A SYSTEM THAT KNOWS WHERE YOU ARE, PROVIDES YOU WITH REAL-TIME WEATHER INFORMATION, AND EVEN GIVES YOU ADVICE ON HOW TO DRESS, IS CHARACTERIZED AS AN AGENT THAT "JUDGES" ITS SURROUNDINGS AND "ACTS" ACCORDINGLY.

💬 Agents are more than just a combination of features, they're a "philosophy of execution

At the end of the day, an agent is not a collection of tools that perform a specific function, but rather a philosophy of execution : an autonomous system that makes its own decisions and acts to achieve its goals. It's a fundamentally different concept than a simple automated system or a bot that follows a set of rules.

A true agent can be an intelligent collaborative partnerthat understands your business goals and navigates itself through complex and changing environments.

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🚀 Outcode: A revolutionary platform for building agents

More than just a functional automation tool, Outcode provides an end-to-end platform for organizations to design, build, and operate their own agents. Outcode enables "real agents" with the following key technology elements

✅ Intelligent agent collaboration based on Multi-Agent Communication Protocol (MCP)

Outcode uses the Multi-Agent Communication Protocol (MCP) to enable multiple agents to work together organically to effectively achieve business goals.

MCP is a way for agents to work independently but collaborate toward a common goal. Each agent has expertise in its area of responsibility and exchanges information with other agents to work collaboratively to automate complex business processes. For example, different agents in sales, inventory management, customer support, and more interact to optimize their tasks and work efficiently toward business goals.

This approach goes beyond the limitations of traditional single-agent systems, allowing agents to collaborate with each other to increase the speed and accuracy of problem solving and maximize synergies between tasks. Agents with different areas of expertise work together to create an environment where faster, more accurate decisionscan be made.

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✅ Unified management based on the Orchestration Framework

Outcode provides a unified orchestration structure that allows you to organically connect and manage a variety of tools, data, and policies with your agents. This structure, which enables seamless integration with your existing IT infrastructure, is essential for building and operating an agent system that is optimized for your business environment. Outcode integrates various APIs, databases, cloud services, external systems, and more, allowing you to monitor andcontrol the activities and data of all your agents in one placethrough a centralized management system. This orchestration structure also allows you to operate your agents in an optimized way while leveraging your existing IT environment. For example, you can apply customized AI agents to different departments, such as finance, customer service, and logistics, and automate interactions between them to increase the efficiency and accuracy of your business processes. Outcode provides a centralized, bird's-eye view of workflow integration and management, making complex tasks seamless.

At Outcode, we don't just automate individual tasks, we focus on empowering agents with the autonomy and judgment they really need to move toward business goals.

📌 Bottom line: the name Agent is not enough.

"Can your system think for itself?"

A system that can answer "yes" to this question is a true agent, and that's what Outcode is all about. Outcode empowers organizations to transform business processes with autonomous AI agents and lead the business of the future with AI-powered autonomous operations.

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Retrieval-Augmented Generation (RAG) systems are a technology that utilizes text embeddings to build recommendation systems. It goes beyond simple search to find and provide semantically relevant information, and combines with LLM to generate more natural and useful answers.

RECENT ADVANCES IN AI TECHNOLOGY ARE MOVING SEARCH-BASED SYSTEMS AWAY FROM SIMPLE KEYWORD MATCHING AND TOWARD RECOMMENDING INFORMATION BASED ON SEMANTIC UNDERSTANDING. IT'S IMPORTANT TO REFLECT THE USER'S CONTEXT AND CREATE SEARCHES AND RESPONSES THAT ARE CONTEXTUALIZED, NOT JUST INFORMATIONAL.

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Inference complexity and purposefulness

The system becomes more complex when you want more complex reasoning. For example, the search and reasoning process will be different depending on whether the user wants contract information or the history of contract changes. If you don't account for these differences, performance can suffer.

ADDITIONALLY, WITHOUT METADATA, IT'S DIFFICULT TO PROVIDE APPROPRIATE RESPONSES. IF THE AI CAN'T INFER A DOCUMENT'S REVISION HISTORY OR CURRENT STATE, IT CAN REDUCE THE ACCURACY OF ITS ANSWERS.

Additionally, a large amount of information does not guarantee that it contains the information you need; in fact, a large amount of unnecessary information can make searching and reasoning more difficult.

SUMMARIZING IS ALSO AN INTERESTING ENDEAVOR. A GOOD SUMMARY CAN BE A WAY TO INCLUDE ENTITIES, MAINTAIN AN APPROPRIATE LENGTH, CONVEY NUANCE, AND EFFECTIVELY CONDENSE AND CONVEY THE NECESSARY INFORMATION. FOR A RAG SYSTEM TO WORK EFFECTIVELY, THE QUALITY OF SUMMARIES IS IMPORTANT, AND IT'S NOT JUST ABOUT CONDENSING INFORMATION, BUT ABOUT GETTING TO THE POINT WHILE PRESERVING MEANING.

ANOTHER EXAMPLE MIGHT BE GENERATING A FULL SUMMARY OF MEETING MINUTES AND ACTION ITEMS. IN SOME CASES, THE USER MIGHT WANT A SHORTER LIST OF ACTIONS, IN WHICH CASE A STRATEGY MIGHT BE TO SPLIT THE TASK AND GENERATE THE SUMMARY AND ACTION ITEMS SEPARATELY. THIS MEANS THAT AI SHOULDN'T JUST SUMMARIZE INFORMATION, BUT DELIVER RESULTS IN DIFFERENT FORMS TO SUIT THE USER'S PURPOSE.

When it comes to fine-tuning, you can use specialized tools to create and train thousands of examples, but you may find it more effective to take a step-by-step approach. It's important to take a step-by-step approach to improve your model's generalization performance and efficiently organize your training data for specific purposes.

You may also need to use Re-ranker because the documents retrieved may not necessarily match your intent exactly. Re-rankers re-evaluate the relevance of documents after the initial search and place the best information at the top. This helps the RAG system generate answers based on more accurate information.

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Difficulty collecting and storing data

THE KEY TO A RAG SYSTEM IS COLLECTING AND PROPERLY MANAGING RELIABLE DATA.

• You need a regular data update process to keep your information fresh.
• Without metadata, it can be difficult to infer the current state of a document.
• Just because there's a lot of data doesn't mean it necessarily contains the information you need.
• You need an efficient data management strategy, including chunking strategies, diverse data sources, and utilizing streaming data.

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Challenges of performance evaluation and continuous improvement

YOU SHOULD EVALUATE THE PERFORMANCE OF YOUR RAG SYSTEM AND CONTINUOUSLY IMPROVE IT, BUT THIS REQUIRES A LOT OF EFFORT.

• You may need a test dataset and an Eval mechanism to evaluate whether the retrieved documents are appropriate.
• Reflect user feedback to improve search and response quality.
• INCOMPLETE DATA MAKES IT DIFFICULT FOR AI TO GENERATE RELIABLE RESPONSES.
• It's even more important that it contains accurate information that is fit for purpose.

WHILE RAG SYSTEMS SEEM SIMPLE IN CONCEPT, THERE ARE MANY COMPLEXITIES IN THE ACTUAL DEPLOYMENT AND OPERATION. FROM DATA COLLECTION TO SEARCH, RESPONSE GENERATION, AND PERFORMANCE EVALUATION, THERE ARE TECHNICAL AND OPERATIONAL CHALLENGES. WHERE SPECIALIZED DOMAIN KNOWLEDGE IS REQUIRED, HUMAN INTERVENTION MAY BE NECESSARY TO COMPENSATE FOR THE LIMITATIONS OF AI.

IN ADDITION, DURING SUMMARIZATION AND DATA PROCESSING, YOU MAY NEED A STRATEGY TO SEPARATE TASKS BASED ON USER NEEDS. FOR EXAMPLE, WHEN GENERATING A SUMMARY OF MEETING MINUTES, YOU MAY NEED THE ABILITY TO GENERATE ACTION LISTS SEPARATELY, AND THIS STRUCTURED APPROACH CAN CONTRIBUTE TO EFFECTIVE RAG SYSTEM OPERATION.

When fine-tuning, it's important to take a step-by-step approach to building more sophisticated models and achieving optimal performance, rather than simply training on large amounts of data.

IN THE FUTURE, MORE SOPHISTICATED RAG SYSTEMS SHOULD BE DEVELOPED THROUGH CONTINUOUS IMPROVEMENT AND OPTIMIZATION.

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With AI technology advancing so rapidly these days, many organizations are adopting AI Agents, but that's not the end of the story - the concept of Agentic Workflows is becoming more important.

🚀 Is a simple AI agent enough?
👉 Individual tasks like simple customer interaction, data analysis, and automatic document generation can be done by an AI agent.
👉 But when you introduce the concept of "workflows" where multiple AIs work together organically and automatically? It' s a whole different game!

Today, we're going to demystify the difference between AI agents and agent workflows.

1. What is an AI Agent?

Think of AI agents as performing specific tasks .

• Chatbots to interpret customer inquiries and answer refund policies
• Automatic summarization AI to summarize long documents
• AI research tools that analyze data to extract insights

🔹 HOW AI AGENTS WORK

💬 User input → 🎯 AI model analyzes → 📤 Results output

IT TAKES A SINGLE INPUT, PROCESSES IT, AND GIVES YOU AN ANSWER.

👉 In a nutshell, you take an AI model and tell it to do something!

2. What is an Agentic Workflow?

It's a structure where multiple AI agents work together to automatically handle more complex tasks.

🔍 Let's take an example.

• A customer asks a question on your online store → chatbot responds first → checks payment → AI agent checks order status → automatically processes refund request!
• Financial analytics AI understands your needs and business objectives → Understands if you want a report, sensitivity analysis, etc.

This is what we call agent workflows, where each AI doesn't just play a single role, but works together organically.

🔹 How agent workflows work

📩 Input → 🏗 Task distribution (inference-based orchestration ) → 🤖 Each AI agent performs its role → 📊 End result

👉 In short: a system where AIs collaborate with each other, exchange data, and divide roles!

🤔 The easy way to organize?

• An AI agent is asystem in which a single AI works alone
• Agent workflows aresystems where multiple AIs work together to automatically handle more complex tasks.

CONCLUSION: AI AGENTS ARE NOT EVERYTHING!

As AI technology evolves, it's no longer enough to have a simple AI chatbot.
Now, the concept of "workflows" where AIs work in concert with each other is essential.

The autonomy and flexibility of agent workflows is essentialbecause real-world company tasks are not just questions and answers, but involve complex decisions and multi-step processes. A single AI agent will only go so far, and a system where multiple AIs work together to automatically coordinate tasks and find optimal solutionswill determine your competitive edge!

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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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