In an era where everything is an agent, what is a true agent?

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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