최근 AI 산업에서 ‘에이전트’라는 개념은 뜨거운 관심을 받고 있습니다. LLM 기반의 챗봇부터 특정 작업을 자동화하는 시스템까지, 다양한 솔루션들이 '에이전트'라는 이름을 달고 등장하고 있습니다. 하지만, 단순히 기능을 조합한 시스템이 ‘진정한 에이전트’라 할 수 있을까요? 본 글에서는 에이전트의 진정한 의미를 재정립하고, 아웃코드가 제공하는 '미래형 에이전트'가 기업 운영에 어떻게 혁신적인 가치를 창출하는지 탐구합니다.

🔀 에이전트의 새로운 정의: AI가 더 스마트해진 이유

많은 사람들이 LLM에 역할을 부여하고 여러 도구를 연결한 시스템을 ‘에이전트’라고 부릅니다. 하지만 이는 에이전트의 가능성을 제대로 살리지 못한 상태입니다. 진정한 에이전트는 단순히 규칙에 따라 작동하는 자동화 시스템이 아닙니다. 에이전트는 비즈니스 환경과 실시간 상황에 맞춰 스스로 판단하고, 실행하고, 학습하며 목표를 달성하는 능력을 갖춘 지능적인 시스템이어야 합니다.

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💬 에이전트, 그 이상의 역할

에이전트는 더 이상 단순한 자동화 도구가 아닙니다. 진정한 에이전트는 비즈니스 목표를 깊이 이해하고, 환경 변화에 적응하며, 예측 불가능한 상황 속에서 스스로 최적의 결정을 내리는 파트너입니다. 아웃코드는 이를 구현할 수 있도록, 기업의 각 부서가 자율적이고 효율적인 의사결정을 내릴 수 있도록 돕습니다.

에이전트가 기업에서 필요한 이유

기업의 대부분의 핵심 업무나 프로세스는 복잡하고 동적입니다. 특히 제조와 재무 업무는 다양한 데이터와 의사결정이 결합되어 있으며, 이들 분야는 실시간 변화에 대응하고 최적화된 의사결정을 내리는 데 에이전트의 능력이 필요합니다. 어시스턴트로는 이러한 복잡한 업무를 해결하기 어려운 이유는, 자율적 판단과 유연한 대응이 요구되기 때문입니다.

제조업에서는 생산 계획, 자재 관리, 품질 관리 등 다양한 업무가 연계되어 있습니다. 이런 업무들은 다양한 데이터 소스와 수많은 변수들을 바탕으로 이루어집니다. 예를 들어, 재고 수준, 생산 속도, 기계 가동 상태 등을 실시간으로 모니터링하고, 이를 바탕으로 즉각적인 결정을 내리는 일은 매우 복잡한 작업입니다.

어시스턴트로는 해결이 어려운 점:

• 어시스턴트는 정해진 명령에 따라 작업을 수행하는 도구에 불과합니다. 예를 들어, “재고 상태 알려줘”라는 요청을 받으면, 단순히 현재 재고 수치를 보여줄 뿐, 변동하는 재고 수요나 생산 일정 변경 등을 고려한 예측 및 조정을 할 수 없습니다.

에이전트의 역할:

반면, 에이전트는 생산 라인에서의 기계 상태, 재고 현황, 주문 수요 등을 실시간으로 분석하고 이를 바탕으로 예측과 결정을 내리는 역할을 합니다. 예를 들어, 재고 부족이 예상되면 자동으로 재주문을 실행하거나, 기계 고장이 발생할 경우 즉시 대체 기계로 전환하는 등의 능동적인 대응을 할 수 있습니다. 에이전트는 목표를 달성하기 위해 필요한 조치를 자율적으로 취하며, 예측 가능한 문제를 미리 해결할 수 있습니다.

재무 부서에서는 예산 관리, 비용 추적, 재무 보고 등 정밀한 계산과 다양한 의사결정이 필요합니다. 재무 데이터는 단순히 수치만으로 끝나는 것이 아니라, 다양한 외부 변수(예: 금리 변동, 환율, 정부 정책)와 내부 변화(예: 부서별 예산 사용 현황 등)에 따라 영향을 받습니다. 이런 동적이고 변화하는 환경에서 정확한 재무 예측과 자원 배분을 하기 위해서는 에이전트의 능동적인 역할이 필수적입니다.

에이전트는 예산 초과 원인을 자동으로 분석하고, 비용 절감 방안을 제시하거나, 향후 예산 계획을 조정하는 등의 자율적 역할을 합니다. 예를 들어, 프로젝트별 비용 현황을 모니터링하면서 예상보다 초과되는 지출을 자동으로 감지하고, 해당 부서에 알림을 보내고 대체 경로를 제시하는 방식으로 상황을 능동적으로 관리합니다. 또한, 금리나 환율 변동을 실시간으로 반영하여 예산 재조정을 제안하고, 전략적인 의사결정을 도울 수 있습니다.

동적이고 복잡한 업무에 대응하는 에이전트

제조와 재무 업무는 모두 동적이고 복잡하며, 실시간 데이터와 분석을 기반으로 한 의사결정을 요구합니다. 어시스턴트는 정해진 규칙에 따라 명령을 수행하는 데 그치는 반면, 에이전트는 실시간으로 상황을 분석하고, 변동되는 데이터에 맞춰 적극적으로 대응할 수 있는 능력을 갖추고 있습니다. 자동화된 프로세스에서 자율적인 의사결정이 가능하다는 점에서 에이전트는 효율성과 정확성을 동시에 달성할 수 있습니다.

에이전트는 복잡하고 동적인 업무 환경에서 자율적이고 효율적인 의사결정을 내리고 업무를 최적화하는 데 필수적입니다. 제조나 재무 업무처럼 실시간으로 변화하는 환경과 데이터를 분석하고, 적합한 결정을 내리며 유연하게 대응하는 능력은 에이전트의 핵심 기능입니다. 어시스턴트는 기본적인 작업 수행만을 할 수 있지만, 에이전트는 복잡한 상황을 실시간으로 인식하고 해결책을 제시할 수 있습니다.

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🧩 진짜 에이전트의 기술적 기준

진짜 에이전트를 정의하는 핵심적인 기술적 기준은 다음과 같습니다.

✅ 자기 루프 (Self-Loop) 기반의 자율적 실행

에이전트는 미리 정해진 횟수나 조건에 따라 작동하는 것이 아니라, 스스로 목표 달성 여부를 판단하고 실행, 관찰, 계획, 반성(Reflect)의 과정을 반복하며 목표를 향해 나아갑니다. Anthropic의 정의처럼, 종료 시점을 스스로 결정할 수 있는 시스템이 바로 에이전트입니다.

Google DeepMind의 "Observe → Plan → Act → Reflect → (Loop)" 구조는 이러한 자기 루프의 핵심 메커니즘을 명확하게 보여줍니다.

• Observe (관찰): 외부 환경으로부터 정보를 수집하고 인식합니다. 센서 데이터, API 응답, 사용자 입력 등 다양한 형태의 정보를 처리합니다.
• Plan (계획): 관찰된 정보를 바탕으로 목표 달성을 위한 행동 계획을 수립합니다. 이 과정에서 다양한 전략과 알고리즘이 활용될 수 있습니다.
• Act (실행): 수립된 계획에 따라 실제 행동을 수행합니다. API 호출, 데이터베이스 조작, 외부 시스템 제어 등 다양한 방식으로 상호작용합니다.
• Reflect (반성): 실행 결과를 분석하고 평가하여 다음 행동 계획에 반영합니다. 이 과정은 에이전트의 학습 및 개선에 중요한 역할을 합니다.
• (Loop): 위 과정을 목표가 달성될 때까지 반복합니다. 각 반복 단계에서 얻은 경험은 에이전트의 판단 능력과 효율성을 향상시킵니다.

이러한 자기 루프는 에이전트가 예측 불가능한 상황에 유연하게 대처하고, 시행착오를 통해 스스로 학습하며, 장기적인 목표를 향해 지속적으로 발전할 수 있도록 합니다.

어시스턴트 시스템 :

회계 담당자가 "이번 달 지출이 예산을 초과했는지 알려줘"라고 요청하면, 기존 시스템은 다음 단계를 따릅니다:

1. 해당 부서 또는 전체 지출 내역 조회
2. 예산 데이터와 비교
3. “○○만원 초과” 또는 “정상”이라는 단순 수치 전달

이 방식은 초과 원인 파악, 향후 지출 추이 예측, 조정 방안 제안 같은 판단 기반의 의사결정은 지원하지 못합니다.

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진정한 에이전트 기반 시스템:

회계 담당자가 "이번 달 예산을 왜 초과했는지, 다음 달엔 어떻게 조정해야 할까?"라고 묻는다면, 에이전트는 다음과 같이 작동합니다:

관찰 (Observe):

• 질문을 통해 사용자가 원인 분석 + 미래 조정안 제안을 원한다는 점 파악

계획 (Plan):

• 부서별 지출 내역 분석 (정규 vs. 이례 지출 구분)
• 전월/전년 동기 대비 지출 증감 요인 식별
• 특정 프로젝트에서의 비용 집중 여부 확인
• 인건비, 고정비, 변동비 항목별 분석
• 다음 달 예상 수입 및 고정 지출 예측
• 지출 조정 시나리오별 효과 시뮬레이션

실행 (Act):

• “이례적 외주비 300만원 지출”이 원인임을 설명
• “다음 달 회의비 및 마케팅 예산 15% 조정 제안”
• 조정안 기반의 예산 시트 자동 생성 및 보고서 출력

반성 (Reflect):

• 사용자 선택 및 반응을 기록하여 향후 대응 개선
• 유사 상황에서 바로 ‘지출 이상 탐지 및 제안’을 사전 수행

반복 (Loop):

• 향후 특정 항목 지출 증가 시, 실시간 알림
• 반복되는 예산 초과 유형을 학습하여 대응 고도화

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✅ 도구 활용 능력 이상의 '판단' 능력

에이전트는 단순히 API를 호출하거나 외부 도구를 사용하는 기능을 넘어, 어떤 도구를 언제, 어떻게 사용할지를 스스로 결정하는 판단 능력이 핵심입니다. 중간 결과를 바탕으로 전략을 수정하고, 예상치 못한 문제 상황에 대한 해결 방안을 모색하는 능력 또한 중요합니다.

예를 들어, "오늘 서울 날씨 알려줘"라는 간단한 요청에 대해, 단순히 날씨 API를 호출하여 결과를 보여주는 것은 에이전트가 아닙니다. 하지만, 사용자의 현재 위치를 파악하고, 실시간 날씨 정보를 제공하며, 옷차림에 대한 조언까지 제공하는 시스템이라면, 이는 주변 상황을 '판단'하고 그에 맞는 '행동'을 수행하는 에이전트의 특징을 보여주는 것입니다.

💬 에이전트는 단순한 기능의 조합이 아닌 '실행 철학'

결국, 에이전트는 특정 기능을 수행하는 도구들의 모음이 아니라, 스스로 판단하고 행동하여 목표를 달성하는 자율적인 시스템이라는 실행 철학에 기반합니다. 단순한 자동화 시스템이나 정해진 규칙에 따라 움직이는 봇과는 근본적으로 다른 개념입니다.

진정한 에이전트는 비즈니스 목표를 이해하고, 복잡하고 변화하는 환경 속에서 스스로 길을 찾아 나아가는 지능적인 협업 파트너가 될 수 있습니다.

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🚀 Outcode: 에이전트 구축을 위한 혁신적인 플랫폼

Outcode는 단순한 기능 자동화 툴을 넘어, 기업이 자체적으로 에이전트를 설계, 구축, 운영할 수 있는 엔드-투-엔드 플랫폼을 제공합니다. Outcode는 다음과 같은 핵심 기술 요소를 통해 '진짜 에이전트' 구현을 지원합니다.

✅ MCP (Multi-Agent Communication Protocol) 기반의 지능적인 에이전트 협업

Outcode는 MCP (Multi-Agent Communication Protocol)를 통해 여러 에이전트들이 유기적으로 협력하며 비즈니스 목표를 효과적으로 달성할 수 있도록 지원합니다.

MCP는 각 에이전트가 독립적으로 작업을 수행하면서도, 공동의 목표를 향해 협력하는 방식입니다. 각 에이전트는 자신이 맡은 분야에 대한 전문성을 가지고 있으며, 다른 에이전트와 정보를 교환하여 협력적인 작업을 통해 복잡한 비즈니스 프로세스를 자동화합니다. 예를 들어, 판매, 재고 관리, 고객 지원 등의 다양한 에이전트가 상호작용하며, 각각의 업무를 최적화하고 비즈니스 목표를 향해 효율적으로 진행됩니다.

이 방식은 기존의 단일 에이전트 시스템이 가진 한계를 넘어서, 에이전트 간의 협업을 통해 문제 해결의 속도와 정확성을 높이고, 업무 간의 시너지를 극대화할 수 있는 큰 장점이 있습니다. 다양한 전문 영역을 가진 에이전트들이 협력하여 더 빠르고, 정확한 의사결정을 할 수 있는 환경을 만들어갑니다.

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✅ 오케스트레이션 구조 (Orchestration Framework) 기반의 통합적인 관리

Outcode는 다양한 도구, 데이터, 정책을 에이전트와 유기적으로 연결하고 관리할 수 있는 통합적인 오케스트레이션 구조를 제공합니다. 기업의 기존 IT 인프라와의 원활한 연동을 지원하는 이 구조는, 비즈니스 환경에 맞춰 최적화된 에이전트 시스템을 구축하고 운영하는 데 필수적인 요소입니다. Outcode는 다양한 API, 데이터베이스, 클라우드 서비스, 외부 시스템 등을 통합하여 중앙 집중식 관리 시스템을 통해 모든 에이전트의 활동과 데이터를 한 곳에서 모니터링하고 제어할 수 있게 합니다. 또한, 이 오케스트레이션 구조는 기업이 기존의 IT 환경을 그대로 활용하면서도 에이전트를 최적화된 방식으로 운영할 수 있도록 돕습니다. 예를 들어, 재무, 고객 서비스, 물류 등의 다양한 부서에 맞춤화된 AI 에이전트를 적용하고, 이들 간의 상호작용을 자동화하여 비즈니스 프로세스의 효율성과 정확성을 높입니다. Outcode는 워크플로우의 통합과 관리를 중앙에서 한눈에 관리할 수 있게 해, 복잡한 업무 처리를 원활하게 할 수 있습니다.

Outcode는 단순히 개별 작업을 자동화하는 것이 아니라, 정말 필요한 자율성과 판단 능력을 갖춘 에이전트가 비즈니스 목표를 향해 스스로 움직일 수 있는 환경을 제공하는 데 집중합니다.

📌 결론: Agent라는 이름만으로는 충분하지 않습니다.

"당신의 시스템은 스스로 판단할 수 있는가?"

이 질문에 "예"라고 답할 수 있는 시스템, 바로 그것이 Outcode가 추구하는 진정한 에이전트입니다. Outcode는 기업이 자율적인 AI 에이전트를 통해 비즈니스 프로세스를 혁신하고, AI 기반의 자율적 운영을 통해 미래의 비즈니스를 선도할 수 있도록 지원합니다.

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In 2025, AI agents are evolving from everyday assistants to the core infrastructure of enterprise operations. We are doing more and more of our work with AI, and the need for enterprise-grade AI agents that are autonomous, self-aware, proactive, and connected to real-world operational systems is rapidly growing beyond reactive tools that simply respond to user requests.

But there's an important question here:

"CAN A GENERIC AI AGENT BE USED FOR ENTERPRISE TASKS?" AND "CAN EVERYDAY AI TOOLS HANDLE SOPHISTICATED TASKS IN THE ENTERPRISE?"

The correct answer is no.

Enterprise environments have a higher level of complexity, unique requirements, and clearer lines of accountability than our personal lives. In this article, we'll discuss these differences, why typical AI agents have limitations in the enterprise, and why an Enterprise Agentic Platform is essential.

1. the nature of the purpose of use is different

TYPICAL AI AGENTS ARE DESIGNED PRIMARILY TO IMPROVE PERSONAL CONVENIENCE AND PRODUCTIVITY. THEY PERFORM RELATIVELY SIMPLE, TASK-ORIENTED FUNCTIONS LIKE MANAGING SCHEDULES, SUMMARIZING CONTENT, OR ANSWERING SIMPLE QUESTIONS.

ENTERPRISE AI AGENTS, ON THE OTHER HAND, HAVE AN ENTIRELY DIFFERENT SET OF NEEDS. THEY NEED TO UNDERSTAND AND EXECUTE COMPLEX WORKFLOWS TO HELP ORGANIZATIONS RUN MORE EFFICIENTLY AND IMPROVE PERFORMANCE.

For example, not just invoice processing:

• Production planning based on demand forecasting
• Supply chain realignment tied to real-time sales data
• Run conditional automated approval processes based on budget control criteria
• MANAGE THE MOVEMENT OF DOCUMENTS AND DATA BETWEEN MULTIPLE SYSTEMS, INCLUDING INTERNAL SYSTEMS, SAP, EMAIL, ETC.
• Human-in-the-loop collaboration, where humans see the results of AI's work and key data in real time and can co-direct judgment or proactively intervene in exceptional situations, to maintain decision quality while continuing automation.

To be truly enterprise-ready, AI agents must be able to autonomously handle sophisticated operational tasks that involve autonomous decision-making + system integration + exception handling + human intervention. Therefore, enterprises don't need AI as a mere assistant, but rather autonomous agents as "operational entities" that organically execute
business processes, and an Enterprise Agentic Platform to support them.

2. single task vs. complex workflow

Typical AI is simple, task-oriented, and driven by direct commands from users. Enterprise applications, on the other hand, need to be able to understand and manage complex flows. They need an "Agentic Workflow Layer" that can design and operate these complex structures, which is what the Enterprise Agentic Platform is all about.

Off-the-shelf chatbots vs. organization-specific orchestration
General-purpose chatbots are sufficient for consumer AI, but businesses need enterprise-level orchestration to meet a variety of needs:

• CONNECT WITH APIS, CRM, ERP, DW, EMAIL, SLACK, AND MORE
• Process status tracking and automated reporting
• Fine-tune your data to understand your organization's context

At the end of the day, it's not about "talk," it's about "action.

3. Security, compliance, and accountability

Consumers only need a relatively simple privacy level of security:
In contrast, enterprise environments require the following elements

• Data Isolation: Customer-specific, department-specific, and project-specific data should be completely isolated, and the
• FINE-GRAINED ACCESS CONTROL AND ROLE-BASED AUTHORIZATION (RBAC): YOU NEED TO HAVE FINE-GRAINED CONTROL OVER WHO CAN SEE WHAT INFORMATION AND RUN WHICH AGENTS
• Audit Trail: A historical record of when an agent made a decision, what decision it made, and what data it acted on.
• Explainability: Ensure explainability and transparency of AI's decisions and behaviors
• And most importantly, in an enterprise environment, you don't have a single agent, you have hundreds or tens of thousands of agents working in parallel.
• Without a management scheme that can centrally monitor, control, deploy, and audit all of these agents, agent-based operations are unsustainable in the real world.

To meet these demands, AI agents need to be structurally trusted and manageable, not just technically smart. The Enterprise Agentic Platform is not just a "tool," it's the infrastructure for an AI-driven operating system.

4. EXECUTION WITHOUT ANALYSIS IS DANGEROUS AI AGENTS WITH BUILT-IN PREDICTIONS AND JUDGMENT

Execution in the enterprise is not just repetition.
Most core tasks involve making decisions based on predictions, judgment, and analysis.

For example, in a demand forecast-based order automation scenario, you might use the

• AI COLLECTS SALES DATA AND
• ANALYZE TRENDS BY SKU
• Forecasting future demand
• Compare inventory levels to determine if an order is needed
• AUTOMATIC ENROLLMENT AND CONTACT NOTIFICATIONS IN ERP

WHAT'S IMPORTANT IN THIS FLOW IS THAT THE AGENT HAS BUILT-IN ANALYTICS AND JUDGMENT, SOMETHING YOU CAN'T ACHIEVE WITH SIMPLE CHATBOT-LEVEL AI.

Outcode provides an Agentic Workflow structure that allows AI to naturally perform "analyze → decide → act" within a workflow.

5. FOR AI TO REALLY "WORK," IT NEEDS TO BE CONNECTED: THE ROLE OF INTEGRATION

NO MATTER HOW GOOD AI IS AT MAKING DECISIONS, IT STILL NEEDS TO BE CONNECTED TO REAL-WORLD BUSINESS SYSTEMS TO EXECUTE.

• DOZENS OF CONNECTIONS TO ERP, EMAIL, SLACK, INBOX, CLOUD STORAGE, AND MORE
• MAKE API CALLS, TRIGGER RPA, UPLOAD FILES, SEND MESSAGES, AND MORE
• Security and permission management should also be built in

The middle infrastructure that connects these different systems is the Integration Platform.

📌 Enterprise Agentic Platform = AI + Orchestration + Integration
Without "Integration" of these, agents are disconnected from business systems and are powerless.

Outcode has a modular iPaaS structure under the hood, allowing agents to "do" real work, not just interactive responses.

** Outcode supports external LLM integration protocols, such as Anthropic's Model Context Protocol (MCP). However, Outcode's MCP is not the same as Multi-Agent Collaboration Protocolwhich has a completely different concept and role. Anthropic's MCP allows LLMs to use the accessIt's an interface for LLMs to access external tools or data. It's a one-time call-based input extension tooland is model-driven in design.

Outcode's MCP, on the other hand, empowers agents to divide roles, collaborate, and execute execute workflowsexecute workflows operations-centric architecturewhere agents share roles, collaborate, and execute workflows. It includes message passing between agents, conditional branching, exception handling, and even human collaboration.

In other words, Outcode can write to external MCPs, but it is not a complete Agentic Execution Platformin its own right.

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✅ Why Enterprise Agentic Platform?
For organizations to truly benefit from AI, they need to have the following elements in one unified structure

• Analytics and predictive capabilities (Data + ML)
• Situational awareness and judgment (LLM + Rule Engine)
• System integration for execution (iPaaS-based integrations)
• Human-in-the-loop collaboration structure

ALL OF THIS MUST BE NATURALLY CONNECTED AND OPERATIONALIZED SO THAT AI CAN BECOME AN OPERATIONAL ENTITY THAT ACTUALLY DOES THE WORK OF THE ENTERPRISE.

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Outcode provides a true Enterprise Agentic Platform that fulfills all of these requirements.

The foundation we are talking about is not just the ChatGPT API.
It is a structured AI platform optimized for enterprise operations, with Agentic Workflow, Multi-Agent Collaboration, and iPaaS-level integration infrastructure.

Outcode is at the center of this transition, helping companies realize AI-driven operational transformation.

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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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Google released a white paper on AI Agents in September 2024, which I summarized based on the work of Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic.

GENERATIVE AI MODELS, LIKE HUMANS, CAN BE TRAINED TO ACCESS INFORMATION OR SUGGEST ACTIONS IN REAL TIME, BUT THEY REQUIRE ACCESS TO EXTERNAL TOOLS AND THE ABILITY TO PERFORM TASKS ON THEIR OWN.

• To find specific information, you can extract from the database or use the
• Personalized shopping lists based on purchase history

You can.

Agents combine thinking, logic, and access to external information to expand the scope of generative AI, which used to be standalone.

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What is an agent

• LLM MODELS ARE THE BRAINS OF THE AGENT PROCESS
  • Thinking with logic frameworks like ReAct, Chain-of-Thought, and Tree-of-Thoughts
• Tools is where the agent accesses external data and services.
  • FOR EXAMPLE, MAKE YOUR API ACCESSIBLE
• The Orhestration Layer is responsible for periodically controlling the agent's process.
  • A kind of workflow concept
• Difference between agents and models

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How agents work

• Gather information
• Reasoning and
• Run an action

ReAct: A Prompted Engineering Framework for Generative Models

Chain of Thought (CoT): A prompted engineering framework to help you think through intermediate steps

Tree-of-thoughts (IoT): A prompted engineering framework for strategic preview or exploration.

Tools

Extension

A role that sits between an agent and an API for executing actions, which may have a Built-in-Extension depending on the type or type of API.

Functions

From a developer perspective, the concept of implementing an extension as a module of code, decoupled from the agent and dependent on the application it actually connects to

Data stores

The ability to utilize externally updated information, often implemented as a vector database. The most well-known model is the Retrieval Augmented Generation (RAG) implementation.

Tools at a glance

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How to improve model performance

• In-context learning: define specific conditions under which an agent will run, leveraging the ReAct framework and more.
• Retrieval based in context learning: A technique for leveraging RAGs to improve model performance.
• Fine-tuning based learning: ideal for cases that utilize large datasets tailored to specific cases

Summary

Building complex agent architectures requires an iterative approach, with experimentation and refinement tailored to specific business cases and organizational needs being key. Due to the generative nature of the models that underpin these architectures, no two agents are alike. However, by leveraging the strengths of each component, you can extend the capabilities of the language model and create an agent program that delivers real value

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A workflow is a set of connected actions (tasks) required to accomplish a job. Traditional workflows are often human-centric, with people approving, submitting, reviewing, and confirming.

Outdated workflow solutions are fading away and new types of workflow platforms are emerging, and here are the key drivers of change.

• Evolving tech stack and growing apps
• High data utilization

TRADITIONAL WORKFLOW/BPM/TASK AUTOMATION SOLUTIONS RELY ON EXPERTS TO MANUALLY CREATE AND INTEGRATE INTERNAL AND EXTERNAL SYSTEMS. HOWEVER, THE TECHNOLOGY STACKS USED BY ORGANIZATIONS ARE EVOLVING AND THE NUMBER OF WORK TOOLS IS GROWING, SO SOLUTIONS THAT REQUIRE DESIGN AND CODING ARE NOT IDEAL.

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Second, data has become an integral part of every business activity. The workflow we want is one where data is fed to a variety of business tools so that they can automatically do what we want them to do, and the data we want is accessible and available to us in real time.

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Emerging workflow platforms that fulfill the above requirements are essential for the following reasons.

1. efficiency

The business world is becoming increasingly complex. More people are juggling multiple, complex tasks every day. Having dozens of tools at your fingertips every day can lead to marginal utility, loss of importance and prioritization, and difficulty getting to the really important tasks and decisions.

2. Data integration

Automating workflows that tie data together can actually reduce costs and time significantly. However, today's technology stacks and data are complex and ever-changing. If you develop your own or use an ill-fitting solution for data integration, the complexity can outweigh the benefits.

3. Hyper-automation

Full automation is already being adopted in many areas. It's evolving toward removing humans from processes, and automated workflows are at the core of how businesses operate.

4. a person is the subject of utilization in data processing.

Your company processes data, and people are doing a lot of the work. It's time to rethink the way we work. Data processing should be automated and people should focus on utilizing the data.

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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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Automation in the enterprise operates differently than automation for personal use. It's about more than just automating tasks, it's about deep integration with operational processes and data across the enterprise. Solving operational bottlenecks should be data-driven, not human-driven, which requires a deep understanding of services, business processes, and use cases for data.

In an enterprise environment, consolidated data is often more important than data that individuals work with, and convergence is essential as teams responsible for operations must work closely together across customer success, solution and service delivery, operations optimization, product development, marketing, and sales processes.

Enterprise automation support tools need to be approached from the ground up.

UNTIL NOW, THE SPECIFIC NEEDS OF ENTERPRISE BUSINESS AUTOMATION HAVE NOT BEEN PRIORITIZED. AS A RESULT, COMPANIES HAVE HAD TO USE A VARIETY OF TOOLS INDIVIDUALLY, INCLUDING DATABASES, CRMS, ERPS, INTERNAL SYSTEMS, CUSTOMER SUCCESS PLATFORMS, DATA-DRIVEN, AI-ENABLED, OMNICHANNEL INTEGRATIONS, CUSTOMER MARKETING PRODUCTS, PRODUCT FEEDBACK TOOLS, AND MORE.

Outcode connects all of this data into one platform.

Outcode: A revolutionary automation-enabled platform for the enterprise

Outcode has been rapidly developing innovative solutions to move beyond traditional 10-year-old automation systems, but that's just the beginning. Today, the Outcode platform includes features such as

• Powerful automation: maximize efficiency with filters, actions, and more
• Data action tools: Empower users to find the data they need on their own in real time
• Integration: Effective integration and organized connectivity of databases, enterprise applications, and business applications.
• Collaboration and access control: Enable active collaboration while maintaining fine-grained access control
• Execution history: Organized logs and history management for all running automations

The best way to understand Outcode is to experience it for yourself. Sign up for a product demo!

THE FUTURE OF AI AUTOMATION

The Outcode platform is developing a range of new features that are tightly integrated with AI. While many organizations have high hopes for AI-enabled tools, most have fallen short of enterprise use cases. Outcode is poised to deliver real value by integrating AI capabilities with the core of the platform. Hundreds of teams use Outcode as a source of enrichment and automation across their operations. We'll be investing even more in powering automation with AI.

What's next?

Outcode will make it easy for teams inside your company to set up creative workflows and automations and scale customer operations. Outcode customers will be able to accomplish more with fewer resources.

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Today, many organizations are connecting their various systems and data to streamline their operations. Private connectors and SaaS connectorsplay an important role in this process. In this article, we'll demystify what these two concepts are and why they're important.

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What is a Private Connector?

A private connector isa connection method used to connect sensitive data or systems that are internal to your company. For example, if you want to automate the use of data in your company's platform, you'll need a private connector. This tool acts as a secure connection between data and systems that are not visible to the outside world and are only used within your company.

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What is a SaaS Connector?

SaaS Connector isa tool that connects various software as a service (SaaS) applications available in the cloud with other systems in your company. SaaS applications are software that can be easily accessed through a web browser, such as Google Drive, Salesforce, and Microsoft 365. SaaS Connector makes it easy to connect these applications with your company's internal systems or other cloud services.

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The roles of private and SaaS connectors

1. Data security (Private Connector): Private connectors securely connect systems, platforms, and more inside your organization to automation services, especially if you have homegrown systems. For example, if you're connecting data to a system you've developed for your own internal use, you can use your own private connector that's not open to all users of the automation platform to make it more secure.
2. Connect with existing systems (Private Connector): Many organizations use proprietary systems. Private Connector acts as a bridge between these internal systems and the latest cloud services, allowing businesses to continue to utilize their existing systems while reaping the benefits of the latest technology.
3. SaaS application integration (SaaS Connector): SaaS Connector connects multiple cloud-based applications to each other. For example, you might want to pull customer data from Salesforce and automatically enter it into your company's ERP system. SaaS Connector makes this task easy.
4. Real-time data processing (Private Connector and SaaS Connector): Both connectors enable automated processing of real-time data. The Private Connector processes data quickly within your organization, while the SaaS Connector enables real-time data flow between cloud applications. For example, when an order comes in from your online store, the order information is automatically reflected in your internal inventory management system.
5. Compliance (Private Connector): Private Connectors help companies manage their data while staying compliant with data regulations. For example, companies following Europe's GDPR regulations can use their own Private Connector to protect sensitive data when automating it.
6. Reduce costs and increase productivity (SaaS Connector): SaaS Connector allows you to connect multiple applications to automate tasks. This reduces errors that occur when working manually and helps your employees focus on more important tasks.

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How to create a private connector

Below are the main steps for creating a Private Connector the traditional way.

1. analyze your requirements

First, you need to clearly define which systems and data you want to connect. For this step, you should consider the following

• Data sources to connect to: databases, internal systems, servers, and more.
• Security requirements: Data encryption, access control, audit logs, etc.
• Connect to: The cloud service or external application to connect to.

This analysis is the basis for designing your private connector.

2. Select a technology

When building a private connector, you'll need to decide which technology to use. Here are some of your main choices

• Programming languages: You can develop in a variety of languages, including Python, Java, .NET, and more.
• API technology: Select the technology for data communication, such as REST API, SOAP API, GraphQL, etc.
• Security technologies: Consider SSL/TLS encryption, OAuth authentication, VPNs, and more.

3. design

The design phase defines the structure and behavior of the Private Connector.

• Architectural design: Design how the Private Connector will transfer data between the data source and the cloud service. This includes data flow diagrams, how data is transferred, how errors are handled, and more.
• Security design: Design security-related elements such as data encryption, access rights management, and audit logging.

4. click Develop

Once your design is complete, you'll actually develop your Private Connector.

• API development: Develop APIs to send and receive data. For example, build an API to securely transfer data between your company's database and a cloud service.
• Implement security: Implement security features, such as encryption, when transmitting data. For example, use SSL/TLS to protect data in transit.
• Logging and monitoring: Add logging and monitoring features to track data flow and detect issues.

5. test

Once development is complete, test the Private Connector to make sure it's working properly.

• Unit tests: Test each feature individually to make sure it works correctly.
• Integration testing: Ensure that the Private Connector integrates well with your overall system.
• Security testing: Perform security checks to ensure there are no data leaks or vulnerabilities.

6. Deploy and operate

Private connectors that pass the tests are deployed to production.

• Deploy: Deploy to on-premises systems, or deploy to a hybrid cloud environment as needed.
• Monitoring: Set up a monitoring system to detect and respond to issues that may arise during operations in real time.
• Maintenance: Resolving issues that arise during production and enhancing features where necessary.

7. documentation and training

Finally, create documentation for the Private Connector and train the IT team or relevant departments that will be using it.

• Documentation: Document how to install the Private Connector, configuration options, operating procedures, and more.
• Training: Train your operations team or users on how to use and maintain Private Connector.

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A better way to create private connectors

Let's take a look at how you can easily create a private connector using a data automation platform.

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What is a data automation platform?

A data automation platform is a tool that automatically connects different data sources and applications and manages data flows. They make it easy to perform integrations without writing code, so they can be utilized by business users as well as IT teams. Some popular data automation platforms include Zapier, Mulesoft, and Outcode .

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Benefits of building a private connector with a data automation platform

• Easy development: Create internal, proprietary connectors for your own use without the need for complex coding.
• Enhanced security: The security features provided by the platform ensure that your data is handled securely.

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How to create a private connector with a data automation platform

These days, data automation platformsmake it easy and quick to build.

1. Sign up for a platform: For example, sign up for Outcode and set up a business account.
2. Connect an internal data source: Connect the data source (e.g., on-premises database, first-party system) to the platform that you want to connect to through the Private Connector. You typically establish a connection by entering database credentials, API keys, etc.
3. Security settings: Make the connector private so that it's only available to your organization and specific teams so that it's not visible to the outside world.
4. Use automations: Internal teams create automations using private connectors.

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Conclusion

Private Connectors and SaaS Connectors play an important role in an organization's integration solution. Private connectors focus on security and leveraging existing systems, while SaaS connectors connect different cloud applications to streamline work. If you're planning a digital transformation, it's critical to understand and leverage these two roles.

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Most companies rely on a variety of solutions, data, and applications to run their business, provide services, or make sales. While the amount of software you use can vary greatly depending on your company's size, industry, and operations, recent studies and reports show that companies are already using a wide variety of software applications and systems.

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Intergration as a service is the use of the

"Integration as a Service" (IaaS) refers to a cloud-based service that provides integration capabilities that connect various software applications and data sources.

These are features and services built into Outcode that help organizations fill gaps in their work and processes, create automated data flows, and ensure disparate systems work together seamlessly. With IaaS, companies can immediately take advantage of complex integration infrastructure without having to build and maintain it themselves.

First, companies rely on a variety of applications, systems, solutions, and more to run their businesses, develop products and services, sell and market, and more.

1. Small and medium-sized businesses (SMBs): Reported to use an average of 73applications. If you think about the software, systems, solutions, etc. that your company uses, no matter how small, you're probably using more than 20 different pieces of software.
2. Large Company: Using an average of 129applications up toover 2,500applications. Larger companies require more software due to their diverse departments and complex operational structure.

The key components of service integration are

• Data integration
• Automate business flows (workflows)
• AUTOMATIC CONNECTOR OR APP, API CONNECTION

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Your company's real-world problems

The average company uses a wide variety of software, including databases, CRM, ERP, email, office tools, collaboration tools, project management tools, human resources/finance/production/inventory/logistics, messaging, marketing, and more, but they are often disconnected and siloed. gaps in tasks and processes, and people are taking over what programs should be doing. that programs should do.

Service integrations are a way to address these issues, but they are all low-code, which makes them difficult for end users or practitioners to use.

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Automation as a service is the use of a

"Automation as a service" (AaaS, automation-as-a-service, or automation services) refers to cloud-based offerings that provide the ability to automate various business processes and tasks. AaaS makes it easy for businesses to take advantage of automation capabilities without having to build and manage complex automation infrastructure themselves. This can increase productivity, improve operational efficiency, and reduce errors.

In short, "Business Automation services" are provided to automate tasks and processes.

A core service and customer value at Outcode, we make it easy for people who run and manage tasks and processes to automate their work.

• Lower costs: Eliminate development costs from automation infrastructure and minimize operational costs by paying as you go.
• Usability: Simple, straightforward usability that can be used by users or practitioners without asking developers, requiring no training and no difficulty.
• Immediacy: Highly usable and business-optimized features enable users to implement automated tasks whenever and wherever they want.

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Your company's real-world problems

No company is immune to the benefits of automation, but the automation solutions on the market are stuck in a 15-year-old rut. They're awkward, unreliable, inconvenient, and expensive.

What's more, because it was developed on an older technology stack, maximum usability is low-code, which means that the automation solution can't be used by the people who run the business.

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Outcode Services

Outcode is a service built to make service integration and business automation challenges simple and straightforward on one platform. It's especially optimized for small and mid-sized businesses with limited time, money, and resources.

• INTEGRATE DATA ACROSS MULTIPLE DATA SOURCES, APPLICATIONS, AND APIS
• Empower practitioners to create and manage the data automation they need to do their jobs.
• Unified management and bulk control of apps connected to automation across your organization
• Reliable data automation and high security
• Achieve the most substantial digital transformation beyond productivity gains

Our unique automation reflects the real world, without the complexity of development and maintenance, so you can solve your data integration and business automation challenges once and for all.

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POWERTASK, THE OPERATOR OF OUTCODE, HAS BEEN SELECTED FOR THE 2022 DATA-STARS!

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Let's chat with Chris about how he came to apply to Datastars and what it was like to participate in Kickoff Day.

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Q: WHAT IS DATASTARS?

Organized by the Korea Data Industry Promotion Agency, DataStars is a business incubation program that provides data-specific mentoring, infrastructure, and commercialization to help data innovation startups become globally competitive.

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Q: WHAT INSPIRED YOU TO APPLY?

Outcode is a platform created to help companies utilize data more systematically. There are not many commercialization programs in Korea that specialize in data, so I joined the program because I thought I could learn a lot from the expert mentors.

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Q: WHAT WAS THE MOST MEMORABLE PART OF YOUR KICKOFF DAY?

It was great to talk to other startups about data-driven businesses, from mentorship from data and systems experts to tips on raising investment.

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Q: DO YOU HAVE ANY TIPS YOU CAN SHARE WITH THOSE PREPARING FOR DATASTARS?

Because Datastars is a specialized business incubator with a 25:1 competition rate, it's important to work hard on all of your materials. Here's a tip, I recommend working on your application as if you were creating an investor relations deck or presentation. I've tried to keep it as simple as possible so that it's easy to understand if you've never heard of us before.

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With the support of this initiative, the Outcode team plans to grow the service to help automate data.

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Last week, we covered the"[Outcode] Supercharging Productivity" seminar at the Korea Investment Accelerator.

We talked about why our teams need automation, what data automation is that everyone's talking about, and how teams that are using Outcode are increasing their productivity.

• What is data automation?
• Automation is a matter of survival
• How are other teams automating?
• Start automating

What is data automation?

There are so many different definitions of data automation. The Outcode team says it's "making data easy to use and automated for anyone.

Automation is a matter of survival

Have you ever thought, "Data-driven operations and data automation, my team should be doing that..."?

Most outcoders have had this problem.

"Do we really need to automate this now, even though it's working fine the way it is?" "What are the specific benefits of automating this?

Accenture reportsand survey responses from Outcode users have shared that data automation not only saves resources and grows revenue, but also improves business confidence - meaning that data and tasks are handled according to the processes you've created, eliminating mistakes and increasing efficiency.

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If it really works as well as it does, why weren't we automating our data?

Every business, whether it's a startup or an SMB, has data. Customer information, payment history, user logins, you name it, it's probably stored in a variety of different ways, and for some reason, automating it all sounds like something that developers have to do, and it's something that's going to take a lot of time and money, and most people just give up.

But the gap between those who automate and those who don't is getting too wide for us to just throw in the towel, and at the other extreme, our business is becoming less likely to survive.  

We encourage you to spend some time with your team to explore what automation needs your team has. Because no matter your industry, no matter your team, you need automation.

Start automating

At this seminar, we had someone say, "I want to start automating, but I don't know where to start," and there's one thing that the Outcode team always emphasizes whenever we get questions like that.

"All automation starts with data."

Think about where your team is storing data and what services or apps they're using.

1. Data storage: Excel, Google Sheets, or a database like MySQL is great, but make sure you know where your team's data is stored.
2. Other services and apps: Take stock of the services you use to do your job. Slack, Google Sheets, Naver Works, KakaoTalk, etc.

Automation starts with thinking about how we can connect our team's data and the services we use.

I hope you'll be able to use the data you've accumulated over the course of your career in a variety of ways, automating processes that were once handled manually, and creating the data automation that's right for your team.

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At theApplication Innovation & Business Solutions Summit in Las Vegas in June 24, 2014, Gartner announced the concept of BOAT, which stands for Business Orchestration and Automation Technologies, which, as explained by VP Analyst Saikat Ray, signifies the evolution of automation tools and technologies.

BOAT focuses on the convergence and evolution of various automation technologies - Business Process Automation (BPA) technologies, Robotic Process Automation (RPA) tools, Integration Platform as a Service (iPaaS), No-Code Application Development Platform (LCAP), and Generative AI.

He explained that from a business perspective, automation is now easy to understand and justify its need: it provides cost optimization, agility, efficiency, and accuracy.

However, it's not always possible to do so. Among the reasons why

1. In the automation software market, tools and vendors often have partial or overlapping functionality across tools and vendors.
2. Because it's still too hard for business users to use

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The importance of orchestration

THE "O" IS FOR BOAT IF YOU CAN'T ORCHESTRATE THE PROCESS OF BUILDING AND SELLING YOUR COMPANY'S PRODUCTS AND SERVICES AND THE DATA THAT COMES WITH IT, YOU WON'T GET THE TRUE BENEFIT OF AUTOMATION TECHNOLOGY.

Organizations often adopt automation tools for partial or discrete tasks. While they may see small wins in the short term, by their very nature, they are disconnected from the company's business processes and data and require more effort and IT resources to integrate into business processes. These automation tools create technology debt and lead to abandoned automation that can't keep up with the ever-changing nature of the business.

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The importance of data

From a functional perspective of what orchestration integrates, you can think of it as connecting data, systems, and applications, but at a more fundamental level, it's about connecting the data that happens in your business and is needed for your processes.

IN THE WORLD OF PROCESS AUTOMATION, IT'S NOT ENOUGH TO BE CLOUD-FIRST, API-FIRST, OR AI-FIRST; SCALABLE AND ELASTIC PROCESS ORCHESTRATION IS FUNDAMENTAL, AND DIFFERENTIATION IN ORCHESTRATING THE DATA THAT HAPPENS IN THE PROCESS BECOMES ESSENTIAL.

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End-Users

From a Business Orchestration and Automation Technologies (BOAT) perspective, the most important factor is the user.

As many companies are undergoing digital transformation, the workload of IT departments has skyrocketed. This has led to an increased demand from business units to develop the applications they need directly. To automate work processes and drive innovation, companies have started to create development environments where non-developers can participate. Companies are actively adopting automation solutions to increase work efficiency, giving rise to the concept of so-called citizen developers.

The knowledge and skills required for citizen developers to be effective are varied. These include business understanding, basic technical knowledge, and problem-solving skills, as well as the ability to leverage low-code and no-code platforms. Acquiring basic programming knowledge has been essential. Because of these obstacles, the number of civic developers in the average organization is very small, and dedicated developers in line-of-business departments are often referred to as civic developers.

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Beyond citizen developers

The most important thing about the new technology, Business Orchestration and Automation Technologies (BOAT), is that it allows business users to create the automation they need for their processes on their own.

The rise of disruptive technologies, including generative AI, is opening up automation to anyone in an organization, not just citizen developers. Where automation used to require specialized knowledge and training, AI is eliminating that step.

AUTOMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE ARE POWERFUL TOOLS IN THEIR OWN RIGHT, BUT THEY ARE EVEN MORE SYNERGISTIC WHEN USED TOGETHER. AI ADDS INTELLIGENCE TO AUTOMATION TECHNOLOGY, ENABLING IT TO HANDLE MORE COMPLEX AND UNSTRUCTURED TASKS, AND AI MAXIMIZES THE REACH OF AUTOMATION BY APPLYING AI-REPLACED FUNCTIONS IN REAL TIME. THE CONVERGENCE OF THESE TWO TECHNOLOGIES IS PLAYING A KEY ROLE IN DRIVING INNOVATION ACROSS A WIDE RANGE OF INDUSTRIES AND MAKING COMPANIES MORE COMPETITIVE.

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The Outcode team has raised the highest amount of follow-on funding from the Korean investment accelerator Correct Accompaniment Program!

Outcode is an innovative no-code platform that enables startups and small to mid-sized businesses to automate their work. With Outcode, you can connect the various data you use in your operations and create business apps that automatically process them based on your desired scenarios and conditions.

Startups such as Lazy Society, S2W, Tree Planet, Korea Investment Accelerator, Kakao Ventures, Smart Inheritance, IMRN, Guam Tours, and many more are using Outcode, and it's been very well received by them.

With this investment, we plan to focus on expanding the automation area, including mass usability, and upgrading the product through service planning and hiring designers to prepare for full-scale market entry.

The Outcode team entered the data automation market because they realized that existing automation services weren't directly helping companies improve and evolve their business processes. They realized that most companies were doing things the same way or relying on manual labor, without improving their most important tasks.

No-code Data Automation to improve operations

The data you need to do your job is in many places. It's in databases, spreadsheets, and countless apps and services, and we're creating new forms of automation software that allow you to freely connect this data and connect it to other services on your terms and in your state.

While traditional work automation software focuses on connecting events, Outcode focuses on data automation, which is essentially automating data freely. Traditional automation is about getting alerts or notifications automatically, but to fundamentally transform work and create new processes, they need to be built around data and enable diverse teams to collaborate and continuously improve.

Outcode enables organizations to not just automate tasks, but to collaborate on different work scenarios and implement new automations that enable breakthrough improvements. Just as Microsoft Excel came along in the 1980s and made data easy and simple to use in a program for the first time, Outcode is revolutionizing the data automation market in an industry that hasn't seen much change in the last 30 years.

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