Many organizations have invested years in tools, bots and integrations, yet still feel their processes are not truly automated. The reason is simple: most processes were merely automated, not orchestrated. The difference between RPA and an agent is the difference between a fixed script that gets stuck when input changes, and a system that reasons and knows how to solve problems on its own, sets its own path and acts. This article is the practical guide for making that transition.

The Difference Between RPA and an AI Agent

RPA operates according to rigid logic, and gets stuck when input arrives in an unexpected format. An agent, on the other hand, captures data from multiple sources, reasons and resolves in order to advance toward the goal, acts within its defined boundaries, handles exceptions without a ready script, and documents every decision. The example illustrates: a forecasting tool says a shipment will be late; the agent identifies, checks alternatives, reroutes, updates the warehouse management system, notifies the customer, and escalates only when the case exceeds its authority. The agent chooses the path; RPA merely walks the path you defined.

מודל הנהלים המומלץ לעבודה עם סוכני AI בשלוש רמות

המטמיעים המובילים עובדים לפי מודל מדורג. ברמת הסיכון הנמוכה ביותר נמצאות החלטות בנפח גבוה, שגרתיות והפיכות, שבהן עלות הטעות קטנה ומובנת והסמכות תחומה: הסוכן פועל, מתעד וממשיך הלאה, למשל הזמנות חידוש מלאי סטנדרטיות מתחת לסף הוצאה, או תיחור מחדש של מוביל בתוך רשת ספקים מאושרת. ברמת הסיכון הבינונית, שבה המהירות חשובה אך אדם יכול להתערב בתוך חלון זמן מוגדר, הסוכן פועל אלא אם האדם החליט להתערב ומבטל את הפעולה של הסוכן. ברמת הסיכון הגבוהה, של החלטות אסטרטגיות או בלתי הפיכות, הסוכן מכין וממליץ, והאדם מכריע. הכלל החוצה את כל הרמות: כל החלטה אוטונומית מתועדת עם הנתונים וההיגיון שמאחוריה, ומרחב הסמכות מורחב רק ככל שהראיות מצדיקות זאת.

Where to Start

Start with a single, focused, high-value use case. For example, logistics exception management and standard inventory replenishment are the recommended starting points: the decision logic is well-defined, the data requirements are clear, the impact is measurable, and the risk is low. A focused pilot on a bounded use case can go live within three to six months, while connecting multiple processes takes longer. The common mistake is chasing a broad vision of an ‘autonomous supply chain’ before the data infrastructure exists.

The Infrastructure That Must Be in Place

Agent-based systems require clean, real-time data from the ERP, warehouse management system, transportation system and procurement systems. Data quality and completeness must be assessed before selecting a platform. This is precisely why, according to a leading international research and advisory firm, more than 40% of agent projects are expected to fail by 2027 — not because of the agent but because of legacy information systems and data. Procedures and authorities for agents must be built in parallel, not afterward, and operational results must be measured.

The Orchestration Layer: Where Comidor Comes In

What most organizations are missing is the orchestration layer: the platform that connects agents, humans and existing systems into a single controlled and documentable process. A low-code platform for process management is built precisely for this. Comidor, which Mashik implements in Israel, provides a visual drag-and-drop process engine, AI agents and cognitive automation (OCR, NLP and text classification), RPA integration for third-party systems, machine learning models within the process, document data extraction that reduces processing time by approximately 80%, and built-in human-in-the-loop controls and documentation. In practice, business and IT staff build the agent-guided process together, the agent operates within the process and within its defined settings bounded by permissions, and every action is recorded. This way an ‘agent’ becomes a ‘controlled process’.

From Pilot to Implementation

Technology is the easy part. The difficulty is choosing the right process: the right first use case, the data infrastructure, defining procedures and characterizing processes and orchestration. This is what implementation means. Mashik combines supply chain and procurement consulting with practical implementation of Comidor in Israel, so that the transition from RPA to autonomous agents delivers measurable results.