I. What is Agentic AI?<\/h2>\n1. Definition and origin of the term<\/h3>\n
Agentic AI<\/strong> (agent-based AI or autonomous AI agents) refers to artificial intelligence systems that can autonomously execute goals. Instead of merely responding to prompts or generating text on demand, they can proactively plan<\/strong>, select actions<\/strong>, interact with their environment<\/strong> (digital systems, APIs, external services), and adapt their behavior<\/strong> to achieve one or multiple predefined objectives.<\/p>\n The term originates from the concept of agents<\/strong> in artificial intelligence and systems theory (software agents, intelligent agents)\u2014programs that can observe their environment, make decisions, and take actions. In recent years, as large language models (LLMs) and tool-using agent frameworks have advanced, the term agentic<\/em> has gained popularity to distinguish between reactive AI<\/strong> and proactive, goal-driven AI<\/strong> with planning capabilities.<\/p>\n Traditional AI (narrow AI)<\/strong> performs specific tasks on demand: image classification, Q&A, prediction\u2026 It waits for inputs and then produces outputs.<\/p>\n<\/li>\n Agentic AI<\/strong> receives a goal (or even infers one) and then proactively tries to achieve it through an iterative observe \u2192 plan \u2192 act \u2192 learn<\/strong> loop.<\/p>\n<\/li>\n<\/ul>\n Traditional AI usually performs one-shot inference<\/strong>.<\/p>\n<\/li>\n Agentic AI executes multi-step action sequences<\/strong>, calling APIs, retrieving data, fixing errors, and iterating until the goal is met.<\/p>\n<\/li>\n<\/ul>\n Narrow AI typically operates within a \u201cblack box\u201d and returns an output.<\/p>\n<\/li>\n Agentic AI can directly affect external systems<\/strong>: send emails, update a CRM, trigger cloud jobs, adjust system configurations, or collaborate with other agents.<\/p>\n<\/li>\n<\/ul>\n Agentic AI often incorporates feedback loops<\/strong> (online learning, reinforcement learning, or continuous fine-tuning) to refine behavior based on real outcomes.<\/p>\n<\/li>\n Traditional AI is generally updated only through periodic offline training cycles.<\/p>\n<\/li>\n<\/ul>\n In summary:<\/strong><\/p>\n Agentic AI = goal-driven + planned + actionable AI<\/strong><\/p>\n<\/li>\n Overview of Agentic AI. Source: indosakura<\/p><\/div>\n A typical Agentic AI system consists of multiple components working together in a continuous loop: Sense \u2192 Model \u2192 Plan \u2192 Act \u2192 Learn<\/strong>. Below is a detailed breakdown of each component and its operational mechanics:<\/p>\n Goal \/ Intent Manager<\/strong><\/p>\n Receives goals from the user or from another system (e.g., \u201creduce transportation costs by 10% this month,\u201d \u201cresolve 90% of customer support tickets within 24 hours\u201d<\/em>).<\/p>\n<\/li>\n Converts goals into machine-computable formats such as utility functions, constraints, and KPIs<\/strong>.<\/p>\n<\/li>\n<\/ul>\n Perception \/ Observations<\/strong><\/p>\n Collects data from various sources: internal APIs (ERP, CRM), IoT sensors, logs, web data, email, and streaming pipelines.<\/p>\n<\/li>\n Preprocessing steps include parsing, normalization, enrichment, and RAG (retrieval-augmented generation)<\/strong> for contextual information.<\/p>\n<\/li>\n<\/ul>\n World Model \/ State Representation<\/strong><\/p>\n Represents the current state of the environment: metrics, relationships, constraints, and historical data.<\/p>\n<\/li>\n May include a knowledge graph<\/strong>, vector embeddings<\/strong> (for semantic retrieval), and predictive models<\/strong>.<\/p>\n<\/li>\n<\/ul>\n Planner<\/strong><\/p>\n Generates a sequence of actions required to move toward the goal. Planning approaches include:<\/p>\n Classical planning<\/strong> (symbolic planners) for environments with explicit constraints.<\/p>\n<\/li>\n Search-based planning<\/strong> (MCTS, heuristic search).<\/p>\n<\/li>\n Policy-based planning<\/strong> (reinforcement learning\u2013derived policies).<\/p>\n<\/li>\n Hybrid methods:<\/strong> LLM reasoning proposals validated by rules or logic-based planners.<\/p>\n<\/li>\n<\/ul>\n The planner evaluates alternatives, estimates risks, and allocates resources.<\/p>\n Executor \/ Policy<\/strong><\/p>\n Executes selected actions: calling APIs, creating tickets, sending emails, modifying configurations, launching workflows, etc.<\/p>\n<\/li>\n Monitors the outcome of each step and performs rollbacks if needed.<\/p>\n<\/li>\n<\/ul>\n Memory \/ Long-term State<\/strong><\/p>\n Stores action history, conversation context, and feedback to improve future behavior.<\/p>\n<\/li>\n Uses persistent storage systems, vector databases for RAG, or fine-tuning pipelines.<\/p>\n<\/li>\n<\/ul>\n Learning Loop<\/strong><\/p>\n Collects performance signals (rewards, KPIs) to update the model or policy.<\/p>\n<\/li>\n Supports both offline retraining<\/strong> (periodic) and online learning<\/strong> (real-time\/incremental).<\/p>\n<\/li>\n<\/ul>\n How Agentic AI works. Source: advertisingweek<\/p><\/div>\n Safety, Constraints & Governance<\/strong><\/p>\n Rule engines and policy guardrails (e.g., Open Policy Agent<\/strong>, hard constraints) prevent invalid or unsafe actions.<\/p>\n<\/li>\n Audit logs, explainability modules, and human-in-the-loop checkpoints support high-risk decision points.<\/p>\n<\/li>\n<\/ul>\n Coordination \/ Multi-agent Mechanisms<\/strong> (if applicable)<\/strong><\/p>\n When multiple agents collaborate, they need communication protocols, role\/permission management, and conflict-avoidance mechanisms (negotiation, market-based allocation).<\/p>\n<\/li>\n A central orchestrator<\/strong> manages tasks, dependencies, and agent interactions.<\/p>\n<\/li>\n<\/ul>\n Integration Layer & Tooling<\/strong><\/p>\n Adapters for APIs, command runners, headless browsers, and RPA bots.<\/p>\n<\/li>\n Supporting frameworks: LangChain, AutoGPT, LlamaIndex<\/strong>, and orchestrators like Airflow \/ Argo<\/strong>, plus MLOps and observability stacks.<\/p>\n<\/li>\n<\/ul>\n Example Flow (sense \u2192 plan \u2192 act)<\/strong><\/p>\n The agent receives the goal: \u201cReduce Inventory A by 20% within 30 days.\u201d<\/em><\/p>\n<\/li>\n It collects inventory data, lead times, and forecasts.<\/p>\n<\/li>\n It predicts demand and identifies bottlenecks in the supply chain.<\/p>\n<\/li>\n It generates an action plan: adjust supplier orders, reprioritize SKUs, launch clearance promotions.<\/p>\n<\/li>\n It executes actions: calls order APIs, triggers marketing campaigns, creates logistics tasks.<\/p>\n<\/li>\n It monitors KPIs daily and adapts the strategy (learn & adjust).<\/p>\n<\/li>\n<\/ol>\n Agentic AI does not operate in a simple \u201cquestion\u2013answer\u201d manner like traditional chatbots. Instead, it runs through a continuous closed loop. Each iteration includes:<\/p>\n Collects information from the environment: system APIs, databases, IoT sensors, logs, websites, email, etc.<\/p>\n<\/li>\n Combines real-time data with historical data.<\/p>\n<\/li>\n Enhances context using RAG (retrieval-augmented generation)<\/strong> or by querying a knowledge graph to improve planning accuracy.<\/p>\n<\/li>\n<\/ul>\n Determines multi-step strategies to achieve the goal based on perceived data.<\/p>\n<\/li>\n Uses LLMs for reasoning and analysis, alongside specialized planning tools (heuristic search, symbolic planners).<\/p>\n<\/li>\n Can simulate outcomes before execution to minimize risk.<\/p>\n<\/li>\n<\/ul>\n Executes actions via tools: calling APIs, triggering workflows, sending emails, creating tickets in enterprise systems, etc.<\/p>\n<\/li>\n Verifies the result of each step and can cancel or adjust the plan if deviations occur.<\/p>\n<\/li>\n<\/ul>\n Logs the results and effectiveness of actions taken.<\/p>\n<\/li>\n Updates the model or policy to improve performance in the next iteration.<\/p>\n<\/li>\n Employs reinforcement learning or online learning when continuous self-optimization is required.<\/p>\n<\/li>\n<\/ul>\n Clear and Measurable Goals<\/strong><\/p>\n Agentic AI requires objectives with explicit KPIs\u2014for example, \u201creduce order processing time to < 2 hours\u201d<\/em> instead of \u201cspeed up the workflow.\u201d<\/em><\/p>\n<\/li>\n These KPIs act as the basis for evaluating success or failure.<\/p>\n<\/li>\n<\/ul>\n Complete Context & Reliable Data<\/strong><\/p>\n Missing or inaccurate data leads to poor decisions.<\/p>\n<\/li>\n A robust data pipeline with standardized, frequently updated data is essential.<\/p>\n<\/li>\n<\/ul>\n Control Mechanisms & Permission Limits<\/strong><\/p>\n Guardrails ensure the agent does not perform actions outside its authorized scope.<\/p>\n<\/li>\n Human-in-the-loop checkpoints should be integrated at high-risk decision points.<\/p>\n<\/li>\n<\/ul>\n Operating principles of Agentic AI. Source: outsystems<\/p><\/div>\n LLMs:<\/strong> For analysis, reasoning, and high-level decision-making.<\/p>\n<\/li>\n RPA & API orchestration:<\/strong> For automated execution across enterprise systems.<\/p>\n<\/li>\n Process mining:<\/strong> Helps the agent understand real process flows for more effective planning.<\/p>\n<\/li>\n MLOps & Observability:<\/strong> Monitoring, logging, and performing rollbacks when necessary.<\/p>\n<\/li>\n<\/ul>\n Goal:<\/strong> Reduce inventory of product X by 25% within 45 days.<\/p>\n<\/li>\n Data gathering:<\/strong> Inventory data, sales history, supplier lead times.<\/p>\n<\/li>\n Planning:<\/strong> Reduce orders from supplier A, increase promotions in region B, reallocate stock from warehouse C to D.<\/p>\n<\/li>\n Execution:<\/strong><\/p>\n Call ERP APIs to modify purchase orders.<\/p>\n<\/li>\n Launch marketing campaigns.<\/p>\n<\/li>\n Send internal logistics requests.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n Monitoring:<\/strong> Review KPIs weekly and autonomously adjust the strategy if inventory reduction is slower than expected.<\/p>\n<\/li>\n<\/ol>\n<\/blockquote>\n Benefits and risks of Agentic AI. Source: encrypted<\/p><\/div>\n Agentic AI represents a significant leap in automation, offering enterprises faster, more flexible, and more efficient operations. However, to fully harness its potential while minimizing risks, organizations must implement a well-structured strategy\u2014from goal definition and data governance to monitoring mechanisms and security architecture.<\/p>\n Agentic AI is not just an emerging technology concept \u2014 it is already transforming how organizations operate, make decisions, and interact with customers. With autonomous capabilities and multi-step task execution, Agentic AI can create value across multiple layers: from internal process optimization to enabling entirely new products and services.<\/p>\n Scenario:<\/strong><\/p>\n In factories or warehouses, Agentic AI gathers data from IoT devices, AI Vision cameras, and ERP systems to plan production, allocate resources, and coordinate workforce scheduling.<\/p>\n Benefits:<\/strong><\/p>\n Reduce downtime through Predictive Maintenance<\/strong>.<\/p>\n<\/li>\n Optimize inventory levels and lower storage costs.<\/p>\n<\/li>\n Improve accuracy in task assignment and resource utilization.<\/p>\n<\/li>\n<\/ul>\n2. How Agentic AI differs from traditional AI<\/h3>\n
Goals and autonomy<\/h4>\n
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Planning and multi-step actions<\/h4>\n
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Interaction with the environment<\/h4>\n
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Continuous learning and self-optimization<\/h4>\n
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3. How Agentic AI Works \u2014 Architecture and Core Components<\/h3>\n
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II. How Agentic AI Works \u2014 Core Principles<\/h2>\n
1. The Fundamental Cycle: Perception \u2192 Planning \u2192 Action \u2192 Learning<\/h3>\n
1.1. Perception<\/strong><\/h4>\n
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1.2. Planning<\/strong><\/h4>\n
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1.3. Action<\/strong><\/h4>\n
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1.4. Learning<\/strong><\/h4>\n
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2. Three Foundational Factors That Determine Effectiveness<\/h3>\n
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3. Supporting Technologies<\/h3>\n
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4. Real-world Example: Agentic AI in Supply Chain Operations<\/h3>\n
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III. Benefits & Risks of Agentic AI for Enterprises<\/h2>\n
1. Benefits<\/h3>\n
Strategic-level Automation<\/strong><\/h4>\n
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Faster and More Accurate Decision-making<\/strong><\/h4>\n
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Scalability Without Increasing Headcount<\/strong><\/h4>\n
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High Adaptability to Market Changes<\/strong><\/h4>\n
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2. Risks & Challenges<\/h3>\n
Goal Misalignment<\/strong><\/h4>\n
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Dependence on Data Quality<\/strong><\/h4>\n
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Security & Compliance Risks<\/strong><\/h4>\n
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Unpredictable Behavior<\/strong><\/h4>\n
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IV. Applications of Agentic AI in the Enterprise<\/h2>\n
1. Intelligent Operations Management<\/strong><\/h3>\n
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2. Automated Yet Personalized Customer Service<\/strong><\/h3>\n