--- title: "What Is Agentic AI? Definition, Architecture & How It Differs from Generative AI" url: https://alicelabs.ai/en/insights/what-is-agentic-ai description: "Agentic AI means AI systems that autonomously perceive, reason, plan, and act using tools. Learn the definition, architecture patterns, and how agentic AI differs from generative AI." lang: en --- Alice Labs (https://alicelabs.ai/en/) Services What we do (https://alicelabs.ai/#welcome)About Alice (https://alicelabs.ai/#who-we-are)Case (https://alicelabs.ai/en/case)Insights (https://alicelabs.ai/en/insights)Contact (https://alicelabs.ai/#email-form) AI Agents Definition Fresh Last reviewed: 19 September 2026 · 18d ago # Agentic AI /eɪˈdʒɛn.tɪk ˌeɪ.aɪ/ A paradigm in artificial intelligence where systems autonomously perceive, reason, plan, and act using tools in a continuous loop — going beyond single-turn generation to execute multi-step tasks toward a defined goal. Also known as: Agentic artificial intelligence · Autonomous AI agents · AI agents · Agentic systems ## TL;DR Quick Answer Cited by AI > Agentic AI refers to AI systems that autonomously perceive, reason, plan, and act using tools in a loop to achieve goals. Unlike generative AI, which produces content from a single prompt, agentic AI takes multi-step autonomous actions with memory and tool use. Image: Eric Lundberg - Author at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Written by Eric Lundberg: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Image: Linus Ingemarsson - Reviewer at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Reviewed by Linus Ingemarsson: https://www.linkedin.com/in/linus-ingemarsson/ Published April 16, 2026 · Updated September 19, 2026 8 min read ## In context Autonomous research pipeline "An agentic AI system receives a research brief, searches academic databases, retrieves and summarizes papers, identifies gaps, and produces a structured literature review — iterating until quality criteria are met." Multi-step customer service "An agentic AI handles a refund request by reading the ticket, querying the order system, checking the refund policy, processing the return via the payment API, and sending the confirmation email — without human intervention." DevOps incident response "An agentic AI detects an alert from a monitoring system, queries logs, diagnoses the root cause, applies a known remediation via infrastructure-as-code, and opens a post-incident ticket for human review." Enterprise data analysis "An agentic AI receives a business question, writes SQL queries against a data warehouse, interprets results, generates charts, and iterates on the analysis until the stakeholder question is fully answered." ## Related terms AI Agent: https://alicelabs.ai/en/insights/what-is-an-ai-agent AI Agent Frameworks: https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026 LLM (Large Language Model): https://alicelabs.ai/en/insights/what-is-an-llm Tool use / Function calling: https://alicelabs.ai/en/insights/what-is-function-calling ## Key points - Agentic AI describes AI systems that autonomously perceive, reason, plan, and act using tools in a loop. Generative AI produces content from prompts; agentic AI takes autonomous multi-step actions. - Gartner named agentic AI a top strategic technology trend for 2025, signaling mainstream enterprise adoption (Gartner, October 2024). - Andrew Ng (Stanford) popularized the 'agentic workflows' concept in 2024, defining four core patterns: reflection, tool use, planning, and multi-agent collaboration. - Anthropic defines agents as 'LLMs using tools in a loop' — the simplest and most precise framing (Anthropic, December 2024). - Key difference from RPA: agentic AI handles unstructured tasks, adapts to new situations, and doesn't need pre-programmed rules for every scenario. - Deloitte's State of Generative AI in the Enterprise (Q1 2026) reports that 25% of companies using generative AI are expected to launch agentic AI pilots in 2025, growing to 50% by 2027 — the fastest enterprise adoption curve Deloitte has tracked for any AI capability. 01 / 05 Section ## Agentic AI Definition: What Makes AI 'Agentic'? In short Agentic AI refers to AI systems that go beyond single-turn generation by autonomously perceiving inputs, reasoning about goals, planning multi-step strategies, acting through tool use, and reflecting on results. The defining characteristic is autonomous, goal-directed behavior over multiple steps. The term "agentic" comes from "agency" — the capacity to act independently. In AI, it marks a shift from passive response generation to active, goal-directed behavior. Anthropic's December 2024 engineering guide offers the clearest definition: agents are "LLMs using tools in a loop." That loop is the key differentiator. - **Passive AI** receives a prompt and returns a response. One turn, done. - **Agentic AI** receives a goal and autonomously decides what steps to take, which tools to call, and when the goal is met. Multiple turns, self-directed. Andrew Ng (Stanford) popularized the concept of "agentic workflows" in 2024, describing how LLMs become dramatically more capable when given tool access and iterative loops rather than single-turn prompts. 02 / 05 Section ## Agentic AI vs Generative AI: What's the Difference? In short Generative AI produces content (text, images, code) from a single prompt. Agentic AI takes that generation capability and wraps it in autonomous multi-step execution — using tools, memory, and planning to achieve goals over many turns. Generative AI is the engine; agentic AI is the vehicle. This is the most common point of confusion. Generative AI and agentic AI are not opposites — agentic AI builds on top of generative AI. | Dimension | Generative AI | Agentic AI | | --- | --- | --- | | Core action | Generates content from prompts | Takes autonomous multi-step actions | | Interaction model | Single-turn (prompt → response) | Multi-turn loop (goal → plan → act → observe → repeat) | | Tool use | Optional (code interpreter, search) | Central — tools are how agents act on the world | | Memory | Context window only | Short-term + long-term memory across sessions | | Decision-making | User decides next step | Agent decides next step autonomously | | Planning | None — responds to current prompt | Decomposes goals into sub-tasks and sequences them | | Example | ChatGPT answering a question | An AI agent researching, drafting, and publishing a report | Source: Alice Labs analysis Image: Linus Ingemarsson (https://alicelabs.ai/images/linus-ingemarsson.png) Image: Eric Lundberg (https://alicelabs.ai/images/eric-lundberg.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. Book a Discovery Call 03 / 05 Section ## Agentic AI Architecture: The Perception-Action Loop In short Agentic AI systems follow a five-phase loop: Perception (intake inputs), Reasoning (interpret and understand), Planning (decompose into steps), Action (execute using tools), and Reflection (evaluate results and decide whether to continue). This loop runs until the goal is achieved or a stopping condition is hit. Every agentic AI system — regardless of framework — implements a variation of this core loop. The phases map directly to Andrew Ng's four agentic patterns. 1. **Perception.** The agent receives inputs: a user goal, sensor data, incoming messages, or results from a previous action. 2. **Reasoning.** The LLM interprets the inputs, considers available tools and current state, and decides what needs to happen next. 3. **Planning.** For complex goals, the agent decomposes the task into sub-steps and sequences them. This is Ng's "planning" pattern. 4. **Action.** The agent executes — calling an API, running code, querying a database, or delegating to another agent. This is "tool use" in Ng's framework. 5. **Reflection.** The agent evaluates the result: Did the action succeed? Is the goal met? Should I revise my plan? This is Ng's "reflection" pattern. The fourth Ng pattern — **multi-agent collaboration** — emerges when multiple agents run this loop in parallel or sequence, each specializing in a different sub-task. Alice Labs practitioner team ## Ready to implement agentic AI? Alice Labs designs and builds production agentic AI systems — with governance, observability, and human-in-the-loop controls — for enterprises across Sweden and Europe. Talk to an agent engineer 04 / 05 Section ## Agentic AI Use Cases: Where It Delivers Real Value In short Agentic AI delivers the most value in tasks that are multi-step, require tool use, involve unstructured inputs, and benefit from adaptive decision-making. Leading use cases include customer service automation, code generation and debugging, research and analysis, sales prospecting, and IT operations. Agentic AI excels where traditional automation (RPA, rule-based workflows) breaks down — tasks with unstructured inputs and unpredictable execution paths. - **Customer service.** Agent reads ticket, queries order system, checks policy, processes action, and responds — handling cases RPA cannot because the input is unstructured natural language. - **Software engineering.** Coding agents (Claude Code, Cursor, Devin) read bug reports, navigate codebases, write fixes, run tests, and iterate. They adapt to each codebase rather than following fixed rules. - **Research and analysis.** Agent searches databases, retrieves documents, extracts data, synthesizes findings, and generates structured reports — iterating until quality criteria are met. - **Sales prospecting.** Agent enriches leads from public sources, scores fit against ICP criteria, drafts personalized outreach, and updates the CRM with recommended next actions. - **IT operations.** Agent monitors alerts, diagnoses incidents by querying logs, applies remediations, and escalates to humans when confidence is low. The common thread: these tasks require adapting to novel inputs, not following a fixed script. That's the gap agentic AI fills that RPA cannot. 05 / 05 Section ## When to Use Agentic AI (and When Simpler Solutions Win) In short Use agentic AI when the task requires dynamic multi-step reasoning, tool use, and adaptation to unpredictable inputs. Use simpler approaches — generative AI, workflows, or deterministic code — when the task path is predictable, single-turn, or requires strict determinism. Not every AI use case needs agentic AI. Anthropic's December 2024 guide explicitly warns against over-engineering: start with the simplest solution that works. Alice Labs applies the same test when scoping enterprise AI agents (https://alicelabs.ai/en/ai-agents) engagements. Compare implementation partners in our roundup of ai agent development companies (https://alicelabs.ai/en/insights/ai-agent-development-companies-2026). - **Use agentic AI** when: the task path is unpredictable, multiple tools are needed, and iterative refinement materially improves results. - **Use a workflow** when: steps are known in advance, even if some steps involve LLM calls. Cheaper, faster, more predictable. - **Use generative AI (single-turn)** when: the task is a single prompt-to-response interaction — summarization, translation, content drafting. - **Use deterministic code** when: the logic is strict, math-heavy, or requires exact business rules. LLMs are not calculators. | Capability | RPA | Traditional Automation | Agentic AI | | --- | --- | --- | --- | | Handles unstructured input | No | No | Yes | | Adapts to new scenarios | No | Limited | Yes | | Requires pre-programmed rules | Yes | Yes | No — learns from context | | Multi-step reasoning | Scripted only | If-then logic | Dynamic, LLM-driven | | Cost per task | Low | Low | Higher (LLM inference costs) | | Best for | Repetitive, structured tasks | Known workflows | Complex, unpredictable tasks | Source: Alice Labs analysis Alice Labs practitioner team Book a Discovery Call ## About the Authors & Reviewers Written by Image: Eric Lundberg - Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/eric-lundberg.png) Eric Lundberg (https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs View profile: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Eric Lundberg on LinkedIn: https://www.linkedin.com/in/eric-lundberg-3530451bb/ Email Eric Lundberg: eric@alicelabs.ai Reviewed by September 19, 2026 Image: Linus Ingemarsson - CEO & Co-Founder, Alice Labs at Alice Labs (https://alicelabs.ai/images/linus-ingemarsson.png) Linus Ingemarsson (https://www.linkedin.com/in/linus-ingemarsson/) CEO & Co-Founder, Alice Labs CEO & Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements View profile: https://www.linkedin.com/in/linus-ingemarsson/ Linus Ingemarsson on LinkedIn: https://www.linkedin.com/in/linus-ingemarsson/ Email Linus Ingemarsson: linus@alicelabs.ai Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is agentic AI in simple terms? Agentic AI is AI that can act on its own to achieve goals. Instead of just answering a question (like a chatbot), an agentic AI system can plan steps, use tools (search the web, call APIs, write code), and keep working until the task is done — without a human directing each step. ### What is the difference between agentic AI and generative AI? Generative AI (like ChatGPT) generates content from a prompt — one turn, one response. Agentic AI takes that generation capability and adds autonomous multi-step action: it plans, uses tools, observes results, and iterates. Generative AI is the engine; agentic AI is the self-driving vehicle built around it. ### What is the difference between agentic AI and AI agents? Agentic AI is the paradigm — the idea that AI systems should act autonomously with tools and planning. AI agents are the concrete software artifacts that implement agentic AI. Think of 'agentic AI' as the design philosophy and 'AI agent' as the thing you build. ### Why did Gartner name agentic AI a top trend for 2025? Gartner predicted that agentic AI will move from experimental to operational in enterprises. Their forecast: by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. The driver is autonomous task execution — reducing the need for humans to supervise every step. ### What are the main frameworks for building agentic AI? The leading open-source frameworks in 2026 are LangGraph (from LangChain), CrewAI, AutoGen/AG2 (Microsoft), and Semantic Kernel (Microsoft). LangGraph gives fine-grained control over the agent loop. CrewAI focuses on multi-agent role-based collaboration. AutoGen specializes in conversational multi-agent patterns. ### How is agentic AI different from RPA? RPA (Robotic Process Automation) follows pre-programmed rules on structured data — it clicks buttons in a fixed sequence. Agentic AI handles unstructured inputs (natural language, images), adapts to situations it hasn't seen before, and makes dynamic decisions. RPA is best for repetitive, predictable tasks; agentic AI handles the unpredictable ones. ### Is agentic AI safe for production use? Yes, with proper guardrails. Production-safe agentic AI includes: scoped tool access (limit what the agent can do), human-in-the-loop approval for high-risk actions, input/output guardrails, comprehensive logging and observability, and evaluation harnesses to catch regressions. Anthropic's 'Building effective agents' guide covers these patterns in detail. ### What are the characteristics of agentic AI? Agentic AI has five defining characteristics: (1) autonomy — decides its own next step without a hardcoded script; (2) goal-directedness — works toward a stated objective rather than a single response; (3) tool use — invokes APIs, code, and databases to act on the world; (4) memory — retains context across many turns, often with long-term storage; (5) reflection — evaluates its own outputs and revises. Deloitte (Q1 2026) identifies these five as the enterprise readiness checklist. ### What is the definition and characteristics of agentic AI? Agentic AI is a paradigm where AI systems autonomously perceive, reason, plan, and act using tools in a continuous loop toward a defined goal. Its four core characteristics, per Andrew Ng's 2024 framework, are: reflection (self-critique), tool use (external action), planning (task decomposition), and multi-agent collaboration. Gartner projects that 33% of enterprise software will embed these characteristics by 2028, up from under 1% in 2024. ### What did Andrew Ng mean by 'agentic workflows'? In 2024, Andrew Ng described four design patterns that make LLMs dramatically more capable: reflection (self-critique), tool use (calling external systems), planning (decomposing goals into steps), and multi-agent collaboration (multiple specialized agents working together). He argued these 'agentic' patterns unlock capabilities far beyond single-turn prompting. Previous in AI Agents ### Best AI Agent Frameworks 2026: 6 Compared (Open-Source) https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026 Next in AI Agents ### Open Source AI Agent Frameworks Comparison 2026 https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026 ## Further reading - Gartner — Top 10 Strategic Technology Trends for 2025 (https://www.gartner.com/en/articles/gartner-top-10-strategic-technology-trends-for-2025)· gartner.com - Anthropic — Building effective agents (Dec 2024) (https://www.anthropic.com/research/building-effective-agents)· anthropic.com - LangGraph documentation (https://langchain-ai.github.io/langgraph/)· langchain-ai.github.io - Microsoft AutoGen / AG2 documentation (https://microsoft.github.io/autogen/)· microsoft.github.io ## Related services AI Agent Development Services Alice Labs builds custom agentic AI systems for enterprise clients.: https://alicelabs.ai/en/ai-agents ## Related reading quick take ### What Is an AI Agent? The precise definition, four-component architecture, and real-world examples. 7 min https://alicelabs.ai/en/insights/what-is-an-ai-agent deep dive ### Best AI Agent Frameworks 2026 The 6 leading open-source agent frameworks compared. 11 min https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026 deep dive ### AI Agent Orchestration Autonomous agent orchestration best practices — how the control loop is designed in real deployments. 10 min https://alicelabs.ai/en/insights/ai-agent-orchestration deep dive ### Enterprise AI Strategy: 6-Step Framework Where agentic AI fits in the broader enterprise AI strategy. 12 min https://alicelabs.ai/en/insights/enterprise-ai-strategy-framework ## Sources 1. Gartner — Top 10 Strategic Technology Trends for 2025 (October 2024) (https://www.gartner.com/en/articles/gartner-top-10-strategic-technology-trends-for-2025)(accessed 2026-04-16) 2. Anthropic — Building effective agents (engineering guidance, December 2024) (https://www.anthropic.com/research/building-effective-agents)(accessed 2026-04-16) 3. Andrew Ng — Agentic Design Patterns (talks and blog posts, 2024) (https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/)(accessed 2026-04-16) 4. LangGraph documentation (LangChain) (https://langchain-ai.github.io/langgraph/)(accessed 2026-04-16) 5. Microsoft AutoGen / AG2 — Multi-agent framework documentation (https://microsoft.github.io/autogen/)(accessed 2026-04-16) 6. CrewAI — Multi-agent orchestration framework documentation (https://docs.crewai.com/)(accessed 2026-04-16) Next scheduled review: 2026-12-18 Alice Labs practitioner team Book a Discovery Call Share Share on LinkedIn (https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fwhat-is-agentic-ai)Share on X (https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fwhat-is-agentic-ai&text=What%20Is%20Agentic%20AI%3F%20Definition%2C%20Architecture%20%26%20How%20It%20Differs%20from%20Generative%20AI) ## Get in Touch! The lab usually responds within 24 hours. Need help with AI? 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CrewAI focuses on multi-agent role-based collaboration. AutoGen specializes in conversational multi-agent patterns." } }, { "@type": "Question", "name": "How is agentic AI different from RPA?", "acceptedAnswer": { "@type": "Answer", "text": "RPA (Robotic Process Automation) follows pre-programmed rules on structured data — it clicks buttons in a fixed sequence. Agentic AI handles unstructured inputs (natural language, images), adapts to situations it hasn't seen before, and makes dynamic decisions. RPA is best for repetitive, predictable tasks; agentic AI handles the unpredictable ones." } }, { "@type": "Question", "name": "Is agentic AI safe for production use?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, with proper guardrails. Production-safe agentic AI includes: scoped tool access (limit what the agent can do), human-in-the-loop approval for high-risk actions, input/output guardrails, comprehensive logging and observability, and evaluation harnesses to catch regressions. 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