What began with content generation is now moving toward autonomous agentic systems. Generative AI and agentic AI push this shift in different ways. Generative AI is widely adopted, yet its business impact is uneven (Sukharevsky et al., 2025). Although it excels at drafting and summarizing, it does not complete tasks end-to-end.
Agentic AI goes further. It plans the work, executes each step, and adapts as conditions change. Adoption is moving fast, and many enterprises have already moved beyond pilots (KPMG, 2025). Both approaches rely on one constant: high-quality AI training data.
For enterprises and developers, knowing where each approach fits is essential for the next wave of AI solutions. This article explains the differences between Generative AI and Agentic AI, shares real examples, and shows how they can work together to deliver stronger results.
Understanding Generative AI vs. Agentic AI
Put simply, Generative AI creates new material in response to prompts, while Agentic AI is an autonomous system that can plan, execute, and adapt to finish a multi-step task. Let's examine both technologies more closely:
What is Generative AI?
Generative AI models learn patterns from large datasets and produce new content in response to user queries. They assist with drafting, summarizing, and adapting material across various formats, including text, code, images, and audio. The quality of their outputs tracks the quality of their training data. Without diverse, well-labeled examples, models drift or produce inconsistent results when they face unfamiliar domains.
Generative AI also needs the right tooling. Teams use annotation to build reliable datasets, quality assurance (QA) to verify accuracy, and evaluation to keep outputs consistent and safe as models evolve. Appen’s AI Data Platform (ADAP) brings these pieces together at scale.
What is Agentic AI?
Agentic AI refers to autonomous systems that plan, act, and adapt to complete tasks (Marr, 2025). Unlike generative models, which create outputs from prompts, agents handle multi-step workflows by breaking down tasks, calling tools or APIs, and adjusting based on feedback.
For example, an e-commerce return agent verifies orders, generates shipping labels, notifies customers, and updates inventory, without human intervention. Operational records track each action, which aids in evaluation, tuning, and auditing. Human oversight steps ensure that risks are managed effectively.
Agentic AI often works in conjunction with generative models. While agents manage the workflow, generative AI can be embedded to assist with tasks such as drafting emails or summarizing reports, or suggesting which tool to use. This combination of generative outputs and agentic AI workflows ensures both creativity and completion.
Key Differences Between Agentic AI and Generative AI
Agentic AI and generative AI differ in their goals, inputs, outputs, and evaluation, as shown in the table below.
These technologies share a synergistic relationship; generative AI creates fluent content, while agentic AI ensures structure and completion.
Real-World Use Cases
Generative and agentic AI shine in different parts of the workflow. Generative models excel at producing content from prompts, whereas agentic systems are for workflow automation.
Generative AI
- Tools like GitHub Copilot boost developer productivity by suggesting context-aware code and drafting functions from natural language prompts. Studies show developers code up to 55% faster, focusing on creative problem-solving rather than repetitive tasks (Brady, 2023).
- Amazon’s Enhance My Listing tool uses generative AI to optimize product titles and descriptions. Adopted by over 900,000 sellers, it improves listing quality by 40%, with 90% of content accepted unchanged, driving sales growth (Westmoreland, 2024). Learn more retail AI solutions.
- Generative AI tools, such as ChatGPT, are used by 58% of employees to draft emails, summarize reports, or analyze data, resulting in a 67% improvement in efficiency (Gillespie & Lockey, 2025).
Agentic AI
- Agentic AI automates long-tail supplier renewals by running end-to-end negotiation playbooks (proposal → counteroffer → agreement) and writing outcomes back to sourcing systems. Result in faster cycles on thousands of small contracts while humans focus on strategic categories. Walmart is using this type to optimize supplier contract management (Hoek et al., 2022).
- Agentic security workflows auto-investigate alerts across identity, endpoints, and cloud; correlate context; draft incident timelines; and initiate guided remediation with approval gates. This reduces manual triage and speeds time-to-contain in daily SOC operations. Microsoft is using this type to optimize security incident response (Li, 2025).
- An on-call SRE agent like Datadog’s Bits AI SRE investigates alerts, triages root causes, shares live summaries, and automates runbook steps—helping teams standardize incident handling and reduce MTTR (Tai, 2025).
Can Agentic and Generative AI Work Together?
Yes. Most production teams blend them. The agent breaks down the goal into steps, selects the appropriate tools, and verifies each result. The generative model creates what the workflow needs next, such as a counteroffer, an incident note, or an SQL query. After each step, the agent validates, executes, logs the outcome, and learns from the feedback. That loop moves work from idea to done with fewer handoffs.
The data story differs on each side. Generative models learn from curated and annotated datasets. Agents learn from live interaction logs and preference signals. Many teams incorporate retrieval from trusted sources to ensure their outputs remain grounded and current.
Two common hurdles are integration and RLHF. Integration means connecting to the systems you already use while following policy and change control. While reinforcement learning from human feedback (RLHF) incorporates human intelligence to review or rank outputs, and the system learns those preferences. As a result, future actions are improved.
When done well, hybrids deliver speed and reliability, creating room for innovation.
Ready to Power Your AI With Appen
Understanding the patterns is the first step. The next step is high-quality, reviewable data that holds up in production. Appen delivers high-quality AI data across text, image, audio, and video, enabling generative and agentic systems to improve over time. Generative models gain accuracy and style control. Agents gain better decision quality and safer tool use.
Looking to deploy the right AI for your use case? Collaborate with Appen’s experts to guide your AI strategy with tailored data solutions for GenAI and agentic systems.
References
Brady, D. (2023, April 14). How generative AI is changing the way developers work. The GitHub Blog. https://github.blog/ai-and-ml/generative-ai/how-generative-ai-is-changing-the-way-developers-work/
Gillespie, N., & Lockey, S. (2025, April 28). Major survey finds most people use AI regularly at work – but almost half admit to doing so inappropriately. The Conversation. https://theconversation.com/major-survey-finds-most-people-use-ai-regularly-at-work-but-almost-half-admit-to-doing-so-inappropriately-255405
Hoek, R. V., DeWitt, M., Lacity, M., & Johnson, T. (2022, November 8). How Walmart Automated Supplier Negotiations. Harvard Business Review. https://hbr.org/2022/11/how-walmart-automated-supplier-negotiations
KPMG. (2025, June 26). AI Quarterly Pulse Survey: Q2 2025 [Review of AI Quarterly Pulse Survey: Q2 2025]. KPMG; KPMG. https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2025/ai-quarterly-pulse-survey-q2.pdf
Li, Dorothy. (2025, March 24). Automate cybersecurity at scale with Microsoft Security Copilot agents. TECHCOMMUNITY.MICROSOFT.COM. https://techcommunity.microsoft.com/blog/securitycopilotblog/automate-cybersecurity-at-scale-with-microsoft-security-copilot-agents/4394675
Marr, B. (2025, February 3). Generative AI Vs. Agentic AI: The Key Differences Everyone Needs To Know. Forbes. https://www.forbes.com/sites/bernardmarr/2025/02/03/generative-ai-vs-agentic-ai-the-key-differences-everyone-needs-to-know/
Tai, K. X. (2025, June). Introducing Bits AI SRE, your AI on-call teammate | Datadog. Datadog. https://www.datadoghq.com/blog/bits-ai-sre/
Sukharevsky, A., Kerr, D., Klemens Hjartar, Lari Hämäläinen, Bout, S., & Leo, V. D. (2025, June 13). Seizing the agentic AI advantage. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage
Westmoreland, M. B. (2024, September 19). How Amazon uses generative AI to help sellers and shoppers. Aboutamazon.com; US About Amazon. https://www.aboutamazon.com/news/innovation-at-amazon/amazon-generative-ai-seller-growth-shopping-experience