By Rad Anandakumar, Practice Lead, Data and AI, Avocado Consulting
In short: Generative AI creates content, such as text, images and code, in response to a prompt. Agentic AI works towards a goal by planning, selecting actions and using tools across multiple steps. Its autonomy depends on the permissions and oversight you define. Many agentic systems use generative AI as part of their reasoning and content creation.
The terms "agentic AI" and "generative AI" are often used interchangeably. The distinction matters when you decide what an AI system should produce, which systems it can access and which decisions still need your approval.
To make the most of both technologies, and choose the right one for each problem, it helps to understand what each does, where they differ and where they overlap.
What is generative AI?
Generative AI is artificial intelligence that creates new content, such as text, images, audio or code, based on patterns learned from its training data. It's the technology most people already know through tools like ChatGPT, Claude, Gemini, Copilot and Midjourney.
Text-focused generative AI commonly uses large language models (LLMs), while other model architectures generate images, audio and video. A generative model produces content from its inputs. Assistants built around these models can also include tools and agentic capabilities, so the same product can support both approaches.
What is agentic AI?
Agentic AI is artificial intelligence designed to pursue a goal: planning steps, selecting actions, using tools and adjusting its approach based on feedback. An agent can work through several steps within defined permissions, with human approval where the task or risk requires it.
For your organisation, the practical distinction is what the system is authorised to do. An agent can select the next step and respond to new information, but it still needs clear operating boundaries, monitoring and escalation rules.
In a business setting, that might mean an agent that triages a support ticket, looks up the customer's account, drafts a response, updates the CRM and escalates to a person when confidence is low or policy requires human approval.
What's the difference between agentic AI and generative AI?
The main difference is autonomy. Generative AI produces an output when asked. Agentic AI decides what to do next and does it, across multiple steps and systems, until it reaches a goal.
| Comparison | Generative AI | Agentic AI |
|---|---|---|
| Purpose | Creates content in response to a prompt | Achieves a goal by planning and taking actions |
| How it works | Prompt in, response out | Plans, acts, checks results and adjusts in a loop |
| Autonomy | Low: needs a person to prompt and guide each step | Varies: acts within defined permissions, with human approvals and escalation rules |
| Task complexity | Single-step tasks, like drafting an email or summarising a document | Multi-step tasks that need context retained over time |
| System integration | Mostly interacts with the user | Uses tools, APIs and business systems to get work done |
| Adapting | Uses training and supplied context; model weights usually remain fixed during a task | Adjusts its approach using results and new data; this does not imply retraining |
| Core technology | LLMs and other transformer-based models | Usually an LLM plus planning, memory, tool use and guardrails |
| Examples | ChatGPT, Claude, Gemini, Copilot, Midjourney | Customer service agents, coding agents, workflow and operations agents |
| Example goal | Write a blog post or generate an image | Resolve a support ticket or reconcile invoices end to end |
What are some real-world examples of agentic AI?
Software agents can support research, planning and business processes. Autonomous vehicles and agricultural drones illustrate related perception, planning and action capabilities, but they are distinct engineering systems rather than direct equivalents of LLM-based business agents.
Autonomous vehicles. Self-driving systems use sensor data to plan movement and respond to road conditions within defined operating limits. They illustrate autonomous decision-making, but should not be treated as the same architecture as a business agent built around an LLM.
Research and planning agents. AI agents can take a goal, such as planning a business trip, and work through it independently: searching for flights, comparing accommodation and building an itinerary around the traveller's preferences. They draw on multiple data sources and adjust their plan as they learn more.
Business process automation. AI agents can coordinate invoicing, data entry, scheduling and reporting across ERP systems, email and spreadsheets. They can assess exceptions and select permitted next steps, with human approval for actions such as payments or changes to sensitive records.
Agricultural drones. Depending on their configuration, drones can combine crop imaging, flight planning and targeted spraying. These capabilities illustrate physical automation; they do not, on their own, establish that a drone uses an LLM-based agent.
How is agentic AI different from RPA?
Robotic process automation (RPA) follows fixed rules to automate repetitive tasks. Agentic AI decides how to reach a goal and adapts when things change. RPA is reliable for predictable, high-volume work. Agentic AI suits work that needs judgement, or where inputs vary.
| Comparison | RPA | Agentic AI |
|---|---|---|
| Purpose | Automates repetitive, rule-based tasks | Achieves goals through decisions and multi-step actions |
| Task complexity | Simple, predictable tasks that follow a script | Complex tasks that need decisions and adaptation |
| Decision-making | Fixed rules that change only when someone updates them | Decides based on context and real-time data |
| Adaptability | May need reconfiguration when inputs or interfaces change | Can adjust within its capabilities, permissions and guardrails |
| Examples | Data entry, invoice processing, payroll, form filling | Support ticket resolution, exception handling, multi-system workflows |
The two increasingly work together. RPA handles the predictable steps, while AI agents handle the exceptions and decisions in between.
Will agentic AI replace generative AI?
No. They're complementary, not competing. Generative AI excels at creating content, and agentic AI is designed to make decisions and carry out multi-step tasks.
In fact, generative AI usually powers agentic AI. Most AI agents use an LLM to understand instructions, reason through a plan and generate the content each step needs. The agentic layer adds the planning, memory, tool use and guardrails that turn those outputs into action.
The future is likely to blend both: agents that can run a process, make decisions and produce the emails, reports and designs they need along the way. For organisations, the more useful question isn't which one to choose, but which problems need content and which need action.
Ready to put AI to work?
Avocado's AI and Data practice helps organisations decide where generative and agentic AI will deliver real value, then build it safely on a trusted data foundation. Not every organisation needs autonomous AI, and most shouldn't attempt it without the right foundations in place. We'll help you work out where you are and what makes sense next.
Book a discovery session to discuss your use case, explore our AI Strategy Workshop, or see our AI and Data solutions.
About the author
Rad Anandakumar is Practice Lead, Data and AI at Avocado Consulting. He founded AI Consulting Group, which joined Avocado in 2026. With more than 20 years in data analytics, machine learning and AI, he has led digital transformations and AI-powered business solutions using generative AI, IoT and predictive analytics.
