What is agentic AI?
Learn what agentic AI is, its benefits, challenges, agentic AI use cases, and how to implement it to drive smarter, autonomous process automation.
What is agentic AI?
Agentic AI is a powerful concept in the field of artificial intelligence, and it represents a significant change in the way humans interact with AI. Agentic AI is a form of AI that is more capable than ever of solving complex, multi-step problems with a high degree of autonomy and reasoning—and it is being rapidly adopted in the market.
Agentic AI is first and foremost characterized by its ability to act independently. It’s a form of AI that is capable of complex reasoning, smart decision-making, and a capacity to act on those decisions—without requiring human intervention. This technology can enable massive new possibilities, but there are also risks and pitfalls to watch out for.
Key differences between traditional AI, generative AI, AI agents and agentic AI
Overview | Strengths | Uses | Examples | |
|---|---|---|---|---|
Traditional AI | Rule-based models based on predefined logic | Deterministic task execution, auditability | Analyze and understand a task, and respond according to rules | Recommendation engines |
Generative AI | Produces new content from prompts | Interacts in natural language, create human-like responses | Creates new code or summarizes text | GitHub Co-Pilot, ChatGPT |
AI Agent | Autonomous goal-based entities that perform tasks | Takes action towards a single goal, can learn | Automates customer service, personal “assistants” for specific goals | Customer service chatbot |
Agentic AI | Orchestrates multiple AI agents, incorporating human-in-the-loop oversight | Combines deterministic control with AI flexibility, manage complex end-to-end processes | Multi-step document processing (IDP), fraud detection, claims handling and approval | Driverless cars, Amazon automated fulfillment robots |
What is the difference between agentic AI vs generative AI?
How does agentic AI differ from traditional AI?
What’s the difference between agentic AI vs AI agents?
Benefits of agentic AI
The promise of agentic AI is immense. Agentic AI can autonomously solve complex problems and continuously improve through learning, transforming the way humans and AI collaborate to solve critical business problems. Agentic AI benefits include:
Increase productivity and efficiency at scale
Agentic AI provides a high degree of autonomy that can easily scale based on demand. By operating independently, it can accomplish tasks faster than a human could while freeing up humans for more complex goals. This has long been one of the chief benefits of automation, and agentic AI takes this to the next level.
Make smarter decisions
Agentic AI can not only consult data more quickly than a human, but it can also understand how it successfully solved a problem and, with no human intervention, incorporate that knowledge into its knowledge base for future use. By doing so, agentic AI can make smarter and faster decisions, potentially in ways a human would not have even considered.
Reduce operational costs
From avoiding fines due to compliance failures to allowing for more strategic allocation of human resources, using agentic AI to minimize human error and boost productivity can dramatically reduce the costs of your business operations.
Deliver better customer experiences
Agentic AI can help you resolve issues more quickly, with instant and effective personalized responses. This greatly reduces the number of cases that require time-consuming human involvement and helps customers get what they are looking for faster.
Challenges of agentic AI
For all its promise, agentic AI poses significant challenges that must be overcome before you can expect to return value from using it, let alone start trusting your most mission-critical processes to it, whether wholly or in part.
Visibility and trust
One of the biggest challenges with agentic AI is simply trust—left unguided, AI is known to make unexpected decisions or even hallucinate. Worse still, it often can’t tell you why it came up with the answer it did.
Governance and auditability
Agentic AI is powerful because it gives the AI autonomy to act, but how can you ensure it will follow appropriate ethical rules and compliance regulations?
Humans in the loop
There are times when humans need to be involved in the decision-making process, whether it’s in a designed way or on an ad-hoc basis. AI needs to call on humans whenever it’s appropriate.
Connections to other tools
The AI will only be as powerful as the data and tools it has access to. To use agentic AI properly, you need to be able to feed it new data and connect it to new tools easily as business needs change.
Discovering value with AI
Even after you have solved the challenges above, it can be difficult to know where to start. Simply “throwing AI” at a problem will not generate optimal value and could even set you up for a value trap, where a small point solution does not scale and creates yet more technical debt.
Overcoming the challenges of agentic AI
Agentic AI examples and use cases
There are many possible applications for agentic AI, spanning use cases as varied as software engineering, customer service, healthcare, financial services, process orchestration, and many more.
Application development and IT support
AI agents are capable of analyzing and rapidly producing large amounts of code, speeding up the production of new applications as well as helping with maintenance and support. AI agents can be further trained on company data and documentation, taking care of most of the coding and research, so developers and technicians can be free to focus on more creative or complex goals.Customer service
When questions come up, customers want fast and personalized answers. AI agents are able to access multiple tools and knowledge bases to understand a particular problem, investigate a solution, and ultimately provide it to the customer, all without human intervention.Healthcare
Both patients and providers can benefit from agentic AI in different ways. An AI agent could proactively monitor patients and provide alerts or information, or even schedule appointments. Clinicians can also take advantage of agentic AI to help them review patient data and make faster and more accurate diagnoses, as well as to generate thorough medical documentation.Financial services
Agentic AI opens up possibilities for AI agents to provide personalized financial recommendations and even take actions like buying and selling based on them. Agents could also detect anomalous transactions in real time and take an active role in preventing fraud.Marketing
AI has long been used to help research and even generate content, but agentic AI allows you to move beyond planning and into execution. AI agents can deploy content across channels, monitor performance with assigned tools, track user behavior and personalize messaging (both in terms of giving personalized next steps and also through persistent chat conversations with memory).Enterprise and business processes
There are many other use cases in an enterprise environment. A sales AI agent can validate pricing and contracts, an employee onboarding agent can set up IT accounts and trainings, or a compliance agent could continuously check to make sure regulatory policies are followed. In general, agentic AI helps you move to goal-based automation rather than purely rule-based flows.Additional resource: Agentic orchestration across industries
Download this infographic to learn:
- How enterprises are using AI to create value across customer experience, employee productivity, and IT efficiency
- Why orchestration is essential to scale AI
- Key accelerators to operationalize AI and drive enterprise-wide adoption
What are the most common agentic AI architectures?
There are many ways to implement agentic AI. Here are a few of the most popular:
Monolithic agents
A monolithic agent architecture uses one all-purpose agent to perform desired tasks, taking advantage of tools, memory, and execution. It can be the quickest to stand up and deploy and can work for single-purpose tasks, but it can be challenging to scale or maintain as complexity grows.Multi-agent
This architecture uses a number of specialized agents to collaborate to complete tasks. Specialization allows each agent to be well-trained in a target area and to work together, but it can be hard to gain visibility into how a decision was made when multiple agents were involved, and lag time during sometimes complex communication loops can slow performance.Reactive agents
The reactive agent architecture involves deploying stateless agents that purely react to a query, without the need for long-term planning or memory. This enables rapid and reliable responses to queries or monitoring, but it’s not able to learn, adapt to long-term goals or handle multi-step complexity.Orchestrated agents
Using an orchestrated agent architecture, also known as agentic orchestration, pairs an agent for making dynamic decisions with an orchestration engine for more deterministic or human-centric decisions. This setup enables strong process governance and complete visibility and auditability, as well as a high degree of modularity as agents can be easily updated/managed from the engine. While it can handle complex tasks exceptionally well, it does require orchestration tooling as well as agentic capabilities to implement.How does agentic AI work with Camunda?
Agentic AI operates through a multi-step process:
- Understanding the goal: AI agents receive a prompt and determine what will be needed to achieve the goal.
- Planning: The agent will create a plan to achieve the goal based on its understanding of what is needed, using techniques such as “chain of thought” reasoning to map out its thinking.
- Execution: Next, the agent will act to complete the goal. It can take advantage of any tools it has at its disposal or call APIs to do so.
- Remember: Agents need to be able to remember what has happened in order to provide a good experience and improve over time. While not all architectures will use long-term memory, most will at least use short-term memory that remembers what has happened within a current conversation.
See Agentic Orchestration in Action
- Repeat: Particularly if long-term memory is implemented, agents can learn to improve by repeating this process and reflecting on the results. They can then learn from good or bad outcomes and adapt their reasoning for future decisions.
- Orchestration: Particularly in complex or regulated environments, orchestration allows the agent to more easily work with other agents and keep humans in the loop, while giving complete visibility and auditability to IT.
In short, agentic AI understands, decides, and acts, often in self-learning loops that can be orchestrated to enhance productivity and safety.
Getting started with agentic AI with Camunda
Agentic AI is an exciting development in artificial intelligence and can open up significant benefits in terms of automation and productivity for an organization. Camunda makes it easy to get started with agentic AI by implementing it with our orchestration engine and visualizing the entire end-to-end process with BPMN.
As a composable platform, Camunda allows you to implement agentic AI alongside the tools and systems you are already using today. As business needs evolve over time, our open architecture makes it simple to add, remove, or update tooling as you go, without locking you into any particular technology stack. Agentic orchestration is currently being used widely across industries today.
Of course, Camunda also has everything you need to get started with end-to-end process orchestration built-in. Whether you are building an entirely new process or seeking to improve the performance of an existing one, Camunda is here to help you get more value quickly.Learn more about what sets Camunda apart and about how you can get started with agentic orchestration today.
Additional resources:
Transforming process orchestration with agentic AI
Embrace AI to reduce costs, mitigate risks, and enhance customer satisfaction.
Download guide
Why agentic process orchestration belongs in your automation strategy
How to make agentic orchestration work seamlessly within your end-to-end business processes.
Download guide
2026 State of Agentic Orchestration and Automation
Learn how 1,150 IT leaders are using AI agents today and what’s preventing them from scaling in production.
Download report