Automation Anywhere AI Agents: First Look at the Future of No-Code Automation
Traditional RPA bots follow rigid rules - but what if your automations could think, plan, and adapt like humans? Automation Anywhere's new AI Agents (currently in preview) bring LLM-powered intelligence to the Control Room. See how these agents differ from traditional bots and learn how to build your first AI Agent without writing code.
What Are AI Agents in Automation Anywhere?
AI Agents represent a fundamental shift in how automation works within the Automation Anywhere platform. Unlike traditional RPA bots that follow predefined, rigid rules, AI Agents are smart, self-driven systems powered by large language models (LLMs). These agents can understand goals you set, plan actions to achieve those goals, and complete tasks autonomously.
The key differentiator is adaptability. Where traditional bots fail when faced with unexpected scenarios, AI Agents can reason through problems, access different tools as needed, and even collaborate with other agents in multi-agent workflows. This makes them ideal for complex, dynamic processes that would be difficult or impossible to automate with conventional RPA.
Game-changing capability: AI Agents can handle processes that require decision-making, interpretation of unstructured data, and adaptation to changing conditions - all without human intervention.
Key Components of an AI Agent
Every AI Agent in Automation Anywhere consists of several core components that work together to enable intelligent automation:
1. Role Definition
This establishes the agent's purpose and personality - similar to how you would brief a human employee. For example, a claims evaluation agent would be given the role of an insurance claims adjuster.
2. Goals and Action Plans
Unlike traditional bots that execute specific steps, AI Agents are given objectives and develop their own plans to achieve them. The action plan outlines the general approach while allowing flexibility in execution.
3. Tools Access
Agents can be granted access to various tools including other agents, existing TaskBots, API tasks, and forms. This toolkit enables them to complete complex, multi-step processes.
4. Model Connections
Agents connect to LLMs that provide their reasoning and decision-making capabilities. Different models can be selected based on the task requirements.
No-code advantage: All these components are configured through a visual interface - no programming required. Building an AI Agent is as simple as creating a TaskBot.
Traditional RPA vs. AI Agents
Understanding when to use traditional RPA versus AI Agents is crucial for automation success. Each approach has distinct strengths:
| Feature | Traditional RPA | AI Agents |
|---|---|---|
| Decision Making | Follows strict rules | Can reason and adapt |
| Process Complexity | Best for linear processes | Handles dynamic workflows |
| Tool Usage | Fixed set of actions | Dynamically selects tools |
| Exception Handling | Requires predefined rules | Can improvise solutions |
| Implementation | Step-by-step recording | Goal-oriented configuration |
The rule of thumb: Use traditional RPA for predictable, repetitive tasks and AI Agents for processes that require judgment, interpretation, or adaptation.
Best Use Cases for AI Agents
AI Agents shine in scenarios that would be challenging for traditional automation. Some prime examples include:
Claims Processing
An AI Agent can evaluate insurance claims by reviewing documentation, determining validity based on policy rules, and generating concise reports - handling the entire process from submission to decision.
Research Assistants
Agents can be configured to gather information on specific topics, summarize findings, and deliver reports through email or chat interfaces - perfect for staying updated on industry trends.
Customer Service Routing
AI Agents can analyze customer inquiries, understand intent, and route cases to the appropriate department or resource - adapting to new query types without reprogramming.
Key indicator: If your process requires interpretation of unstructured data or adaptation to changing conditions, it's likely a good candidate for AI Agent automation.
How to Build an AI Agent
Creating an AI Agent in Automation Anywhere's Control Room follows a straightforward process:
Step 1: Access the AI Agent Interface
Navigate to the new AI Agent section in Control Room (currently marked as preview). Click "Create" to start a new agent.
Step 2: Define Agent Properties
Give your agent a meaningful name and optionally use the prompt generator to create an initial configuration based on a natural language description of what you want the agent to do.
Step 3: Configure Core Components
Set up the agent's role, goals, and action plan. These elements guide the agent's behavior and decision-making process.
Step 4: Connect Tools
Add the tools your agent will need to complete its tasks - this could include other agents, TaskBots, API tasks, or forms.
Step 5: Set Up Input/Output
Define the variables your agent will use to receive information and deliver results.
Step 6: Test and Optimize
Run your agent and use the built-in optimization features to refine its performance. The prompt optimizer can suggest improvements to your agent's configuration.
Pro tip: Start with a clear, specific goal for your agent. Well-defined objectives lead to more effective automations.
Tools and Integration Options
One of the most powerful features of AI Agents is their ability to leverage existing automation assets as tools:
Existing Automations as Tools
Your current TaskBots, MetaBots, and API tasks can be integrated into AI Agents as tools they can call upon when needed. This protects your RPA investments while adding AI capabilities.
Human-in-the-Loop
The human review toggle allows agents to pause and request human input when faced with situations requiring judgment beyond their configured capabilities.
Multi-Agent Workflows
Agents can work together, with one agent calling another to complete specialized tasks - similar to how human teams collaborate.
This tool-based architecture makes AI Agents incredibly flexible. Rather than rebuilding existing automations, you can incorporate them as components within your agent's toolkit.
Current Preview Status
As of October 2025, the AI Agent functionality in Automation Anywhere is in preview mode with several important considerations:
Preview Limitations
The feature is not yet generally available and shouldn't be used for production workloads. The interface and capabilities may change before official release.
Access Requirements
Currently, AI Agents are only available in preview environments. You'll need special access to explore this functionality.
Future Expectations
Based on the preview, we can expect AI Agents to become a core part of Automation Anywhere's platform, potentially changing how organizations approach automation strategy.
Strategic advice: Now is the time to experiment with AI Agents in test environments and identify use cases for when the feature becomes generally available.
Watch the Full Tutorial
See the AI Agent builder in action with our complete video walkthrough. At 3:45, you'll see how to configure a claims evaluation agent that can review submissions and generate reports autonomously.
Key Takeaways
Automation Anywhere's AI Agents represent a significant evolution in RPA technology, bringing LLM-powered intelligence to the Control Room:
In summary: AI Agents handle dynamic, goal-oriented tasks through reasoning and tool use rather than rigid scripting. While currently in preview, this technology will likely change how organizations approach automation strategy when it becomes generally available.
- AI Agents differ from traditional RPA by being goal-oriented rather than step-oriented
- No-code configuration makes AI Agents accessible without programming skills
- Best for processes requiring interpretation, judgment, or adaptation
- Can integrate with existing automations as tools in their toolkit
- Currently in preview - interface and features may change before GA
Frequently Asked Questions
Common questions about Automation Anywhere AI Agents
AI Agents in Automation Anywhere are intelligent, self-driven systems powered by LLMs that can understand goals, plan actions, and complete tasks autonomously. Unlike traditional rule-based RPA bots, AI agents can use multiple tools, handle complex workflows, and even collaborate with other agents.
These agents represent a significant evolution in automation technology, moving beyond rigid, predefined processes to more flexible, goal-oriented automation that can adapt to changing conditions.
- Powered by large language models (LLMs)
- Can understand goals and plan actions
- Work with multiple tools and other agents
Traditional RPA bots follow strict, predefined rules while AI Agents can make decisions, adapt to changing conditions, and handle goal-oriented tasks. AI Agents have access to memory, planning capabilities, and can use multiple tools dynamically based on the task requirements.
Where traditional bots fail when faced with unexpected scenarios, AI Agents can reason through problems and find solutions. This makes them suitable for much more complex and variable processes than conventional RPA can handle.
- Traditional RPA: Rule-based, rigid processes
- AI Agents: Goal-oriented, adaptable workflows
- AI Agents can handle unstructured data and exceptions
AI Agents excel at dynamic, goal-oriented tasks that require decision-making and adaptability. Examples include claims evaluation, research summarization, and complex multi-step processes that would be difficult to program with traditional RPA.
Good candidates for AI Agent automation typically involve interpretation of unstructured data, variable processes that can't be fully predefined, or tasks requiring judgment calls based on context.
- Claims processing and evaluation
- Research and information summarization
- Dynamic customer service routing
- Processes requiring interpretation of documents
Yes, AI Agents can integrate with existing TaskBots, MetaBots, and API tasks as part of their toolset. This allows organizations to leverage their current automation investments while adding AI capabilities.
Existing automations become tools that the AI Agent can call upon when needed, creating a powerful combination of traditional RPA reliability with AI flexibility.
- Existing TaskBots can be used as tools
- MetaBots provide reusable components
- API tasks extend functionality
Key components include the agent's role definition, goals, action plans, input/output variables, model connections to LLMs, and access to tools like other agents, processes, forms, and API tasks. Human-in-the-loop options are also available.
These components work together to create an intelligent automation that can understand its purpose, plan how to achieve its goals, access the tools it needs, and deliver results - all while being configurable through a no-code interface.
- Role, goals, and action plans guide behavior
- Model connections provide reasoning capability
- Tools extend functionality
No, AI Agents are created through a no-code interface similar to building TaskBots. You define the agent's role, goals, and tools through configuration rather than programming.
The visual interface makes AI Agent creation accessible to business users and automation developers alike, with options to generate initial configurations from natural language prompts.
- Completely no-code configuration
- Natural language prompt generation available
- Visual interface similar to TaskBot creation
As of October 2025, AI Agents are in preview mode within Automation Anywhere's Control Room. The interface and features may change before general availability, which hasn't been officially announced yet.
Organizations interested in exploring this technology should work with their Automation Anywhere representatives to gain access to preview environments where they can experiment with AI Agents.
- Currently in preview as of October 2025
- No official GA date announced
- Features and interface may change
GrowwStacks specializes in implementing AI-powered automation solutions including Automation Anywhere AI Agents. Our team can help identify use cases, design agent architectures, and implement solutions that combine traditional RPA with AI capabilities.
We offer free consultations to evaluate your automation needs and develop a roadmap for incorporating AI Agents into your automation strategy as the technology becomes generally available.
- Use case identification and prioritization
- Agent architecture design
- Implementation and optimization
- Free initial consultation
Ready to Explore AI-Powered Automation for Your Business?
While AI Agents aren't yet generally available, now is the perfect time to plan your strategy. Our automation experts can help you identify the best use cases and prepare for implementation.