About AI Agents

This page explains what AI agents are, how they work, and when you should use them in your applications. It also provides a guide to help you decide between using an AI agent and an AI skill.

For practical examples of AI agents in action, see Use Cases. To get started building your first agent, see Create and Configure an AI Agent.

What is an AI agent?

An AI agent is a smart, configurable object that can:

• Understand your goals through a natural language prompt.
• Interact with your data and systems using tools.
• Perform tasks like routing cases, updating records, or starting workflows.

Each AI agent follows your instructions to complete tasks consistently.

How AI agents work

AI agents operate using these key elements:

• Prompt: A written description of the tasks the AI agent should perform. This helps the AI agent understand the goal and how to approach it. When you create a new AI agent, you enter your prompt in the Instructions field.
• Tools: Design objects that perform a single specific task. They can let the AI agent access data, trigger processes, call other AI agents, or retrieve information. Tools ensure AI agents act within structured, secure boundaries. For details on available tools, see AI Agent Screen Reference. For document tool guidance, see Working with Documents in AI Agents.
• Inputs: Data that the AI agent receives to complete a task. Inputs define what information the AI agent needs at the start of its run. For AI agents that use the Process tool, you must configure at least one input variable.
• Outputs: Data that the AI agent returns after completing a task. Outputs are optional—some agents don't produce an explicit result. Instead, they may create side effects during their run, such as writing to a record type or updating data in a process.

You define the goal and set up tools specific to each AI agent, giving you full control over how each AI agent behaves. To run an AI agent, you must use a process model with the Execute AI Agent smart service. When the AI agent runs, it interprets your prompt, selects the appropriate tools, and executes each step automatically.

You can manually stop a running AI agent at any time. This is useful when testing or when an AI agent is heading off-track. Appian also automatically stops agents that exceed resource limits to protect system performance. For details, see Stop a running AI agent and Run limitations.

Unattended execution model

AI agents are designed to run autonomously without human intervention, completing multi-step workflows from start to finish. This unattended execution model enables true straight-through processing for complex tasks that previously required manual decision-making.

When you design an unattended AI agent:

• Plan for autonomous operation: Structure your AI agent to complete its entire workflow without requiring human input during execution.
• Build in exception handling: Use your prompt to define when the AI agent should escalate to a human rather than proceeding. For example, "If the confidence score is below 70%, flag for manual review instead of proceeding."
• Design for side effects: Many AI agents don't return explicit outputs. Instead, they create side effects during execution such as updating records, sending notifications, or starting downstream processes.
• Enable human oversight: While AI agents run unattended, you can monitor their decisions and intervene when needed through the Monitor tab or by building review workflows.

This unattended model is what differentiates AI agents from AI skills, which are typically called within a process where you control the flow. AI agents control their own flow based on your instructions and the tools you provide.

For step-by-step guidance on creating AI agents, see Create and Configure an AI Agent. For integration details, see Use AI Agents in a Process.

When to use an AI agent

AI agents are best suited for tasks that require flexible, goal-driven automation using dynamic reasoning. Unlike deterministic logic (where the same inputs always produce the same outputs), AI agents interpret context, adapt to variations, and make decisions that can't be fully scripted in advance. They use a prompt and tools to reason through various challenges, freeing you from having to model every possible scenario up front.

Typical use cases:

• Involve multi-step goals where the path to completion may vary, like case triage and resolution—automatically reviewing case data, determining priority, assigning it to the right group, and escalating if needed.
• Are too complex to define with static logic or rules, such as interpreting unstructured emails or documents and deciding what action to take next.
• Can be partially or fully delegated to AI, with optional human oversight—like automating initial decisions and updates while flagging exceptions for review.
• Require dynamic routing, where the next step depends on real-time context. For example, assigning tasks based on workload, urgency, or case attributes without predefined routing conditions.
• Require pulling together information from different sources to make a decision. For example, an AI agent can check your CRM for customer history, pull recent support tickets, review product usage data, and then decide whether to escalate an account—all in one workflow. Traditional process models would require complex branching logic to coordinate these lookups and interpret the results.
• Require collaboration with other AI agents, where specialized AI agents work together to complete complex workflows. For example, a main AI agent coordinates with separate AI agents for triage, data extraction, and approval routing.

For detailed examples and implementation guidance, see Use Cases. To learn how to build a complete triage solution, see Tutorial: Build a Triage AI Agent.

When not to use an AI agent

AI agents are powerful, but they're not the right solution for every automation challenge. Understanding when to use traditional process models or rules-based automation helps you build more reliable, cost-effective solutions.

Avoid using AI agents when:

• The process path is fully known and unchanging. If you can map every step and decision point in advance, a traditional process model will be faster, more reliable, and less expensive to run.
• High-volume, simple tasks need maximum efficiency. For repetitive tasks that don't require reasoning (like data validation or simple routing), rules-based automation offers better performance and lower cost.
• Real-time performance is critical. AI agents involve LLM calls and tool execution, which introduce latency. For processes requiring sub-second response times, use traditional automation.

Understanding cost and reliability trade-offs

When choosing between AI agents, AI skills, and rules-based automation, consider both the execution characteristics and business requirements of your use case.

Reliability considerations

• Rules-based automation: Produces deterministic, 100% predictable results. The same inputs always generate the same outputs.
• AI skills: Highly reliable for their specific task, but outputs may vary slightly based on the LLM's interpretation.
• AI agents: Reliability depends on prompt quality, tool configuration, and the complexity of the reasoning required. More complex AI agents may need iterative refinement to achieve consistent results.

Cost considerations

AI agents consume resources differently than traditional automation:

• AI actions usage: Each LLM call consumes AI actions based on prompt length, tool descriptions, and response generation.
• Tool execution: Every tool call (querying records, starting processes, reading documents) adds processing time and resource consumption.
• Multi-step reasoning: Complex AI agents that require multiple reasoning cycles will consume more resources than simple, linear processes.

Development and maintenance

• Rules-based automation: Requires skilled developers to build and maintain, but produces highly reliable results.
• AI skills: Moderate development effort focused on prompt engineering and testing.
• AI agents: Lower barrier to entry for basic AI agents, but complex AI agents require careful prompt design, tool configuration, and ongoing monitoring to maintain performance.

Choosing the right automation approach

Appian offers multiple automation capabilities, each suited to different types of tasks. Use this framework to determine the best approach for your use case.

Decision framework

Approach	Best for	Reliability	Cost	Example use cases
Rules-based automation	Deterministic, high-volume tasks where accuracy is paramount	Very high (100% predictable)	Very low execution cost	Payroll calculations, financial transactions, compliance checks, data validation
AI skill	Single, well-defined cognitive tasks that benefit from AI	High for specific task	Low to medium	Document summarization, sentiment analysis, text classification, entity extraction
AI agent	Complex, multi-step processes requiring reasoning and dynamic decision-making	Medium to high (probabilistic)	Medium (tokens + tool calls)	Case triage, lead qualification, multi-system data synthesis, exception handling

When to use rules-based automation

Choose traditional process models and rules when:

• You need 100% deterministic results.
• The process logic is fully defined and stable.
• High-volume execution requires maximum efficiency.
• Strict audit trails are required.
• Real-time performance is critical.

When to use an AI skill

Choose AI skills when:

• You need AI to perform a single, repeatable task.
• The task involves text analysis, generation, or transformation.
• You control the process flow and specify exactly when AI is used.
• You want to add AI capabilities to an existing process.

When to use an AI agent

Choose AI agents when:

• The process requires reasoning across multiple steps.
• The path to completion varies based on context.
• You need to synthesize information from multiple sources.
• The logic is too complex to define with static rules.
• Dynamic decision-making is required.

Hybrid approach

The most effective automation strategies combine multiple approaches:

• Use AI agents for reasoning and decision-making.
• Use AI skills for specific cognitive tasks within a process.
• Use rules-based automation for deterministic execution and high-volume processing.
• Design for human oversight on exceptions and edge cases.

Deciding when to use an AI skill or an AI agent

This comparison helps you choose between AI skills and AI agents for specific scenarios.

Option	When to use it
AI skill	Choose this option when you need AI to perform a single, well-defined task within a structured process. You control the flow, define each step, and specify exactly when AI should be used—for example, to extract data, classify text, or summarize a document. AI skills support models in customer provided AWS accounts.
AI agent	Choose this option when you need AI to handle a complex, multi-step task where the process can't be fully defined up front. AI agents use your prompt and available tools to determine how to reach a goal, making them ideal for tasks that involve reasoning, dynamic decision-making, contextual awareness, or collaboration with other AI agents.

The following diagram shows how to decide whether to use an AI skill or an AI agent for your use case.

Key terms

Term	Description
AI agent	A configurable design object that performs tasks using a prompt and tools you define.
Prompt	An instruction that tells the AI agent what task to perform and how to approach it. When you create a new AI agent, enter your prompt in the Instructions field.
Tool	A design object that allows the AI agent to interact with people, data, and systems or provides context for the task.
AI Agent tool	A tool that allows one AI agent to call another, enabling multi-agent collaboration.
Input	Data the AI agent receives to complete a task. You configure inputs when you set up the AI agent.
Output	Data the AI agent returns after completing a task. You configure outputs when you set up the AI agent.

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