Process Automation

Hi Everyone, I am facing an issue while creating an Extraction Model using AI Document Center. During the Extraction Model creation wizard (Steps 1–6), the final step displays the list of objects that will be created ( 4 AI Skills and 2 Process Models). After clicking “Confirm”, I receive a success message indicating that the Extraction Model has been created successfully. However, the underlying objects that are supposed to be generated are not actually created. I have verified this across the entire environment and cannot find the AI Skills or Process Models that were listed in the final step. Because these objects are missing, when I try to run the extraction from the UI, the process fails with an error indicating that the required Process Model cannot be found. A few additional observations from my debugging: This issue started occurring only recently. The same setup was working fine earlier. No application objects or configurations have been modified. The E

My use case is that I want to be classify documents into one of several categories, then depending on the category, extract certain fields from it. My question is for this kind of use case, what exactly is the difference between using AI Skills to do this versus using AI Doc Center? Looking around, I see some people saying AI Doc Center uses more tokens and that it can handle more complex cases, but I can't see anything in documentation about if that's actually true or not, and anything about when you'd want to use one or the other. I've been in loops reading the same page over and over again, so at this point I might have missed something, but if someone's able to help point me towards an answer, that would be most appreciated!

When I test AI Agent it is working as expected and giving result. But when I incorporated it within Process model, it is giving error: Agent 250512b0-a322-45b6-85c5-525b87755a8c could not be found. So, I created another AI agent and getting same error stating Agent could not found. I have double checked the UUID of the AI Agent it is correct. What could be the reason? In Admin console for our environment AI services are enabled and active.

Hi Team, Our chat agent is intermittently failing when attempting to execute tool calls. End users are receiving one of the following error messages during these interactions: Tool execution failed: Downstream tool request failed I'm currently experiencing a system issue retrieving the data.

How accurate AI Agent works same task with different data. Will it hallucinate or imagine things and provide output? Does it reconcile based on the repeated tasks or does it take every new data as new task?

Hello everyone, I’m configuring the Appian MCP Server in Appian 26.6 and I noticed a difference between the endpoint URL shown in the Administration Console and the one described in the documentation. In the Administration Console > MCP Server, the endpoint URL is displayed as: <ENVIRONMENT_URL>/mcp See Image 1. However, the documentation for enabling the Appian MCP Server says that the endpoint URL follows this format: <ENVIRONMENT_URL>/suite/mcpdocs.appian.com/.../enable-configure-mcp-server.html See Image 2. My question is: Which endpoint should be used when configuring an external MCP client in Appian 26.6? Should it be: <ENVIRONMENT_URL>/mcp or: <ENVIRONMENT_URL>/suite/mcp Has anyone configured this successfully and can confirm which URL is correct? Thanks in advance. Attachments Image 1: Administration Console showing the MCP endpoint as /mcp. Image 2: Documentation showing the MCP endpoint format as /suite/mcp. 

Hi Team, We are evaluating AI Skills/Generative AI usage in Appian and would like to monitor token consumption for optimization and cost governance purposes. I have a few questions: Is there a way to view the input tokens, output tokens, and total tokens consumed by an AI Skill execution? Does Appian expose token usage metrics at the AI Skill or process level? What is the recommended approach to track and analyze token usage for prompt optimization? Has anyone built a custom monitoring/dashboard solution to estimate token consumption for AI Skills and Agents? thanks in advance.

I have configured a Chat Agent named "Roster Assistant" using a!agentChatField with the following setup: The agent is connected to our Roster Record Type as a tool. Field-level instructions have been configured within the record type. Agent goals and instructions are designed to: Respond to greetings such as "Hi" or "Hello". Retrieve and return roster information for users. Problem The agent does not respond as expected. Regardless of the message sent—including simple greetings like "Hi"—the chat either takes an unusually long time to process the request or eventually returns an error. Additionally, when reviewing the execution in the Monitor tab, the agent run remains in a "Processing" state for an extended period before ultimately failing with an error. As a result, the agent is unable to provide responses for even basic interactions, making it difficult to determine whether the issue is related to the agent config

Hello, I have built a chatbot in appian. I have two objects in my interface. First is a text box, which takes user question. And, the second is a button user clicks to send question to AI bot and get response. Currently, when text box loses focus, a local variable called isProcessing is turned to true(So the user can be shown that processing is occurring). And, when the button-press returns with AI response, the isProcessing is turned to false, and the AI response is shown to user. I want to have it so that when the text box loses focus, the local variable isProcessing is turned to true, as well as data being sent to AI bot. And, then when AI bot returns, the isProcessing is turned to false, and result shown. Is this possible? Thanks for your time.

I'm currently working on a case management project in Appian and exploring the potential of the Data Fabric Chatbot as a conversational interface for employees to manage their work. While the chatbot works great for querying and retrieving data. But today, I'm wondering if Appian has any plans to evolve it into a fully actionable interface in future releases. User: "Give me all my open cases" Chatbot: Returns a list of open cases assigned to the user in the table. User: "Open the high priority case" Chatbot: Opens the case in a side panel or new screen User: Uploads a document, fills in the required details, and submits Chatbot: Confirms the case is completed This would basically make the Data Fabric Chatbot a one-stop shop for employees — no more jumping between screens just to find and complete a case. Everything handled right there in the chat.

Hello All, I am using Version 26.3 and while additing new AI Skill, i am getting blank screen everytime. I cleared cache, restarted browser and system but with no luck. Even tried with other bowsers also. Within my organization, the same is working fine for few but there are few who are facing issue similar to what i have been facing. Please suggest. Br, Kunal

Hi All, I have a use case where we have to search for values in numerous constants if it matches a specific email domain. Just applying the search box value isnt productive as it doesnt provide the results with actual constant values, also gives descriptions which is not useful in my case. 

We are trying to automatically delete the execution instances of a Robotic Task, regardless of whether the executions were completed, cancelled, or failed. We have configured the Automatic Process Clean-Up setting by enabling the option "Delete completed or cancelled executions including artifacts (days)" and setting it to 1 day. However, the execution instances of the Robotic Task are still not being deleted automatically after this period. Is there another configuration or step required to ensure that these execution instances are removed automatically? Thank you in advance for your help.

1. The extracted document contains a table with over 300 rows, but the reconciliation screen displays only 100 fields because the related record type query is limited to retrieving 100 items. Do you have any suggestions on how we can reconcile all the rows? 2. The "Highlight Value in Document" feature on the reconciliation screen is not working properly. I’m using AI Document Center version 3.1. Is there any known reason why this issue occurs? 3. Sometimes, when a document has a large number of rows, the extraction model does not extract all the data. For example, if the document has 600 rows, it only extracts about 350 rows. Are there any limitations on the document extraction model? 4.The extraction process is taking a considerable amount of time. Are there any best practices or configuration changes to improve extraction performance? Environment Details: AI Document Center Version: 3.1 LLM: 4.5 (Reasoning)

Hi Everyone, I'm using Appian Document Extraction and need to process multiple invoice documents uploaded in a claim. What is the recommended approach to: Pass multiple invoices for extraction Extract data from each invoice Store the results in a List of Record Type/CDT Can a list of document IDs be passed directly to Document Extraction, or should each document be processed individually (using a loop/subprocess)?

Hi, I am working on DocCenter Classification and have a few questions around reconciliation and automation. Looking for guidance from anyone who has implemented this. **My Use Case:** I want to classify documents automatically without any human involvement in the reconciliation step. **My Questions:** 1. Is it possible to auto-reconcile a classification instance in DocCenter without human intervention? 2. Does DocCenter Classification expose a confidence score as an output in a process model (similar to the Classify Documents Smart Service)? 3. If DocCenter Classification does not support auto-reconciliation natively, what is the recommended approach to achieve straight-through processing (STP) for classification? Thanks in advance. 

Hi everyone, I’m facing an issue while using Appian AI Skill (GenAI) with an Excel file. When I upload an Excel file with empty data, I get one type of classification output. However, when I upload the same file structure but with data inside, it produces a different classification output. Essentially, the classification result varies depending on whether the file has data, and it seems inconsistent each time. Has anyone experienced this issue before? Any insights on why the classification behavior changes like this?

I am working with a rule in Appian where I expect a consistent structure of List of Map, but I am seeing inconsistent data types in the output.a!localVariables( local!indexval: index( ri!userFilters, "userFilter", null ), local!filters: split(local!indexval, ";"), a!forEach( items: local!filters, expression: a!localVariables( local!valueStart: find("value=[", fv!item), local!valueBlock: if( isnull(local!valueStart), "", mid( fv!item, local!valueStart + 7, len(fv!item) ) ), local!cleanValueBlock: substitute(local!valueBlock, "]", ""), /*Values */ local!AccountName: if( find("name=AccountName", fv!item) > 0, a!forEach( items: split(local!cleanValueBlock, ","), expression: trim(substitute(fv!item, """", "")) ), {} ), local!ProductCategory: if( find("name=Product Category", fv!item) > 0, a!forEach( items: split(local!cleanValueBlock, ","), expression: trim

I am exploring the Appian AI  agend and faced the below Appian error. enable to understand and fix these issue.If anyone has faced similar issue please let know the fixes.

I am facing error in the AI agend while reading the document.LLM execution failed: EVENT_NOT_FOUND - No event exists with name: setVariableValueSteps1:  When I am passing this document as in the TOOL in the AI agend than it is working fine.Steps2:  When passing this document in the input than facing this issue.Please just How can I pass document dynamically in the process model?

Hi Everyone,We have a requirement where we are using AI-based document extraction to extract fields from a predefined form.In the same form, if there are any empty spaces and the user accidentally adds handwritten text or notes outside the expected form fields, we need to detect that additional handwritten content separately.Is it possible in Appian with help of AI extraction to achieve this use case ? Kind Regards, Kavya

I have a Record Type with a Document record field. We have a business requirement to be able to use Smart/Semantic search on this and a text field. The Smart Search Sync consistently fails with the following error message, and a list of failed record ids: "Cannot retrieve the document. Verify the document management record type is configured correctly." Through investigation, we discovered, that the Document field for these records was NULL, which is consistent with the above error message. We would like to have a successful search indexing, for this record type, and so we removed those records from the dataset temporarily.  However, now we get the following error message: "We couldn't check the smart search status. Try resyncing your record type." Attempting to Sync is what produced this error, so it seems a bit unhelpful as an error message.   Why is the indexing failing when the selected smart search field is empty? Is there a workaround? Now that t

Has anyone configured MFA for Appian RPA bots? How is the manual action to accept or decline the request on authenticator bypassed ?

I have many robotic tasks, all of them runs when triggered either from process or by Re-execution or By TEST button, but one of the Robotic tasks which was executing previously has stopped triggering from process model, the process model gets stuck at the robotic tasks node without any errors and fails with timeout after long time. Re-execution of the previous instances is also not triggering any new instance for this robotic tasks alone. 

Sometimes, when I test an agent, it processes for a while, then after a few minutes throws this error: System Error This appears to be a system issue. Try again later or submit a support case if the problem continues. Source: Run Agent - Agent: DG_testAgent - Run ID: 52 Details: LLM execution failed: INVALID_TOOL_RESPONSE - Required schema field not provided in data tree: textValue Any suggestions on what could be going on? Sometimes the agent succeeds, but the frequency of getting this error makes me concerned about using this in production. 26.3 environment hosted on Appian Cloud.