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10 AI Agent use cases to replicate today

  • July 3, 2026
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vinicius.pereira
Community Manager

đŸ‘€  For all users

🔐  Available for all plans with AI Credits

🎯  For anyone who wants to activate their first AI Agent or expand into new processes

 

The biggest obstacle to activating an AI Agent isn't technical. It's the difficulty of imagining where AI fits into a process you already have. The question "what do I automate with AI?" paralyzes more than any configuration step.

For each case below, you'll find the problem it solves, the configuration logic in Pipefy, and the expected result. Identify which one resembles a process you already run and use it as your starting point.

 

How to choose where to start:

  • Volume and repetition: which decision is made most often per week in your process?
  • Definable rule: can the decision criteria be written down as text? If so, it can become an instruction.
  • Clear output field: is there a field on the card where the analysis result should be recorded?
  • If the answer to all three is yes, you have a use case ready to activate today.


📖  What you will learn here:

 

 

Build and validate data

The agent reads fields or documents on the card and automatically fills in fields with extracted data or with judgments based on criteria you define.

 

Case 1: Ticket triage and categorization

Every ticket that arrives at the service desk requires someone to read the description, decide the urgency level, and route it to the right queue. At high volume, this consumes senior analysts' time on operational work.

The agent reads the ticket description as soon as the card is created and fills in the urgency, category, and support queue fields based on the criteria you define in the instruction.

 

Configuration in Pipefy

Trigger: when a card is created in the pipe.

Input fields: @Problem description, @Entry channel.

Instruction (summary): "Read @Problem description. Classify @Urgency as High if there is loss of access, a production failure, or impact on more than 5 users. Classify as Medium for slowness or partial limitation. Classify as Low for all other cases. Fill @Category with the type of problem (Access, Hardware, Software, Network, Other). Do not move the card."

Output fields: @Urgency, @Category.

 

Include examples in the instruction: "Examples of High: user can't log into the ERP system, server is down. Examples of Low: question about a feature, request for new software." Concrete examples increase classification accuracy.

 

Case 2: Resume analysis against job description

In high-volume recruiting, resume screening is the bottleneck. Each CV requires reading, comparing against requirements, and a binary decision. The work is repetitive and always follows the same logic.

The agent reads the PDF resume attached to the card and compares it against the job requirements stored in the knowledge base. It fills in the result and the reason without the recruiter needing to open the file.

 

Configuration in Pipefy

Trigger: when a card enters the "Screening" phase.

Input fields: @Resume (PDF attachment field).

Knowledge base: PDF document with the job description and the required and desired requirements.

Instruction (summary): "Read the file in @Resume. Compare it against the job requirements in your knowledge base. If the profile meets 70% or more of the required requirements, fill @Screening result with Approved. If not, fill it with Rejected and @Reason with an objective sentence explaining what was missing. If the resume can't be read, fill it with Inconclusive."

Output fields: @Screening result, @Reason.

 

Update the job description document in the agent's knowledge base every time the requirements change. The agent uses what's in the document, not what changed in your head.

 

Case 3: Reimbursement receipt validation

In reimbursement processes, an analyst opens each receipt, checks the amount and date, and decides whether to approve or reject it. At high volume, this consumes hours per week on checks that follow exactly the same rules.

The agent extracts the data from the receipt. A complementary automation performs the comparison against the declared amounts. Separating extraction from comparison produces more reliable results.

 

Configuration in Pipefy

Trigger: when the @Proof of payment field is updated.

Input fields: @Proof of payment (attachment field).

Instruction (summary): "Read the file in @Proof of payment. Extract the total amount and fill @Extracted amount. Extract the transaction date and fill @Receipt date. If the file can't be read, fill @Extraction status with Inconclusive. Do not compare amounts. Do not move the card."

Output fields: @Extracted amount, @Receipt date, @Extraction status.

Complementary automation: if @Extracted amount differs from @Requested amount (tolerance $0.10), move to the "Review" phase and notify the analyst.

 

Don't ask the agent to extract and compare in the same instruction. Numerical comparisons by the agent have variable precision. Extraction + automation with a formula is more reliable and easier to audit.

 

Case 4: Invoice data extraction

Manual entry of invoices in accounts payable processes generates rework and typing errors. The data is in the document. Someone has to copy it into the system.

The agent reads the PDF invoice and automatically fills in the card fields. The analyst only steps in when there's an inconsistency.

 

Configuration in Pipefy

Trigger: when a card enters the "Entry" phase.

Input fields: @Invoice (PDF attachment field).

Instruction (summary): "Read the file in @Invoice. Extract the issuer's Tax ID and fill @Supplier Tax ID. Extract the service description and fill @Description. Extract the total amount and fill @Invoice amount. Extract the issue date and fill @Invoice date. If any field can't be extracted, fill it with Not identified."

Output fields: @Supplier Tax ID, @Description, @Invoice amount, @Invoice date.

Recommended skill: access to the approved suppliers database to cross-check the extracted Tax ID.

 

Automate communications

The agent reads the card's context and generates personalized communications, eliminating the need to draft repetitive messages.

 

Case 5: Automatic response to email requests

When requests arrive by email, the first step is always the same: read it, understand the type of request, and draft an acknowledgment or clarification response. It's the most repetitive part of support work.

The agent reads the email received on the card, identifies the type of request, and generates a draft contextualized response. The analyst reviews and approves before sending.

 

Configuration in Pipefy

Trigger: when a card is created (a form receives an email or an integration creates a card).

Input fields: @Email received (long text or attachment field).

Instruction (summary): "Read the content in @Email received. Identify the type of request and fill @Request type. Draft a first-contact response in a formal tone, confirming receipt and stating a 2-business-day turnaround. Fill @Response draft with the generated text. Do not send the email."

Output fields: @Request type, @Response draft.

Complementary automation: when @Approved for sending is marked as Yes, send an email with the content of @Response draft.

 

Keep a human in the loop during the first month. Once the response pattern is validated, you can remove the approval step for low-risk request types.

 

Case 6: Personalized collection based on delinquency context

Collection messages sent in the wrong tone or without context have low effectiveness. A first notice has a different tone than a third attempt. Manually drafting each message in the right tone takes time and depends on the analyst's availability.

 

Configuration in Pipefy

Trigger: when a card enters the "Collection" phase.

Input fields: @Customer name, @Amount due, @Due date, @Number of previous contacts.

Instruction (summary): "Read the card's fields. If @Number of previous contacts is 0, draft a cordial first notice stating the amount and due date. If it's 1, draft a second notice with a more direct tone and a request for payment confirmation. If it's 2 or more, draft an escalation notice stating that the case will be forwarded to legal. Fill @Collection text with the result."

Output fields: @Collection text.

 

Operate with more visibility

The agent enriches the card with context that facilitates human decision-making or automatically triggers the next step in the process.

 

Case 7: Risk classification in approvals

In approval processes with multiple risk criteria (amount, supplier, deadline, history), the approver needs to read all fields before deciding. At high volume, this becomes a bottleneck.

The agent reads the card's fields, applies the risk criteria, and fills in a risk level field with justification. The approver arrives at the card with the context already consolidated.

 

Configuration in Pipefy

Trigger: when a card enters the "Approval" phase.

 

Input fields: @Requested amount, @Supplier, @Delivery deadline, @History of occurrences.

 

Instruction (summary): "Analyze the card's fields. Classify @Risk level as High if the amount exceeds $50,000, the supplier isn't registered, or the deadline is less than 5 business days. Classify as Medium if two of these criteria are present. Classify as Low for all other cases. Fill @Risk justification with a sentence explaining the main risk factor identified."

 

Output fields: @Risk level, @Risk justification.

 

Complementary automation: if @Risk level is High, notify the manager and move to the "Management approval" phase.

 

 

Case 8: SLA escalation with context

SLA alerts without context generate noise. The manager receives the notification, opens the card, and still has to understand what's happening before acting. A useful escalation arrives with the diagnosis already in place.

 

Configuration in Pipefy

Trigger: 1 day before the @Due date field expires.

Input fields: @Current phase, @Assignee, @Activity history, @Request type.

Instruction (summary): "Analyze the card's current state. Identify the current phase, who the assignee is, and whether there has been activity in the last 24 hours. Fill @SLA diagnosis with a summary: current phase, assignee, last recorded activity, and the main risk to meeting the deadline."

Output fields: @SLA diagnosis.

Complementary automation: send a notification to the manager with the content of @SLA diagnosis.

 

The agent doesn't guarantee SLA compliance. It guarantees that the manager arrives at the escalation with enough context to act quickly.

 

Measure and summarize

The agent consolidates information distributed across fields or documents and produces structured summaries, eliminating manual reading and synthesis.

 

Case 9: Support history summarization

In customer support, any team member may need to take over an ongoing case. Reading the entire interaction history before responding takes time and increases the risk of inconsistency.

 

Configuration in Pipefy

Trigger: when the @Support assignee field is updated (assignee change).

Input fields: @Interaction history, @Request type, @Current status.

Instruction (summary): "Read the history in @Interaction history. Generate a summary with three parts: (1) current situation in one sentence, (2) main points raised by the customer in the most recent contacts, (3) recommended next step based on the current status. Fill @Case summary with the result."

Output fields: @Case summary.

 

Case 10: Automatic database update from cards

When a process is completed, the card's data needs to be recorded in a centralized database for future reference. Doing this manually creates delays and inconsistency in the repository.

 

Configuration in Pipefy

Trigger: when a card enters the "Completed" phase.

Input fields: key fields on the card that need to be recorded in the database.

Instruction (summary): "Upon completing this card, create a record in the @Service history database with the following fields: @Customer, @Request type, @Opening date, @Completion date, @Assignee, @Final result. Fill @Record created with Yes after creating the record."

Output fields: record created in the database + @Record created (control field on the card).

 

The agent can create up to 80 fields when creating a record. Use the @Record created field as a control to avoid duplicates in case the automation is triggered more than once on the same card.

 

Where to start

You don't need to implement all 10. You need to implement one, let it stabilize, and expand from there.

  • Choose the case with the highest volume and the clearest rule in your process.
  • Map the input and output fields before writing the instruction.
  • Test with 5 real cards before activating at volume. Each test consumes between 2 and 3 AI credits.
  • Monitor the results in the card's Activities tab and in the Automation Logs during the first few weeks.
  • Refine the instruction based on errors. Instructions improve with iteration.

 

Testing cost:

  • Each agent execution consumes 2 AI credits.
  • Executions that read a document (PDF or image) consume 3 credits.
  • Testing one case with 5 cards costs between 10 and 15 credits.
  • Each plan includes 1,000 one-time use credits. The testing cost is low.

 

Before proceeding, confirm that you understand:

☐ The three criteria for identifying a good AI Agent use case

☐ The configuration structure for each case: trigger, input fields, instruction, output fields

☐ Why it is important to separate extraction (agent) from numerical comparison (formula-based automation)

☐ How the control field prevents duplicates in the database

☐ How to test before activating at scale

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