The fastest way to build AI agents

Create a customer support chatbot that classifies each user message into one of four categories: Billing & Payments, Product Help, Account Management, or Escalation. For the first three, reply using information from my support knowledge database. For Escalation use an Agent Node to send a Slack message in #triage-user-messages saying “user has requested [summary of issue]” and tell the user that a human will follow up soon.

Create an agent that reviews all deals marked as “Closed Lost” in HubSpot for the week.
• Extract key details: deal size, loss reason, competitor, and stage lost.
• Identify recurring themes or reasons (e.g., pricing, missing feature).
• Summarize findings in a Slack or Notion update for the Sales and Product teams every Friday.

Create an agent that gives a weekly update on active deals in Hubspot, summary consisting: of size of deal, last communication, % likelihood, next activity date etc.Ranks Green, Yellow, Red dealsWraps up any past close date deals (Red). Prompts user to either move close date or change to close lost with reason.

Create an agent that takes in call transcript and customer email address as text, extracts objections, and updates the associated HubSpot contact record.If a field for objections doesn't exist then create one.

Create an agent that takes a webinar transcript as text input. It should extract the main themes, do light web research for supporting stats, and generate 3 LinkedIn posts, 1 blog outline, and a Slack summary draft.

Create an agent to analyze the earnings calls of software companies and quickly turn those into content that I can share with my network that adds valuable insights.The input would be the transcript of the earnings call.
The agent should find the most interesting highlights from the earnings call, whether quantitative or qualitative, summarize, and output a 4 or 5 slide brief to automatically create a presentation.

Create an agent that helps with creative writing. I will provide a web url & the desired output. Output could be a blog, poem, Slack message. Once the agent has the web url it should extract the main themes from the url. Then use the main themes to do web research with LLM's native capabilities. Use web research and the document to come up with the output

Create an agent that checks data processing agreements (DPAs) or privacy policies for compliance with GDPR, CCPA, and other frameworks.
• Parse documents and extract references to key obligations (data retention, subprocessor lists, breach notification).
• Compare coverage against a compliance checklist.
• Output a compliance score, missing elements, and action recommendations.

Create an agent that reviews NDAs (Mutual or One-Way) and highlights deviations from standard terms.
• Extract clauses around confidentiality period, exclusions, governing law, and IP ownership.
• Compare each clause to a company-approved template.
• Generate a summary of differences and a risk assessment (Low / Medium / High).
• Output a short summary in Slack and a detailed Markdown review.

Create an agent that reviews legal contracts against a checklist and generates risk assessments with lawyer-friendly summaries.
‍
• Inputs:    
- Contract PDFs    
- Review checklist document    
- Risk profile (Low / Medium / High)

• Workflow:    

1. ParseDocs: Extract sections from contracts and checklist items into structured JSON.    
2. ClauseCheck (GPT-4.1): Compare contract clauses to checklist items, flag missing or concerning language, and summarize findings.    
3. RiskAssessment (GPT-4.1): Score each flagged issue based on the provided risk profile, categorize risks (legal, financial, compliance), and propose mitigations.    
4. Summary (GPT-4.1): Generate a professional legal memorandum including:        
- Executive summary and risk overview      
 - Clause-level redline recommendations (CHANGE FROM / TO format)        
- Negotiation strategy and approval guidance

• Outputs:    

- flagged_issues: structured JSON risk assessment    
- review_summary: Markdown-formatted legal summary

Create an agent that generates personalized care plans from EHR data and clinical guidelines.
• Parse diagnosis codes, medications, and lab results.
• Recommend care steps, goals, and follow-up intervals.
• Format in clinician-friendly Markdown for EHR entry.

Create an agent that reviews claim submissions for policy compliance and error detection.
• Cross-check diagnosis, procedure, and modifier codes against payer rules.
• Identify duplicates, invalid codes, or unbundling issues.
• Recommend adjustments or rejections with explanations.

Create a prior authorization review agent that:
‍• Accepts 3 documents (clinical notes, codes, medical necessity forms) and plan criteria
• Extracts text from documents (GPT-5 requires text, not files)
• Uses agent tools to parse notes, extract codes, check coverage, and identify missing docs
• Outputs structured JSON summary with coverage determination, missing items, and recommendations

Create an agent that compiles a weekly summary of each client’s investment portfolio.
• Pull holdings, performance, and benchmark data from a PDF file upload that I will provide as input
• Highlight top-performing assets, risk exposure, and allocation drift.
• Generate personalized Markdown summaries for advisors to share with clients.

Create an agent that automates “Know Your Customer” (KYC) checks.
• Take customer-uploaded documents as inputs (ID, proof of address, corporate certificates).
• Verify document validity, completeness, and expiry.
• Flag missing or inconsistent information and recommend follow-up actions.
• Output a compliance summary

Create an agent that analyzes transaction patterns to identify potential fraud.
• Pull recent transactions from a PDF file upload that I’m going to provide as input
• Build Agent node with tools that will detect anomalies using rule-based and LLM pattern recognition (e.g., velocity, unusual merchant, location mismatch).
• Summarize flagged cases in Slack #triage-user-messages with supporting evidence.
• Generate JSON output for fraud operations dashboards.

Create an agent that detects when a support ticket is escalated (e.g., repeated follow-ups or high urgency).
‍
- Summarizes the full context and previous correspondence.
- Assigns it to the correct engineer in Linear and posts a summary to the #internal-support-triage channel.

Create an agent that scans HubSpot for deals with upcoming renewal dates in the next 60 days. Rank customers by renewal risk (High, Medium, Low) based on communication recency, usage metrics, and deal notes. Send a weekly renewal risk summary to the account owner with action recommendations (e.g., “Schedule QBR,” “Share new feature update”). Send this summary to a slack channel ID that the user provides as input