The fastest way to build AI agents

Create an agent that scans the CRM for “stale” leads (no activity for >30 days) with positive historical engagement (e.g., opened emails, previous demos).
• Generate a short personalized outreach draft referencing the last conversation or interest area.
• Suggest next best action (e.g., send new case study, invite to webinar).
• Auto-draft message in Gmail for review before sending.

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:
• as competitors come up in calls or prompted by user, it researches the companies docs and then updates our Notion database
• Keeps Notion database updated on a daily or weekly cadence with new calls from Gong

Create an agent that has a document for standard Order Form templates, either scrapes latest call or has input for needed fields and auto-populates required fields or negotiated terms into order forms. Use Gong and Hubspot as tools

Create an agent that allows a sales leader to ask for updates on any active deal. Similar to ChatGPT with pluggins but can connect hubspot, SF, gong, fathom and update relevant fields.

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 goes through all the transcripts / notes from the meetings I had today - Gong or Fathom. Take the list of next steps & or follow up items. Provide me with a draft for each follow up item. I.E. emails or slack message.

Create an agent that looks at my google calendar invites for the day. For any external meetings do research on hubspot to see if there is an associated deal, scrape previous calls, and look into the company website to prep me for the meeting. Additionally give a summary of each person joining the call and provide a link the their LinkedIn profile. I want this roll up sent for each meeting on slack every morning.

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 monitors mentions of our company or key topics across Twitter/X, LinkedIn, and YouTube.
• Identify influencer or partner mentions, categorize them (positive, neutral, negative), and record them in a Notion database.
• Send a daily digest to Slack summarizing high-impact mentions and recommended engagement actions.

Create an agent that monitors Slack, Gong, and HubSpot for mentions of customer success or wins.
• When a rep mentions a happy customer, automatically create a Notion entry for potential case study opportunities.
• Include deal owner, ARR, and customer context.
• Send a weekly digest to the marketing team with “new potential stories.”

Create an agent that reviews top-performing blog posts in HubSpot or Webflow every month.
• Identify posts older than 6 months that have dropped in traffic or ranking.
• Suggest 3–5 keyword or content updates using SEMrush or Firecrawl data.
• Output an SEO update checklist in Notion or as a Slack thread.

Create an agent that pulls campaign performance data weekly from HubSpot, LinkedIn Ads, and Google Ads.
• Summarize top-performing campaigns, ads, and content by CTR, CPL, and engagement.
• Highlight underperforming assets and suggest next experiments (e.g., headline A/B test, new CTA).
• Generate a Notion summary and a Slack post tagged to #marketing-performance every Monday.

Create an agent that takes a url, and finds their case studies then extract the content from each case study. With that info then I want to extract who is their user: the name, role and company they work for. If there is a testimonial give it to me, otherwise summarize how they use the productwe can scrape with firecrawl, and we need to scrape all case studies that are on the url

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 that monitors competitor press releases using Firecrawl. Look at the releases in the last 24 hours,, summarize the key product or pricing changes and compare them to my Notion battlecard. Then generate a Slack update for the sales team with 3 talking points they can use in conversations.

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 automates the first-pass review for M&A due diligence by analyzing all documents in a target company’s data room.

• Inputs:    
- Contract and policy PDFs    
- Litigation summaries or filings    
- Due diligence checklist (Markdown or Notion)

• Workflow:    

1. Ingest Node: Parse all uploaded materials and categorize them by type (corporate, IP, HR, litigation, compliance).    
2. Checklist Node (GPT-4.1): Match documents to checklist items, flag missing or incomplete sections, and extract key issues.    
3. Risk Node (GPT-4.1): Score issues by severity (High / Medium / Low) and classify them by area (legal, financial, operational).    
4. Summary Node: Generate a Markdown diligence summary with:        
▪ Executive overview of top risks        
▪ Section-by-section issue list        
▪ Follow-up questions for counsel        
▪ Recommended next steps for negotiation

• Outputs:    

- issues_log: JSON of findings and risk levels    

- due_diligence_summary: Markdown review for legal and deal teams

Create an agent that compares negotiation drafts from multiple counterparties (e.g., customer legal, procurement, vendor) to highlight open issues and positions.

• Inputs:    
- Multiple contract versions (e.g., Round 1, Round 2, Final)

• Workflow:  
1. DiffNode: Detect redlines and changes across all drafts.    
2. IssueExtractor (GPT-4.1): Group edits by issue type (payment terms, IP, governing law, etc.).    
3. PositionAnalyzer: Determine stance alignment (Accepted / Pending / Countered) across parties.    
4. Summary Node: Output a Negotiation Dashboard summarizing:        
- Open issues with owner & last edit date        
- Convergence percentage over time        
- Recommended next steps for each stakeholder    
5. SheetUpdater: Create a new Google Sheet summarizing open issues and next steps for each stakeholder

• Outputs:    
- negotiation_issues: JSON issue log    
- Entry in Google Sheet

Create an agent that assists legal teams during discovery by clustering and summarizing relevant case materials.

• Inputs:    
- Email archives, filings, depositions, and correspondence PDFs    
- Case outline (topics and relevance criteria)

• Workflow:    
1. Ingest Node: Parse and classify documents by type and relevance.    
2. Cluster Node (GPT-4.1): Use semantic embeddings to group documents by issue (e.g., breach of contract, misrepresentation).  
3. Summary Node: For each cluster, generate a factual summary, key quotes, and potential exhibits.  
4. Timeline Builder: Automatically construct a chronological case timeline highlighting pivotal events and communications.

• Outputs:    

-case_clusters: JSON of document groupings    

- case_timeline: Markdown timeline with summaries and references

Create an agent that extracts obligations and key dates from executed contracts.
• Identify deliverables, renewals, payments, and notification requirements.
• Sync reminders with Google Calendar in a new calendar with the customer’s name

Create an agent that monitors regulatory updates from government and industry websites every week (e.g., FTC, SEC, EDPB).
• Extract new guidance, rulings, or enforcement actions relevant to our business.
• Summarize weekly highlights in a Notion database tagged by region and topic.
• Post a short digest to Slack with key takeaways.

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 compares two contract versions (e.g., customer vs. vendor edits).
• Identify added, deleted, or modified language at the clause level.
• Summarize material changes (e.g., liability caps, indemnity, termination).
• Provide a “redline-style” summary in Markdown with section references.
• Tag each change with a risk category and recommended response.

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.
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• 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 takes patient messages, EHR notes, and ticketing systems for escalation signals then notifies care teams in Slack.
• Parse inbound patient communications for urgent language (e.g., “chest pain,” “shortness of breath,” “urgent refill”).
• Check patient records in the EHR for context (recent discharge, chronic condition).
• Post an alert in the #clinical-escalations Slack channel with:
   
- Patient summary (age, condition, provider)    
- Escalation reason and urgency level    
- Suggested next steps or routing (e.g., on-call nurse, emergency triage)• Log event metadata back to Hubspot for case tracking.

Create an agent that monitors EHR events (labs ordered, imaging pending, follow-up needed) and triggers next steps.
• Parse task lists from care teams.• Assign ownership, deadlines, and reminders in Slack or project tools.
• Provide a daily summary of outstanding actions per provider.

Create an agent that predicts readmission risk for recently discharged patients.
• Analyze EHR data, discharge summaries, and prior admissions.
• Score each patient (Low / Medium / High risk).
• Recommend interventions such as follow-up calls or medication reconciliation.

Create an agent that reviews clinician notes for format and compliance with documentation standards.
• Detect incomplete or unstructured notes.
• Rephrase in standard SOAP format
• Flag potential compliance gaps before billing submission.• Generates final desired output

Create an agent that converts inpatient progress notes and orders into structured discharge summaries.
• Extract key diagnoses, procedures, medications, and follow-up instructions.
• Ensure compliance with Joint Commission standards.
• Output structured JSON for EHR upload and Markdown summary for patients.

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 automates provider credentialing verification.
• Parse uploaded documents (licenses, board certifications, malpractice history).
• Cross-check against primary sources (NPPES, state licensing boards).
• Flag missing or expired credentials and generate a compliance checklist.

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 an agent that automates the initial review of prior authorization requests.
• Parse clinical notes, CPT/ICD codes, and medical necessity forms.
• Compare request data against plan criteria and coverage policies.
• Flag missing documentation or potential denials.
• Output a structured summary for clinician review and audit tracking.

Create an agent that takes the most recent 10k from SEC’s website for a ticker symbol for credit analysis.
• Parse balance sheet, income statement, and cash flow data.
• Calculate key ratios (debt-to-equity, current ratio, EBITDA margin).
• Summarize creditworthiness and red flags in Markdown.

Create an agent that assists underwriters with application review.
• Parse application PDFs, health questionnaires, and prior policy data.
• Evaluate against underwriting guidelines.
• Generate recommendations: approve, deny, or require further information.
• Output decision summary and update Salesforce case record.

Create an agent that aggregates financial news, filings, and analyst reports.
• Extract insights about key tickers, sectors, or market themes.
• Summarize with sentiment scoring and notable price moves.
• Generate a daily digest in Slack for research and trading teams.

Create an agent that reads new regulatory updates or enforcement actions.
• Summarize key points, affected rules, and deadlines.
• Compare changes to current internal compliance manuals.
• Generate Markdown digest and Slack summary tagged to compliance leads.

Create an agent that predicts policy renewals and potential churn.
• Analyze customer data: tenure, claim history, engagement frequency, payment behavior.
• Generate a renewal likelihood score.
• Post at-risk accounts to Hubspot and Slack for proactive outreach.

Create an agent that triages incoming insurance claims based on completeness and risk.
• Parse claim forms, photos, and adjuster notes.
• Categorize claims by type (auto, property, life) and priority.
• Flag high-risk or complex cases for manual review.
• Output structured JSON for claims management system ingestion.

Create an agent that compiles a weekly summary of each client’s investment portfolio.
• Pull holdings, performance, and benchmark data from a CSV, Salesforce or portfolio systems.
• 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.
• Parse customer-uploaded documents (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 and update Hubspot with KYC status

Create an agent that analyzes transaction patterns to identify potential fraud.
• Pull recent transactions from internal APIs or CSVs.
• Detect anomalies using rule-based and LLM pattern recognition (e.g., velocity, unusual merchant, location mismatch).
• Summarize flagged cases in Slack with supporting evidence.
• Generate JSON output for fraud operations dashboards.

Create an agent that analyzes ticket volume and sentiment trends via Slack threads.
• Identifies customers at risk of churn based on engagement drop-offs.
• Scores each account and adds a “Churn Risk” tag to HubSpot.
• Sends a Slack summary to CSMs with recommended re-engagement actions.

Create an agent that identifies recurring support questions from email and Slack transcripts.
• Clusters them by topic and drafts short, standardized responses.
• Updates the internal knowledge base in Hubspot weekly with new entries.

Create an agent that tracks response and resolution times across all support tickets in Zendesk.
• Flags tickets that exceed SLA thresholds.
• Posts daily summaries in Slack, grouped by severity level.
• Provides monthly SLA compliance reports.

Create an agent that aggregates customer feedback from tickets in Zendesk, calls in Gong, and Slack threads.
• Classifies feedback into themes (UI, performance, pricing, integrations).
• Summarizes top 5 recurring pain points each week.
• Generates a Notion page for Product and Success teams.

Create an agent that detects when a support ticket is escalated (e.g., repeated follow-ups or high urgency).
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- 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 compiles a QBR (Quarterly Business Review) summary for each account. It should pull data from product usage metrics, HubSpot deal notes, and recent support tickets. It then summarizes outcomes, product adoption, and key discussion points. Finally generates a slide deck or Notion brief to share with the customer.

Create an agent that reviews daily customer interactions from email, Slack, or Gong transcripts. It should detect sentiment (positive, neutral, negative). Then flag negative or urgent messages and notifies the assigned rep in Slack. Update the sentiment score field in Salesforce. Create that field if needed

Create an agent that scans HubSpot (or Salesforce) for deals with upcoming renewal dates in the next 60 days.

• Cross-reference customer activity from Gong, Fathom, or Slack to detect low engagement.
• 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”).

Create an agent that monitors a shared Gmail inbox for incoming customer support emails. It should classify messages into categories (billing, technical issue, feature request), create a Linear ticket if it’s a bug, and post a summary in a Slack channel.If the customer is in HubSpot, update their contact record with the latest support interaction.