Are you using AI Agent or AI Actions? Come share at the Next Qrew Meetup!
Hello All! I am searching for folks who are currently using the AI Agent and AI Actions to share their experience and feedback and what you've learned so far with the Qrew. Have you used AI Agent to document your apps? Improve a pipelines workflow? If you have a use case, please ping me or respond below! I am looking for customers to share at our next Qrew meetup August 18th or 19th at 12pm EST. Thank you and I look forward to bringing your story to the Qrew in August. -EstherWeekly Report Summaries with AI Actions in Pipelines
So far in this series, we’ve explored how AI Actions can extract key details from emails and analyze complex documents like 10-K filings. Today, we’ll make it even more practical — automating weekly reporting. Many teams rely on weekly Quickbase reports to track key metrics, project updates, or customer issues. With AI Actions, you can now generate a concise AI-written summary of those reports every week — automatically. No manual copy-pasting, no rewriting dashboards, no time wasted on reading reports — just clear insights, delivered on schedule. Why Automate Weekly Summaries? Quickbase reports are great at presenting data — but stakeholders often want a digestible narrative: What changed this week? What trends are emerging? What should we focus on next? By scheduling a Pipeline, it can automatically: Pull a Quickbase report each week. Feed it to AI Actions along with some instructions. Store or email the AI’s analysis to your team. The Pipeline Flow Here’s how this works step by step: Schedule: A scheduled pipeline runs every Monday morning. Quickbase Actions: The pipeline fetches the latest report data (e.g., project updates, tickets, or KPIs). It stores it as a CSV in Quickbase. AI Action: The data is analyzed and summarized by AI in plain English. Quickbase or Email Output: The summary is emailed to key stakeholders. You can also watch the quick demo here: Pipeline YAML: # Report Summary (CSV) # # Account slugs: # - quickbase[REDACTED]: Realm Default Account <none> # - microsoft-outlook[REDACTED]: Georgi Peev (...) # <none> --- - META: name: Report Summary (CSV) crontab: 0 8 * * 1 crontab_timezone: America/New_York enabled: false - ACTION bucket pipeline_row define -> a: inputs-meta: date_format: '%Y-%m-%d' datetime_format: '%Y-%m-%dT%H:%M:%SZ' header_row: Project ID, Project Name, Status, Priority, Owner, Department, Region, Start Date, End Date, Budget, Description, Customer Name, Project Manager, Team Size, Completion, Risk Level, Last Updated, Client Satisfaction, Is Billable, Project Type, Revenue ($), Expense ($), Profit Margin (%) header_separator: ',' type_of_budget: number type_of_client_satisfaction: number type_of_completion: number type_of_customer_name: string type_of_department: string type_of_description: string type_of_end_date: date type_of_expense: number type_of_is_billable: boolean type_of_last_updated: date type_of_owner: string type_of_priority: string type_of_profit_margin: number type_of_project_id: string type_of_project_manager: string type_of_project_name: string type_of_project_type: string type_of_region: string type_of_revenue: number type_of_risk_level: string type_of_start_date: date type_of_status: string type_of_team_size: number name: Define table for report - QUERY quickbase[REDACTED] record search -> b: FILTERS: - AND: - status is "In Progress" <in progress=""> inputs-meta: export_fields: '"Budget ($), Client Satisfaction, Completion %, Customer Name, Department, Description, End Date, Expense ($), Is Billable, Last Updated, Owner, Priority, Profit Margin (%), Project ID, Project Manager, Project Name, Project Type, Region, Revenue ($), Risk Level, Start Date, Status, Team Size" <15, 23, 20, 17, 11, 16, 14, 27, 24, 22, 10, 9, 28, 6, 18, 7, 25, 12, 26, 21, 13, 8, 19>' table: '"AI Actions - Georgi Peev: Report Summary - Projects" <bvjypq5b3>' name: Fetch report records - b<>LOOP: - DO: - a<>ACTION bucket pipeline_row create -> c: inputs: budget: '{{b.budget}}' client_satisfaction: '{{b.client_satisfaction}}' completion: '{{b.completion}}' customer_name: '{{b.customer_name}}' department: '{{b.department}}' description: '{{b.description}}' end_date: '{{b.end_date}}' expense: '{{b.expense}}' is_billable: '{{b.is_billable}}' last_updated: '{{b.last_updated}}' owner: '{{b.owner}}' priority: '{{b.priority}}' profit_margin: '{{b.profit_margin}}' project_id: '{{b.project_id}}' project_manager: '{{b.project_manager}}' project_name: '{{b.project_name}}' project_type: '{{b.project_type}}' region: '{{b.region}}' revenue: '{{b.revenue}}' risk_level: '{{b.risk_level}}' start_date: '{{b.start_date}}' status: '{{b.status}}' team_size: '{{b.team_size}}' name: Add record to table note: match the column names from the query step - a<>ACTION bucket pipeline_row download_csv -> d: {} - ACTION quickbase[REDACTED] record create -> e: inputs-meta: table: '"AI Actions - Georgi Peev: Report Summary - CSV files" <bvjy9kdu7>' name: Create record for file - e<>ACTION quickbase attachment upload -> f: inputs: field: '6' name: '{{d.file_name}}' url: '{{d.download_url}}' name: Upload report file - LOOKUP quickbase[REDACTED] record look_up -> g: inputs-meta: table: '"AI Actions - Georgi Peev: Report Summary - CSV files" <bvjy9kdu7>' export_fields: '"file" <6>' inputs: id: '{{e.id}}' name: Fetch uploaded file - ACTION qb-ai-actions custom_action create -> h: inputs: file_url: '{{g.file.file_transfer_handle}}' system_message: "You will receive a CSV file representing a Quickbase report.\ \ Your job is to analyze the report and produce an HTML-ready email containing\ \ the analysis. Output must be only valid HTML suitable for sending as the\ \ body of an email (no external CSS, no scripts, inline styles only). \nThe\ \ report contains currently active projects.\nBased on trends and dependencies:\n\ - Flag the top 10 projects that are at risk the most. Provide some details\ \ like project id, project name, PM and other info you find useful.\n- Provide\ \ an explanation on why you've flagged each one\n- Give a suggested action\ \ item for each one" user_message: file attached below name: Summarize - ACTION microsoft-outlook[REDACTED] email send_email -> i: inputs: body: '{{h.output_text}}' subject: Weekly report - at risk projects to_addresses: ... ...</bvjy9kdu7></bvjy9kdu7></bvjypq5b3></in></none></none> The AI Prompt You can tailor the AI persona to your audience. Here’s the one from the example that works well for leadership updates: You will receive a CSV file representing a Quickbase report. Your job is to analyze the report and produce an HTML-ready email containing the analysis. Output must be only valid HTML suitable for sending as the body of an email (no external CSS, no scripts, inline styles only). The report contains currently active projects. Based on trends and dependencies: - Flag the top 10 projects that are at risk the most. Provide some details like project id, project name, PM and other info you find useful. - Provide an explanation on why you've flagged each one - Give a suggested action item for each one Why This Matters This example demonstrates how AI Actions can automate recurring intelligence from Quickbase data — not just static documents or ad-hoc inputs. By scheduling weekly summaries, you’re turning your operational data into living insights your teams can actually use. You can take this further: Summarize multiple reports in one flow Compare current vs. previous week automatically Send or record summaries via other Pipelines channels Conclusion AI Actions continues to expand what’s possible inside Pipelines. From analyzing emails and PDFs to now scheduling recurring AI insights, Quickbase users can close the gap between raw data and decision-ready information. Start small: schedule your first AI-powered summary this week — and let your reports start writing themselves!AI & I
The Qrew is excited to bring voices from our Community into our blog. As both a current customer and former Quickbase staff member, Wes McAda is a long-time member of The Quickbase Qrew. If you have an interesting take or perspective on what's taking place in Tech today, and you are interested in writing a blog like the one below, please email Ben Simon at [email protected]. There are many AI tools and uses to consider nowadays. There is talk about it making many jobs, from artists to software developers, redundant. As a professional developer and an occasional artist, I have used AI for both. For example, my goofy, dad-joke loving side gets a kick out of this image: As a Florida State alum and football fan, and lover of both artiodactyls and beer, just “Yay!”. It is an FSU Efes Ewe, but I capriciously digress. I guess I could have paid an artist to render this, but I probably would not have spent the money on it. AI made this silliness accessible. AI also makes higher value work easier. When it comes to software development, I use AI to accelerate front end development work. With proper prompting, I can get code using HTML, CSS, and JavaScript that helps me go much quicker. I do not do a ton of front end work, but when I do, I rarely start from scratch anymore. The same goes for my Python scripts, which I sometimes use to get more out of the Quickbase API. More often than not, I use AI to get started. AI can also help provide alternative approaches for jinja templates that one may use in Quickbase Pipelines or formulas for Excel, SQL queries, or Quickbase formula fields. There are issues with the outputs, so users beware that you still need to know a thing or two about the subject matter and your context. One also needs to be careful providing inputs that may compromise confidentiality, security, or privacy. The point is that AI enables productivity so that I can focus on impact. AI also helps me in ways that other services like Mockaroo or Canva might; I can get downloadable mock data per my specifications or color palettes in hex codes for my colorblind development work. AI helps avoid a tedious task or facilitates my handicap in this case. One area in which there is professional/personal crossover with AI is writing. Anyone who has coded or developed a Quickbase app is familiar with the struggle of figuring out what to name variables or attributes. However, when writing emails or even something creative like one of my recent haikus, AI helps me with words. AI does not write for me; it helps me write better. The AI tools that I generally use are Copilot and ChatGPT. I have also created agents in Boomi, but I am in early stages on how to incorporate a trained agent in a workflow yet. I am looking forward to Quickbase’s forthcoming MCP server offering with great anticipation! My expectation is that enterprises will profit greatly from AI agents when we gain greater confidence in AI’s security and can leverage its processing power on premises. But what interests me most about AI today is how other devs like me are using it. What are some ways others in The Qrew are using AI? Share below!
