Design Engineer Intern

Case study: automating Gumloop's database management with Atlas's schema-as-code tooling. Noa Rogoszinski DevRel Engineer "The biggest win of any tool is when you don't need to look at it ever again and it just works." - Wai Ho Choy, Infrastructure Lead, Gumloop Company background. Gumloop is a collaborative platform that empowers anyone in a company to build AI agents using their preferred models and integrations, while giving IT enterprise-grade visibility and control. Whether these agents are deployed in Gumloop's secure environment or within your own infrastructure, your data always stays entirely in your systems. Managing schemas like Terraform. When Wai Ho Choy joined Gumloop in late 2025, the engineering team had just migrated its infrastructure to Terraform. Declarative, reproducible deployments were the new organizational standard. Kubernetes worked that way; GCP resources worked that way. Databases, however, did not. Schema changes were still applied manually, with SQL operations executed one at a time via Google Cloud commands. Managing multiple database copies across client environments made it impossible to answer basic questions reliably: Was a change actually applied? In what order? Did it stay in sync with the application code? "Those are the basic problems of maintaining a database without infrastructure-as-code," Wai Ho said. The natural next step was to manage schemas the same way Gumloop managed everything else: define the desired state in code, let the platform reconcile the system to match, and trust the process to be entirely repeatable. The team initially turned to Terraform for Google Cloud Spanner schema definitions, but Spanner support proved too limited. Operations failed, sequences broke, and index changes conflicted. Ultimately, the Terraform operator rollout did not survive contact with production. Gumloop needed a true infrastructure-as-code (IaC) equivalent for databases that was declarative by design, deterministic during migrations, and seamlessly compatible with the rest of their toolchain. Searching for a solution. With Terraform ruled out for day-to-day schema management, Gumloop turned to the conventional options. Liquibase was the default recommendation, and Flyway was another contender. Operating under a tight deadline, Wai Ho evaluated both. Gumloop's manual processes were already fracturing as dedicated customer deployments multiplied. There was no time for a lengthy evaluation or a heavy rollout. The team needed a tool that could bridge two worlds: the declarative model promised by Terraform and the explicit sequencing of versioned migrations. Neither legacy option fit the bill. "Liquibase felt overly verbose and generally outdated," Wai Ho said. Flyway and other alternatives required extensive boilerplate setup before Gumloop could manage a single database. Neither felt like a natural extension of the IaC culture the team had just established with Terraform. Furthermore, a unique requirement shaped their search. Since Gumloop builds heavily with AI coding agents, any schema tooling had to seamlessly integrate into a programmatic, terminal-first workflow. "I want to see linting output in GitHub Actions. I want my AI to be able to see it, concisely, in one scroll at most," Wai Ho explained. Choosing Atlas. Atlas was not initially on the list, but once Wai Ho evaluated it alongside Liquibase and Flyway, the contrast was stark: Atlas shipped as a single binary and natively supported both declarative and versioned workflows out of the box. "When I looked at the documentation, I thought, 'This is what developers actually want to use,'" Wai Ho recalled. "The guides for every integration I could ever need were right there and incredibly well-documented, whether for GitHub Actions, the Kubernetes operator, the Terraform provider, or declarative and versioned migrations." Atlas supports both declarative and versioned migration models without forcing the team to adopt a second tool or a conflicting mental model, allowing Gumloop to achieve a working integration faster than with any other option on the table. "I fought for Atlas," Wai Ho said. "If I'm going to maintain multiple databases, this is the tool I'm choosing." Today, Atlas serves as the schema management layer across Gumloop's multi-tenant environments spanning Google Cloud Spanner, Amazon Aurora PostgreSQL, and ClickHouse, and integrates seamlessly into their automated CI/CD pipeline. On every pull request, a GitHub Action spins up the Atlas CLI to validate changes by performing a precise three-way comparison against staging and production. To top it off, the team configured their AI agents to use Atlas's documentation for context awareness when interacting with Atlas's CLI outputs. This allows them to interpret real-time linting results, generate accurate schema changes, and handle integration syntax completely on their own. The outcome. Choosing Atlas gave Gumloop a schema workflow that perfectly mirrors how the team thinks about infrastructure and how they build with AI. Today, when a schema change is needed, an AI agent often drafts the migration, a human reviews it, and CI takes over. If a migration fails, the feedback loop is instantaneous. "The linter may pass visually, but when you run the development database against it, it cracks," Wai Ho described. "That is where Atlas helps. It answers: 'If you were to deploy this today for a new tenant, would it work?'" When a failure occurs, the Atlas CLI output is specific enough that the AI agent can parse the error and automatically fix it. Months after adoption, Atlas runs quietly in the background, delivering several core benefits to the Gumloop team: * True IaC for Databases: Gumloop maintained the declarative mindset they used for Kubernetes and GCP while gaining deterministic, versioned migrations. "Once I started using Terraform, I couldn't go back to anything that wasn't like it," Wai Ho said. * Multi-Cloud Consistency: Atlas acts as the unified schema layer across Google Cloud Spanner, Amazon Aurora PostgreSQL, and ClickHouse. When an AWS expansion required switching from versioned migrations to a declarative model, the team executed the pivot seamlessly without any additional tooling. * Automated Drift Detection: By running three-way comparisons between pull requests, staging, and production on every PR, Atlas catches manual hotfixes instantly and gives developers complete context before deployment. * AI-Native Workflows: The terminal-first, concise CLI output allows AI coding agents to independently debug, reorder files, and resolve migration issues without human engineers getting bogged down in long debugging loops. * Zero Overhead: Atlas successfully handles thousands of lines of schema code without demanding ongoing maintenance. As Wai Ho concluded, "The goal of any infrastructure engineer is to build a bridge that stays up. If I have to go back in, the tool has failed. With Atlas, it's been months since I've had to touch anything." Getting started. By treating database schemas with the same infrastructure-as-code discipline as the rest of the stack, Gumloop eliminated the manual bottlenecks that threaten scaling engineering teams. Integrating Atlas into their CI/CD pipeline didn't just automate their migrations, it created a deterministic, visible, and AI-ready workflow without adding operational overhead. If your team already manages infrastructure with Terraform and wants the same approach for database schemas, or needs schema checks that fit an AI-assisted development workflow, Atlas could be the solution for you.

Gumloop vs Airtable: choosing the right AI automation engine. A deep dive comparison into Gumloop's node-based AI orchestration versus Airtable's database-first AI workflows. The evolution of AI workflows. In the current landscape of enterprise digital transformation, the line between data storage and automated action is blurring. Traditionally, businesses kept their data in one place and their logic in another. Today, the rise of Large Language Models (LLMs) has necessitated platforms that can both house information and process it using autonomous reasoning. Airtable has long been the titan of the low-code database world, evolving from a simple spreadsheet replacement into a sophisticated application builder with native AI features. Gumloop, conversely, represents the new guard of 'agentic' automation. It is a visual framework designed specifically to chain together complex AI operations, treating the LLM as a core component rather than a featured add-on. Choosing between them is not merely a matter of checking feature boxes; it is about deciding where the 'brain' of your operation should live - within your data records or within a discrete, logic-heavy canvas. Core feature set comparison. Airtable provides a rich ecosystem of relational data management. Its AI capabilities, known as Airtable AI, allow users to summarize records, categorize entries, and generate content directly within cells. Its primary strength lies in its interface - building custom internal apps and dashboards that non-technical stakeholders can use immediately is arguably its greatest value proposition. Gumloop (formerly known as Forloop) focuses on the 'plumbing' of AI. It offers a node-based editor where you can drag and drop LLM prompts, web scrapers, and data processors. While Airtable is great at structured data, Gumloop excels at unstructured tasks: scraping a website, feeding that data into a specific GPT-4 prompt, verifying the output with a second AI agent, and سپس sending the result to a third-party API. While Airtable leverages 'Automations' to trigger simple actions, Gumloop's entire architecture is built for multi-step reasoning. Gumloop allows for loops, branching logic, and complex error handling that far surpasses Airtable's native automation building blocks. Database vs. Canvas: the structural difference. The fundamental difference is structural. Airtable is a database-first platform. Information must live in a table to be acted upon. This makes it ideal for Project Management, CRM, and Inventory tracking where the 'state' of the data is the most important factor. You see your items, you see their status, and you see the AI-generated enrichment right next to them. Gumloop is a canvas-first platform. It is designed for high-volume processing where you might not even want to store the intermediate data. It is a factory for information. You might use Gumloop to process 5,000 LinkedIn profiles to extract specific insights. While you could push that data into Airtable afterward, the 'work' happens in Gumloop's flow environment. This architectural split dictates how you handle complexity. In Airtable, complexity is managed through views and linked records. In Gumloop, complexity is managed through nested flows and modularized agents that can be reused across different internal pipelines. Pricing Models and scalability. Airtable operates on a seat-based pricing model. This is standard for SaaS but can become expensive for large teams where only a few people are actually building the automations. Their AI features usually require an additional credit-based add-on or a higher-tier subscription (Pro or Enterprise), making cost prediction slightly difficult as your AI usage scales. Gumloop uses a more usage-centric approach. Since it is a developer-centric tool at heart, pricing often aligns with the compute and tokens you consume. This is generally more cost-effective for high-volume automated tasks that don't satisfy a 'per-user' value metric. If you are running an automation that processes thousands of records in the background, Gumloop's model often provides a better ROI. For small teams, Airtable's free tier is generous for data storage, but the AI and automation limits are reached quickly. Gumloop offers a low barrier to entry for builders, allowing them to experiment with complex flows before committing to enterprise-scale usage. Ideal users and specific use cases. Airtable is the winner for 'Operations' teams. If you need to build a content calendar where AI suggests headlines, or a recruiting pipeline where AI summarizes resumes, Airtable's ability to combine data visualization with light automation is unbeatable. It is the tool for people who need a UI to interact with their data. Gumloop is the winner for 'Automation Engineers' and 'Growth Hackers'. If your goal is to build an autonomous research bot that monitors news sites and posts summarized updates to a Slack channel, or a system that crawls competitors' pricing and runs a sentiment analysis on their reviews, Gumloop is far more powerful. It handles the 'messiness' of the web and high-frequency AI API calls much more gracefully. A frequent hybrid approach is becoming common: using Gumloop as the 'engine' to scour the web and process data, then pushing the final, cleaned results into Airtable to be reviewed by a human team. Final verdict: which should you choose? Choose Airtable if your primary need is a 'Single Source of Truth' for your business data and you want to use AI to make that data more useful. It is the best choice for collaborative environments where multiple team members need to view, edit, and organize information within a polished interface. Choose Gumloop if you are building an AI-first product or technical workflow where the database is secondary to the logic. It is specifically built for those who find Zapier too simple and Airtable Automations too restrictive. Gumloop is the tool for building the automated workforce of tomorrow. In short: Airtable is where your team works; Gumloop is where your AI agents work. FAQs. Can I use Gumloop and Airtable together? Which tool is easier for a non-technical person? Does Airtable support GPT-4? Is Gumloop suitable for enterprise-scale automations?