Integuru

es, but it wasn’t perfect either. The original agent did things the obvious way: (1) have a human do the action; (2) the agent observes the network requests and (3) recreates them. That got us far, but it only supported the path the user triggered. In production, we saw all the uncovered cases: different states, missing fields, permission differences, hidden validations, and request changes we could never catch in a single run. So we started building a new solution from the ground up. Our first step was to add agents that trigger many variations of the same action. To protect the platform’s data integrity, we added a gating layer that blocks outbound requests. This lets us observe the exact request structure, branching behavior, and platform logic without accidentally mutating the live system. But this still wasn’t enough. Some logic is hard to surface by execution alone. A lot of the business rules live in the frontend bundle. So we set out to analyze the true “answer sheet” for each platform: the source code. After experimenting, we got this working. We built a source-code analysis layer that deobfuscates and traces the code associated with each action. In practical terms, our system can handle most tricky edge cases without triggering all flows. Together, these two layers result in much better coverage of the production surface area. They support more edge cases, fail less often, and avoid a lot of the brittle one-off fixes that usually come later. Finally, we added auto-healing and API doc generation to improve reliability and the UX. We also offer a 24/7 on-call maintenance team for companies on the production plan. We now spend most of our time supporting vertical AI companies and helping them connect to their customer systems. We offer a free plan for integrating with one platform and charge for additional platforms, accounts, and overage API calls. For instance, we help healthcare AI companies connect to EHRs and payer portals, and logistics companies connect to TMSs and ERPs. Some companies are now running more than 1M monthly requests per platform. Across our production users, API calls complete in ~3 seconds at 99.9%+ success rate on average. We’re also building a library of APIs that users can use out of the box. That said, this version still has limitations we want to iterate on. Although we already tackle some anti-bot mechanisms, the agent still struggles to generate integrations with heavily anti-botted platforms. When the agent fails, our on-call team steps in to improve the agent or build the integration manually if the customer requests it. Also, the UX for generating an integration is still quite manual. Our next step is to build a CLI experience, so people and their agents can create, test, and use integrations in a much more flexible manner. This also prevents humans from having to wait for Integuru to finish its tasks. We want to one day allow developers and agents to integrate with all platforms instantly. Integuru is an ongoing effort. We’re passionate about automating integrations and would love your feedback!

a accessible. We contacted banks, brokerages, payroll software, and more to request access to their APIs, but none took us seriously. We resorted to building integrations with these systems to extract documents like W-2s and 1099s. We initially used browser automation but ran into two big problems: our integrations (1) weren’t reliable due to changing UIs and (2) had slow execution speeds due to spinning up browsers and waiting for pages to load. We experimented with AI-based automation maintenance, but it didn’t solve slow speeds. So, we concluded that browser automation is useful when execution speed isn’t essential, but reverse engineering is often the only path for performant integrations. Through reverse-engineering dozens of platforms, we noticed many internal API design patterns that LLMs could decipher. We built an agent to automate the creation of integrations. Today, Integuru can analyze a platform’s internal API designs and build an integration in minutes. The agent mimics what a human does when reverse-engineering. Say you want to download utility bills from a utility website. You’d first use Integuru to generate a file of network requests and a file of cookies. You pair these two files with a prompt about your desired action—in this case, to download utility bills. Integuru identifies the final request that downloads utility bills. The request URL might look like this: https://www.example.com/utility-bills?accountId=123&userId=4.... It then identifies parts of the request that depend on other requests. The example URL contains dynamic parts— accountId and userId—that usually are in the response of previous request(s). It then finds other requests whose response contains any of these and adds them to the dependency graph. The newly found request URLs might look like https://www.example.com/getAccountId, https://www.example.com/getUserId, and so on. This process repeats until the most recently found request doesn’t depend on any other request. Integuru then traverses up the graph, starting from the requests without dependencies while converting each request into a runnable function. Integuru supports a surprising number of use cases like downloading documents, sending money, creating virtual cards… People already use the agent to build low-latency APIs for platforms like Robinhood, transportation management systems (TMS), and more. However, the agent still has limitations due to current LLM capabilities and long-tail edge cases, but we’ve been giving each platform to the agent for the first try. When the agent does struggle, we find the generated graphs and code still helpful as references for us to complete the work manually. The agent and all integrations are open-source under AGPL-3.0. We charge for services to (1) build custom integrations when the agent struggles or for your convenience, (2) handle hosting, and (3) manage authentication using authentication cookies from authenticated browser sessions. We charge per API call with an implementation fee for new platforms. We’re currently working to increase the agent’s coverage and improve code generation. We will continue to iterate and want to one day allow developers to integrate with all platforms instantly. Integuru is still an early effort. We’re passionate about automating integrations and would love your feedback!