Our team is working on updating old RPA workflows that currently rely on fragile, pixel-based automation solutions. We're looking to transition toward a more intelligent, web-native approach using scalable browser agents powered by AI. These would help us understand complex web pages and execute workflows dynamically, steering clear of predefined selectors that often fail. We're envisioning a platform where AI can adjust in real time to user interface changes.
We're particularly interested in your insights on a few key points:
1. Performance at Scale: Has anyone successfully implemented AI-powered web interaction for a large number of concurrent processes? What do the latency and cost profiles look like compared to traditional automation tools?
2. Integration and Control: How do you manage these agents? Is there a central cloud dashboard or tool that you've created or used to monitor, queue, and control agent activities?
3. Real-World Reliability: For critical business functions, can AI agents achieve the same 99.9% reliability as well-structured traditional scripts, or is there a trade-off for greater adaptability?
We're looking for more than just product names; we want to hear about architectural choices, frameworks, and any lessons learned during the transition from deterministic to probabilistic automation.
5 Answers
AI agents are more of an addition than a total replacement. For large-scale projects, relying solely on LLMs can be costly and slow. I prefer to use deterministic tools like Playwright for about 80% of tasks and then use the LLMs to help with locating elements or handling unexpected situations. It's key to set up proper management with a queue and logging system to maintain that 99.9% reliability.
It's an exciting shift towards balancing determinism and adaptability. Traditional RPA works well when UIs are stable, but can be troublesome with changes. AI browser agents are more flexible since they understand intent, but they come with added costs, variability, and latency at scale. For important workflows, I would lean towards a hybrid model—using fixed scripts for routine critical tasks and AI for handling more dynamic scenarios.
We've gone through a similar transition from classic RPA to more adaptive methods. A useful tip is to categorize your workflows into deterministic and exploratory types. For tasks like invoice processing where the workflow is fairly standard, traditional methods work best. But for situations that require navigating varied layouts, AI agents start to show their value. On reliability, a hybrid approach works nicely, where the AI handles navigation while critical tasks still rely on confirmed checks, which is essential for high-stakes actions.
Replacing brittle selectors with AI might seem like a great move, but it can lead to new issues. AI browser agents are fantastic when it comes to adaptability, but for critical tasks, many teams hybridize, maintaining a deterministic base with AI as a backup. Orchestration tools (like Runnable) are crucial here, as managing state, retries, and monitoring can be more challenging than the clicking itself.
I've also been diving into AI browser agents. I often use Runable along with Cursor and Vercel for web automation. When it comes to scaling performance, I found that Runable's AI agents can handle a solid number of concurrent tasks, but I did face some latency issues. I had to get a bit creative with my system architecture to manage that. In my experience, there’s always a trade-off between flexibility and that coveted 99.9% reliability, but for what I need, the benefits of AI are worth the risk.
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