Hi everyone! I’m working with AI agents deployed on different platforms including ChatGPT, Google AgentSpace, and Langsmith. The main issue I’m facing is that it’s really hard to keep track of everything when the agents are spread across these separate systems.
Right now I have to check each platform individually to see how the agents are performing, what they’re costing, and if there are any issues. This takes a lot of time and I feel like I’m missing important information.
I’m wondering if anyone here has found a good way to monitor multiple AI agent platforms from one place. Maybe there’s a tool or service that can pull data from different sources and show it all in one dashboard?
I’d love to hear about any solutions you’ve tried or recommendations you might have. Even if you just have some tips on how to organize this better, that would be really helpful. Thanks in advance for any advice you can share!
Try webhook integrations if the platforms support them. Skip the constant API polling - webhooks push data to your system when events happen. Way more efficient and real-time. I built a simple webhook receiver that catches events from different AI platforms and dumps them into a time-series database. Then I use basic visualization tools for unified dashboards. You’re not hitting rate limits or wrestling with API auth constantly. Most platforms like OpenAI support webhooks for completions, errors, and billing updates. For platforms without webhooks, fall back to polling like others mentioned. One thing I wish I’d done sooner - standardize your data format from day one. Every platform sends data differently, so build a common schema before you start collecting. Makes comparing performance across platforms way easier later.
for sure! airtable’s great for keeping track of stuff. u can customize it to pull data from different platforms so it’s all in one spot. might save u a lot of headaches! have fun setting it up!
Had this same issue six months ago with agents running on Claude, OpenAI, and a few custom platforms. I built a basic monitoring script that pings each API every few hours and dumps everything into a shared spreadsheet. Super simple setup - just performance data, costs, and error rates in one place. Took me a weekend to build, but now I catch problems early. If you need something beefier, check out Grafana. It pulls from multiple sources and makes solid dashboards. Start simple though - don’t try to solve everything at once.
Custom scripts work, but they’re a pain to maintain at scale. I learned this the hard way with monitoring across different cloud providers.
Automated workflows changed everything for me. Instead of coding or updating spreadsheets manually, automation pulls metrics from each platform’s API and dumps everything into one dashboard.
You can set triggers for performance thresholds or cost limits. If your ChatGPT agent burns through tokens or your Langsmith agent breaks, you’ll know immediately instead of finding out during daily checks.
I automated reporting too. Every morning I get a consolidated report with key metrics, costs, and any issues from yesterday. Zero manual work once it’s running.
For these monitoring workflows, Latenode connects to all the APIs and processes data without coding. You can hook up multiple AI platforms, set up monitoring logic, and build dashboards in hours instead of weekends.
Been there. Managing agents across platforms becomes a nightmare fast, especially with different API rate limits and billing structures.
Centralized logging saved me. Every agent call, response, and error goes to one log aggregator. I use ELK stack but Logtail works too.
The real game changer? Health checks with proper alerting. Skip reactive monitoring - set up proactive alerts for response time drops, token usage spikes, or API failures. Slack notifications are perfect for this.
For costs, I pull billing data weekly from each platform and normalize the format. Catches trends before they get expensive.
One tip - don’t monitor everything at once. Start with uptime and costs, add performance metrics later. I built a complex system upfront and spent more time fixing monitoring than actual agents.
Want something ready? Datadog has decent AI monitoring now. Pricey but handles the heavy lifting.