Inference Agent Observability Platform — Tracing requests, latency, cost and quality for LLM agents

0:00Welcome back everyone. So like we do every time, we are currently looking at a page on the AI agent store at AI website. Always a good place to start. Yeah. And today our mission is to wrap our heads around something called the inference agent observability platform. This is a really interesting one actually. It is because if you think about it, building AI right now is kind of like building this incredibly fast high speed train, but you just blindfold the conductor before they leave the station. Right. You just send them off and hope for the best. Exactly. You can engineer this incredibly fast LLM engine, but if you don't have a control panel showing your speed, your track conditions, your fuel costs, you're flying completely blind. Yeah. And this platform is basically that missing control panel. Okay. So building on that analogy, what specific dials and gauges are we actually looking at here? Well, this platform, which is from inference.net, is designed to trace every single step at AI takes in production. So monitors LLM calls, specific tool invocations,

1:01prompts, and even downstream provider behavior. Oh, wow. So it's a full X-ray of the system. Yeah. Exactly. It tracks latency, reliability, usage patterns, cost, and quality signals. You can even like continuously benchmark models against live production traces. Yeah. Let me stop you there though, because I'm looking at the page and it lists this with an autonomy level of 33%. Right. So if it's technically an agent platform, why is its autonomy so low? Is it just passively watching? Well, that's the thing. It's also an observability and improvement layer, right? It's not a fully autonomous agent all in its own. Oh, okay. That makes more sense. Yeah. It primarily watches the AI and captures these complex multi-step traces, but it isn't totally passive. It uses an open source tool called Halo. Halo, like the game. No, it's a tool for agent optimization. So it actually helps improve the system based on what it observes. Okay. So since it is this observation layer and not an independent worker, who is this actually for? Is this overkill for someone just

2:02tinkering on their laptop? Oh, absolutely. Yeah. It might be way too heavy for lightweight local debugging. So no weekend projects. Definitely not. It's a closed source paid infrastructure tool, built for AI and native engineering teams, ML engineers, MLOPS, founders. People running real production apps. Right. Teams who want observability integrated directly with model deployment, which boasts a 99.99% uptime, by the way, and custom model training workflows, which brings up the reality of running at that scale, right? The cost. I see the model instances cost $9.98 per hour. Yes, but that's just for these instances themselves. Wait, really? So what about the actual observability dashboard? Well, for the specific observability pricing, buyers actually have to contact the company directly. Oh, so it's one of those call us to find out situations precisely. And that actually leaves us with something I think is really fascinating to mull over. Lay it on me. As these AI systems get infinitely more complex, you know, doing all these non deterministic tasks,

3:05could the infrastructure required to observe and correct the AI eventually become more complicated than the AI itself? Oh, wow. Like we need a massive AI just to understand our AI's control panel. Exactly. It's a wild thought. It really is. Well, if you want to check out the platform we've been looking at today, head over to a iagentstore.ai. Thank you so much for taking the time to rate the podcast. We really appreciate it. And we will catch you on the next one.