Agent-Native Observability

Your data. Your cloud. Your AI. Our observability.

Observability was designed for a person sitting at a keyboard. It returns dashboards. It surfaces logs for a human to scroll through. It expects someone to correlate the signals, chase the traces, and figure out what the metrics actually mean. That person was always the bottleneck, and for years the industry tried to make them faster.

Then AI agents arrived. And suddenly the person is not the bottleneck anymore. The data layer is.

What happens when agents do the work

An AI agent investigating an incident does not query carefully. It queries everything it can reach, as fast as it can reach it. It pulls logs, traces, metrics, and related context simultaneously. It is not trying to be expensive. It is trying to be thorough. And the observability platforms built for careful human queries were not designed for this. They rate-limit. They slow down. They charge per query in ways that make autonomous investigation economically unviable.

There is a second problem that does not get talked about enough. If you solve this by letting a third-party platform run your AI agents for you, those agents need access to everything to work well. Your source code. Your business context. Your incident history. Your LLM prompts. All of it now lives, in some form, on someone else's infrastructure. That is a significant thing to agree to, and most teams do not realise they have agreed to it.

What agent-native actually means

Tsuga is built so that AI agents can use observability data effectively, affordably, and entirely within your own environment. The storage and query layer handles the data volumes agents actually generate. The APIs return relevant context rather than raw data dumps, so agents spend their tokens on reasoning rather than on filtering noise. And because everything runs in your cloud, your agents can connect to every data source in your environment, not just the ones a third-party platform has chosen to integrate with.

This is not just a future-state pitch. It matters right now, even for teams whose agents are still doing most of their work. The architecture that handles agent-scale queries also makes it economically viable to monitor things you currently do not. Staging environments. Development. The full data trail of AI decisions in production. Every environment you currently leave dark because the cost does not justify it.

The three things that make it work

Agent-first APIs. MCPs, CLIs, and query interfaces designed to return the right context, not everything. Agents stay precise, costs stay manageable, investigations stay fast.

AI-scale economics. A query engine and storage layer built for ten times the data volume at a fraction of what traditional platforms charge. When agents generate more data, your bill does not multiply with it.

Your agents, your cloud. Deploy your own agents. Connect them to every system in your environment. Keep your data, your prompts, and your IP inside your perimeter.

Who this is for

Engineering and platform teams who are putting AI agents into production and need the data layer to keep pace. Organisations who understand that the next decade of software reliability runs through the quality of the observability infrastructure their agents operate on, and who are not willing to hand that infrastructure to someone else.