Your team caught an anomaly at 2 AM. By morning, you need a hypothesis with evidence. Not a hunch, not a guess, but a traceable chain from data to conclusion that your chief engineer can review and your compliance team can sign off on.

That investigation currently takes days. Sift Agents compress it into minutes.

Why hardware needs purpose-built agents

General-purpose AI agents are useful for many things. Hardware telemetry analysis is not one of them. The reason is straightforward: the agent needs to understand your data model to produce results you can trust.

When you're debugging an anomaly in a spacecraft propulsion system, the agent needs to know which channels are related to each other, what failure modes look like, when to filter out blips in noisy hardware data, and how to distinguish a real correlation from a coincidence in a high-cardinality time-series dataset. A general-purpose model has none of that context by default.

Sift Agents are purpose-built for hardware. They come with curated skills and tools that understand hardware data structures, sensor relationships, and domain-specific analysis patterns. The result: analysis that's grounded in your actual data, not a best-effort interpretation from a model trained on something else.

Three use cases, one governed platform

Root cause analysis. Give an agent a prompt describing an anomaly. It performs thorough analysis across your telemetry, identifies correlations, runs parallel investigations across multiple hypotheses simultaneously, and surfaces candidates with supporting evidence. Engineers review; the agent does the investigative groundwork. A week of data work becomes a structured analysis in minutes.

Data review and compliance. Regulated industries have compliance workflows that are time-consuming by design – because they matter. Agents automate the validation work using your custom skills, generate evidence as Sift Artifacts, and produce documentation that meets your compliance requirements. The workflow is auditable end-to-end, which is the only way it's useful in a safety-critical context.

Exploration and visualization. Generating plots and visualizations used to require a series of manual computations and chart configurations. At best, you could have clunky and brittle scripts that generate static plots and break anytime a single parameter changes. Sift Agents generate flexible visualizations in Explore from a quick description of what you're investigating. The analysis stays in the platform; the configuration work goes away.

The governance model is built in

If you're building for a regulated industry, the governance model isn't a nice-to-have. It's the reason you'd trust any of this.

Sift Agents operate under a single governance boundary. All data and agentic analysis is governed under your control, with access-controlled permissions. Write operations require human approval. The agent can read your data, analyze it, correlate channels, surface hypotheses, and generate artifacts – but it cannot commit changes without explicit sign-off from a human on your team.

The analysis is traceable. When an agent surfaces a root cause candidate, every step links back to the underlying data. You're not getting a black-box output with a confidence score. You're getting a structured analysis you can inspect, verify, and defend.

The compounding problem it solves

Hardware systems are getting more complex, but the number of engineers who can do deep data analysis isn't scaling at the same rate. Your team is already stretched. The amount of data coming off a modern test vehicle or production spacecraft is growing faster than anyone's ability to manually review it.

That gap widens every quarter. Sift Agents are how you close it – not by replacing engineering judgment, but by compressing the investigative work that sits in front of that judgment.

One prompt to kick off a root cause analysis instead of a week of manual correlation-pulling. Automated data review instead of engineering hours on validation. Explorations that start with a sentence instead of an hour of panel configuration.

Elevate your team

If your team is spending hours investigating anomalies, validating data, or manually generating visualizations, it’s time to hand off some of that work to Sift Agents.