Embedded firmware, instrument software, and software as a medical device. Sagy's agent already understands IEC 62304, the safety standards, the clinical interfaces, and the failure modes you investigate, then it learns your QMS, your risk file, and your reproduction workflow.
Explore related pages: Hardware & Embedded Agent 路 Security & Deployment
Medical device engineering carries a regulatory burden that ordinary software never sees. A complaint or field issue means an engineer has to recover the exact released build, recreate the instrument or bench setup, reproduce a condition that may be intermittent and patient-specific, and tie it back through requirements, risk controls, and verification, while keeping the traceability your QMS and auditors expect. Most of that time is mechanical: context recovery, setup, reproduction, and documentation.
The work is slow, repetitive, and unforgiving of gaps. Sagy's medical device agent is built to absorb the operational load around reproduction and investigation, so your engineers spend their time on the clinical and engineering judgment that matters, not on rebuilding the bench and re-deriving context.
Out of the box, the medical device agent is preloaded with deep domain knowledge that generic AI tools simply do not have.
The agent runs on the engineer's workstation in your environment and connects to the instrument through serial, USB, SSH, or your existing test setup, with PHI and IP staying inside your boundary.
Reads the complaint or CAPA-linked report, recovers prior context from your messaging tool, version control, and issue tracker, identifies the exact released build, sets up the bench, runs the reproduction steps, and retries across known failure patterns, then delivers a structured report with logs and a first hypothesis.
Connects the anomaly to the relevant code, the requirements it implements, and the risk controls and verification that cover it, surfacing traceability gaps and suspect commits without the engineer manually walking the DHF, the repo, and the risk file.
When a new build is ready, the agent runs a pre-configured suite of reproduction workflows against your library of historical issues, catching regressions in known failure modes before they reach formal verification.
Every investigation becomes part of Sagy's memory, with the full trace, logs, and resolution, supporting consistent documentation and giving the next engineer the reasoning instead of a cold start.
Encoded as a Sagy Skill once, then run consistently: how you power the instrument, load the build, apply test inputs, and capture evidence.
The agent matches builds against your CM system and device records, with your part numbers and release conventions.
How your team structures test protocols, links them to requirements and risk controls, and documents results. The agent follows your procedure and respects your traceability.
Every complaint, investigation, and engineering discussion becomes part of the agent's memory. The more your team uses Sagy, the faster it recognizes patterns specific to your device.
The agent knows your repos, your branch and baseline conventions, and which commits to suspect when an issue appears.
If your engineers spend a meaningful chunk of their week on complaint reproduction, bench setup, context recovery, or traceability and documentation work, this agent is built for you.
Years inside embedded, regulated engineering, watching the same operational drag play out: reproduction, bench setup, context recovery, and documentation eating the days that should go to engineering judgment. The medical device agent isn't a generic AI tool with a marketing skin, it's a domain-specific agent shaped by real work, preloaded with the standards and interfaces your team lives in.
We're working with a small number of design partners to push this further. If you build medical devices for a living and the work described on this page sounds painfully familiar, we should talk, including about deployment that fits your privacy and compliance requirements.
We're deliberately taking on a small number of teams to make sure each one gets a deeply tailored agent.
