Embedded flight software, line-replaceable units, and safety-critical hardware. Sagy's agent already understands the bus protocols, the partitioned RTOS world, and the DO-178C / DO-254 reality you work under, then it learns your team and your certification evidence.
Explore related pages: Hardware & Embedded Agent 路 Firmware Reproduction
Avionics work carries weight that most software never does. A reported anomaly on a flight-critical LRU means an engineer has to recover the exact configured build, recreate the lab setup with the right test rig and bus analyzers, reproduce a timing-sensitive fault, and trace it back through requirements, code, and test artifacts, all while keeping the traceability that certification demands. Most of that week is mechanical: context recovery, setup, reproduction, and documentation.
The work is slow, repetitive, and unforgiving of shortcuts. Sagy's avionics agent is built to absorb the operational load around reproduction and investigation, so your engineers spend their time on the judgment that actually requires a DER-level engineer, not on rebuilding the lab for the fourth time.
Out of the box, the avionics 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 secure lab and connects to the LRU through JTAG, serial, bus analyzers, or your existing integration rig, never the open internet unless you allow it.
Reads the problem report, recovers prior context from your messaging tool, version control, and issue tracker, identifies the exact configured build and hardware standard, sets up the rig, runs the reproduction steps, and retries across known failure patterns, then delivers a structured report with bus captures, logs, and a first hypothesis.
Connects the anomaly to the relevant code, the requirements it implements, and the verification artifacts that cover it, surfacing traceability gaps and the suspect commits without the engineer manually walking DOORS, the repo, and the test reports.
When a new build is ready, the agent runs a pre-configured suite of reproduction workflows against your library of historical anomalies, catching regressions in known failure modes before they reach formal verification.
Every investigation becomes part of Sagy's memory, with the full trace, captures, and resolution. The next engineer, or the next program, inherits the reasoning instead of rediscovering it years later.
Encoded as a Sagy Skill once, then run consistently: the way you power the unit, connect bus analyzers, load the build, and capture evidence.
The agent matches builds against your CM system and software/hardware standards, with your part numbers and release conventions.
How your team structures test procedures, captures results, and links them to requirements. The agent runs your procedure, not a generic one, and respects your traceability.
Every problem report, 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 platform.
The agent knows your repos, your branch and baseline conventions, and which commits or model changes to suspect when an anomaly appears.
If your engineers spend a meaningful chunk of their week on anomaly reproduction, lab setup, context recovery, or traceability work, this agent is built for you.
Years inside embedded, safety-critical engineering, watching the same operational drag play out: reproduction, lab setup, context recovery, and documentation eating the days that should go to engineering judgment. The avionics 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 bus protocols your team lives in.
We're working with a small number of design partners to push this further. If you build avionics for a living and the work described on this page sounds painfully familiar, we should talk, including about deployment that fits your security and ITAR constraints.
We're deliberately taking on a small number of teams to make sure each one gets a deeply tailored agent.
