Typical coding context
Source, build, test, text
- Firmware and application code
- Build output and test results
- Software logs and stack traces
Infrastructure for AI hardware development
Give AI agents access to the physical world.
A controlled interface for the logs, telemetry, vision, and commands that connect an AI development harness to real embedded systems, boards, and machines.
- LOGS
- Inspectable
- STATE
- Observable
- CONTROL
- Guarded
The gap
Code is only one side of the system.
Current coding agents can inspect repositories, modify firmware, run software tests, and reason over text logs. But a connected board, a moving actuator, or a controller fault is usually outside their field of view.
When the machine differs from the simulation, the development loop still depends on an engineer to collect state, interpret evidence, and trigger the next diagnostic step.
Missing physical context
Machine state, behavior, evidence
- Sensor values and controller state
- Board-level faults and timing symptoms
- What the hardware is actually doing
AI DEVELOPMENT HARNESS
PHYSICAL HARDWARE
The interface
From writing firmware to debugging the machine.
The tools expose the evidence and controls an AI development harness needs to participate in a physical engineering loop—without presenting the system as an unrestricted autonomous operator.
Integrate the inputs engineers already trust. Let the harness correlate logs with live state, camera observations, and bounded diagnostic commands.
Follow the development loopCapability surface
Evidence in. Reasoning. Controlled action.
Four interfaces designed to make physical-system state useful in an AI-assisted development workflow.
01
Embedded Log Analysis
Turn raw serial, boot, and diagnostic output into structured evidence an AI agent can reason about. Surface crashes, unexpected state transitions, timing anomalies, and recurring failures across boards and custom microcontroller hardware.
Example Correlate a watchdog reset with the code path that preceded it.
02
Robotics Telemetry
Make sensor readings, motor state, position, velocity, temperature, battery condition, error codes, and controller state available to the harness. Give the agent the same observable system context used to understand a running machine.
Example Diagnose an intermittent stop from encoder, current, and controller signals.
03
Real-Time Commands
Expose programmatic, policy-bounded operations for diagnostics and test automation: configure a device, trigger a test, start or stop an operation, move an actuator, or change an approved operating parameter.
Example Trigger a repeatable diagnostic sequence after an anomalous reading.
04
Physical Awareness
Use cameras and image-recognition interfaces to let the harness inspect the physical environment. Check component presence, observe a robot, identify state changes, and detect behavior that logs alone cannot explain.
Example Confirm that a mechanical action occurred before evaluating the next test step.
Development loop
Observe the run. Diagnose the evidence. Improve the next build.
An AI harness works within the engineering loop by receiving physical context at each point where a code-only workflow would otherwise stop.
- 01ModifyAI updates firmware or control code.
- 02DeployFirmware is flashed to a development board.
- 03ObserveLogs, telemetry, and camera evidence are collected.
- 04DiagnoseThe harness identifies inconsistencies and failure signals.
- 05TestA bounded command triggers the next diagnostic check.
- 06RepeatEvidence informs the next revision and test run.
Designed for integration
Meet the hardware where it is.
Designed to integrate with the hardware and development environments engineers already use—not to promise universal compatibility out of the box.
MicrocontrollersDevelopment boardsRobotics platformsCustom embedded hardwareCPU / GPU development systemsTest fixtures
Engineering organizations
Built for engineers who make the machines.
For teams responsible for the full path from silicon, board, or mechanism to production-grade software. The interface is intended to support disciplined evidence gathering—not replace engineering judgment.
Discuss your workflow- 01 Firmware development and embedded debugging
- 02 Board bring-up and hardware validation
- 03 Robotics development and controller tuning
- 04 Automated hardware and regression testing
- 05 Hardware/software integration
- 06 AI-assisted diagnostic workflows
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