Jira tickets
Find similar issues, linked incidents, status changes, and prior debugging paths.
Hardware investigation
Hardware Investigation Agent
AI agents that investigate hardware and embedded system issues automatically by connecting to your codebase, tickets, documentation, firmware, and real devices.
The agent reads the issue, recovers historical context, flashes firmware, connects through serial, reproduces the problem, and generates an investigation report before an engineer starts debugging.
Every investigation makes the next investigation smarter.
Recover context
Prepare the lab
Execute on device
Improve workflows
Real engineering workflows
Operational context plus execution.
Unlike generic AI assistants, Sagy agents execute workflows grounded in your company's real engineering environment.
GitHub repositories
Check firmware fixes, commits, pull requests, and related code paths.
Confluence documentation
Recover device manuals, troubleshooting notes, and internal procedures.
Slack discussions
Use past engineering conversations without asking the same experts again.
Firmware images
Download, compare, and flash the firmware version required for the test.
Serial-connected devices
Open real device sessions and execute repeatable debugging commands.
SSH sessions
Connect to embedded Linux devices, lab machines, and test environments.
Internal procedures
Run the investigation workflow your engineers already trust.
Example workflow
From ticket to investigation report.
The agent follows a repeatable engineering workflow, then updates that workflow when the issue is resolved.
Step 01
Ticket created
A new issue lands in the queue and the agent starts the investigation workflow automatically.
"Device X2000 freezes after boot when connected to sandy terrain sensors."
- Reads the issue description
- Extracts device model, symptoms, environment, and constraints
- Selects the right workflow for this class of hardware issue
Step 02
Recover historical context
Sagy searches company memory before anyone opens a debugging tab.
- Similar Jira tickets and historical reports
- Previous firmware fixes and related merge requests
- Slack debugging threads, device documentation, and recovery commands
- Known unstable firmware versions and hardware limitations
Step 03
Prepare the investigation environment
The agent collects the pieces needed to reproduce the problem instead of waiting on manual setup.
- Downloads the correct firmware image
- Prepares the test environment
- Retrieves reproduction steps
- Identifies the required debugging tools
Step 04
Connect to the device
Sagy connects to the real device or remote embedded environment and opens the right control surface.
- Serial connection
- SSH session
- Remote embedded console
- Robotics control interface or IoT device shell
Step 05
Execute the investigation workflow
The agent runs debugging steps while correlating live observations with historical company knowledge.
- Flash firmware and restart services
- Collect logs and execute Linux commands
- Run diagnostic scripts and monitor device behavior
- Validate reproduction conditions
Step 06
Generate the investigation report
Engineers receive the context, evidence, and hypotheses they need before starting deeper debugging.
- Reproduction status and relevant logs
- Possible root causes and technical hypotheses
- Related historical incidents and links to previous fixes
- Suggested next actions
Step 07
Update the workflow automatically
When the ticket is resolved, Sagy extracts what worked and improves the next investigation.
- Adds successful procedures to reusable workflows
- Captures important commands and recovery strategies
- Improves investigation patterns from resolved issues
- Makes the next similar investigation faster
Investigation report
Engineers start with context already prepared.
Sagy turns device observations, internal knowledge, and workflow execution into an actionable report your team can review.
Investigation output
Device X2000 - sandy terrain sensor freeze
Reproduction status
Relevant logs
Possible root causes
Related historical incidents
Suggested next actions
Links to previous fixes
Technical hypotheses
Why teams use it
Debug complex systems with reusable memory.
Reduce time lost searching for context
Engineers stop digging through Slack, Jira, GitHub, and documentation before debugging can begin.
Reduce interruptions to senior engineers
Critical operational knowledge is captured from real work and reused continuously.
Accelerate hardware and embedded debugging
Sagy can interact with real devices, operational systems, and embedded workflows.
Improve with every execution
Resolved investigations become reusable procedures that the agent can execute again.
Built for engineering teams
Especially useful when software meets real hardware.
Sagy transforms engineering workflows into reusable operational memory that AI agents can execute and improve.
Embedded systems teamsRobotics companiesIoT platformsFirmware engineeringDevice operations teamsInfrastructure and platform engineeringHardware/software integration teams
Operational memory layer
Not another chatbot. An execution layer for AI agents inside engineering organizations.
Turn hardware investigation procedures into workflows that can run, learn, and improve with every resolved issue.