🏆Finalist, Belgium Startup Awards 2026·Backed by Start it @KBC Accelerator
🏆Finalist, Belgium Startup Awards 2026·Backed by Start it @KBC Accelerator
🏆Finalist, Belgium Startup Awards 2026·Backed by Start it @KBC Accelerator
🏆Finalist, Belgium Startup Awards 2026·Backed by Start it @KBC Accelerator
Sagy

Platform

Deployable AI agents that learn from your team's work.

Sagy agents run your team's repeatable workflows inside the tools you already use, and get better with every task your people review and complete.

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HomeSee how Sagy agents learn from your team and take on repeatable work.Sagy in ActionSee agents running real workflows across different teams.Tool IntegrationsConnect the tools and systems your team already works in.Security & DeploymentPrivate deployment, human approval, auditability, and access control.

Use Cases

Start with the work, not the label.

Start with a hands-on engagement if needed, then deploy agents for finance, support, operations, people, or engineering workflows once the right opportunities are clear.

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Services First

Hands-on Workflow MappingWork through one of your real workflows with Sagy first, then decide which parts should become agents.

Business Teams

Finance & Back OfficeReconciliations, document reviews, approvals, and month-end preparation.Customer Support & SalesExample walkthrough of a customer request handled end to end.People & OnboardingGive every new hire an AI companion that answers questions and guides training.OperationsSOP-driven operational work, including production-line support.

Engineering & Technical

Incident Investigation AgentTurn inbound issues into structured, evidence-backed investigations.Firmware ReproductionReduce setup time before embedded debugging starts.Hardware Investigation AgentDevice, lab, and embedded investigation workflows.Engineering MemoryPreserve decisions, fixes, and investigation paths automatically.

Regulated Industries

TelecommunicationsNetworking and connected-device operations.Avionics & AerospaceSafety-critical and certification-heavy workflows.Medical DevicesRegulated investigation and documentation workflows.

Learn

Guides on AI agents and team workflows.

Practical articles on purpose-built agents, keeping team knowledge alive, and running repeatable workflows with AI.

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Blog IndexRead practical articles on AI agents, workflows, and team knowledge.Purpose-Built AgentsUnderstand why focused agents outperform generic assistants.Static Knowledge BasesSee why static docs miss the decisions teams actually need.Preserving Team KnowledgeSee how AI agents keep fixes, evidence, and decisions reusable.Root-Cause WorkflowFollow a source-backed workflow for getting to the real cause of an issue.Customer Issue WorkflowTurn customer reports into structured investigations.

Company

Company, hiring, and policy pages.

Learn who is building Sagy, how we handle data, and where we are hiring.

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TeamMeet the team building Sagy.CareersHelp build AI agents that learn from how teams work.PrivacyUnderstand how Sagy handles customer information and product data.
Sagy hardware investigation dashboard connected to an X2000 device
Hardware investigation

Hardware Investigation Agent for embedded and firmware teams

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.

Schedule a hardware demoReview example workflow

Related reading: why embedded bug reproduction takes so long. Sagy also supports software incident investigation and wireless device workflows.

Every investigation makes the next investigation smarter.

Recover context
Prepare the lab
Execute on device
Improve workflows
Hardware investigation fit

Use this page when device issues take too long to reproduce.

Hardware is a focused entry point into Sagy, not the whole product. The same investigation layer also supports software incidents, wireless failures, firmware regressions, and engineering memory.

Schedule a hardware demoSee the broader incident layer

Customer-reported device bugs

A field issue arrives with partial context, unclear reproduction steps, and pressure for a fast engineering answer.

Firmware and lab setup work

Engineers need the right firmware, device state, lab procedure, serial or SSH access, and logs before debugging can start.

Knowledge trapped in past incidents

The fix may already exist across old tickets, Slack threads, pull requests, docs, or a senior engineer's memory.

What to bring to the demo

  • A real or anonymized customer-reported device issue
  • The tools involved: Jira, GitHub, Slack, docs, logs, firmware storage, serial, SSH, or lab scripts
  • The current reproduction workflow your engineers repeat manually
  • The evidence you need before an engineer trusts the next action
Real engineering workflows

Operational context plus execution.

Unlike generic AI assistants, Sagy agents execute workflows grounded in your company's real engineering environment.

Jira tickets

Find similar issues, linked incidents, status changes, and prior debugging paths.

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.

Sagy investigation dashboard connected to a hardware device

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

Ready for review
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.

Schedule a hardware demo

Ready to test Sagy?

Schedule a demo for one of your high-value workflows.

Bring a real workflow from any team: finance, operations, support, HR, sales, or engineering. We can start by working through it with you as a service, then turn what works into agents that learn from your team.

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sagy

Deployable AI agents that learn from your team's work.

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