🏆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

AI workflow agents that execute and learn.

Sagy helps teams map workflows, execute them with AI agents, and keep proven paths available for the next support issue, engineering investigation, or operational problem.

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HomeSee how Sagy helps teams execute and improve complex workflows.Incident Investigation AgentGather evidence across tools and surface the next action faster.Engineering MemoryPreserve decisions, fixes, and investigation paths automatically.Sagy in ActionFind the Sagy page that matches your team’s use case.

Use Cases

Start with the use case, not the label.

Start with a service investigation if needed, then move into sales and support, embedded systems, or production workflows once the right agent opportunities are clear.

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Services First

Investigation ServicesWork with Sagy to investigate your current issues first, then decide which workflows should become agents.

Sales & Support

Incident Investigation AgentExample use case for turning inbound issues into structured investigations.Investigator DemoExample walkthrough of a support or escalation workflow.

Embedded Systems & Devices

Firmware ReproductionExample use case for reducing setup time before embedded debugging starts.Hardware Investigation AgentExample use case for device, lab, and embedded investigation workflows.Wireless Networking AgentExample use case for networking devices and field debugging workflows.

Production & Regulated Operations

Production Line Support AgentSimple use case for troubleshooting production lines with access to procedures, support context, and prior incidents.TelecommunicationsExample use case for production networking and connected-device operations.Avionics & AerospaceExample use case for safety-critical embedded and certification-heavy workflows.Medical DevicesExample use case for regulated device investigation and documentation workflows.

Workflow Foundations

Tool IntegrationsSee the tool layer that supports Sagy use cases.Confluence AlternativeExample of how Sagy can keep workflow knowledge alive without stale wiki pages.

Learn

Practical guides for engineering investigation.

Read focused content on MTTR, root-cause workflows, customer bugs, embedded reproduction, and secure AI agents for engineering teams.

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Security & DeploymentReview private deployment, human approval, auditability, and access control.Blog IndexRead practical articles for engineering teams investigating complex issues.Reduce MTTRLearn how repeatable incident investigation lowers resolution time.Root-Cause WorkflowFollow a source-backed workflow for engineering root-cause analysis.Slack Jira GitHub IncidentsConnect conversations, tickets, and code changes during incidents.Customer Bug WorkflowTurn customer reports into structured engineering investigations.Incident KnowledgeSee how AI agents preserve fixes, evidence, and decisions.Embedded Bug ReproductionLearn why reproducing customer bugs can take days before debugging begins.Static Knowledge BasesSee why static docs miss the decisions engineers need during incidents.Purpose-Built AgentsUnderstand why focused agents outperform generic assistants for engineering work.

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 for engineering organizations.CareersExplore opportunities to help build the AI investigation layer.PrivacyUnderstand how Sagy handles customer information and product data.
The investigator agent

Investigate any issue, from anywhere.

This use case shows how with Sagy, we can create an agent that runs a complete investigation workflow: understanding the issue, gathering context, checking relevant code and database records, and surfacing existing tickets.

Inputs

SlackEmailCall transcript

Tools touched

JiraGitHubDatabase
What it does

Sagy turns inbound issue reports into structured investigations. The agent reads the report, pulls context from your tools in parallel, and surfaces the root cause and next action. Before anyone opens a tab.

Use a configurable Sagy agent to investigate a customer issue from Slack (or email, or a call), determine whether it is part of a known incident, and surface the appropriate next action. The goal is to reduce manual investigation time by using the agent's workflow, connected tools, and feedback loop.

  • Works from any inbound channel: Slack, email, or call transcripts
  • Cross-checks Jira, GitHub, and your database in parallel
  • Tells you if it's user-specific or part of a known incident
  • Workflow stays auditable. Every step and source is logged.
The workflow

From inbound message to root cause.

Six steps, fully auditable. Each step links to the moment in the walkthrough video.

  1. 1

    Receive and review the customer issue

    Watch at 0:34
    • Open the Slack message (or forwarded email / call transcript) from the customer.
    • Read the issue carefully and identify the core symptom (for example: "cannot log in").
    • Confirm the request is suitable for automated investigation before proceeding.
    • Capture any key identifiers mentioned, such as the user email or account ID.
  2. 2

    Trigger the Sagy agent to begin investigation

    Watch at 1:20
    • In the Slack thread, invoke the Sagy agent using the configured command or prompt.
    • Provide a clear instruction such as: Please investigate.
    • Include the relevant context from the issue so the agent starts with the correct request.
    • Submit the request and wait for the agent to begin processing.
  3. 3

    Ensure the agent has the right workflow and tool access

    Watch at 1:51
    • Verify that the Sagy agent is configured to execute the required workflow for this type of issue.
    • Confirm the agent has access to the necessary knowledge sources and tools.
    • For an investigation, ensure access to Jira tickets, GitHub code, and database records.
    • Make sure the workflow instructions are aligned with the investigation process.
  4. 4

    Let the agent analyze the issue and identify the root cause

    Watch at 3:21
    • Review the agent's response once it completes the investigation.
    • Check whether it identifies the issue as a user-specific problem or a broader platform incident.
    • Use the agent's conclusion to determine the next action.
    • If the agent finds an existing incident, confirm the incident details and status.
  5. 5

    Validate the workflow execution and tools used

    Watch at 4:08
    • Open the workflow details in the Sagy platform.
    • Review the request, the agent used, and the output workflow.
    • Confirm which tools were used during the investigation: Database, Jira, GitHub.
    • Review the workflow description and instructions to ensure they match the intended process.
  6. 6

    Review performance and update the workflow with feedback

    Watch at 4:34
    • Compare the time spent by the AI agent versus the manual process.
    • Record the time saved for reporting or process improvement.
    • Provide feedback on the agent's output if adjustments are needed.
    • Confirm that the workflow updates automatically after feedback is submitted.

Cautionary notes

What to watch for

  • Do not assume every issue is user-specific. Verify whether it is part of a broader incident.
  • Ensure the agent only has access to approved tools and data sources.
  • Review the agent's conclusion before taking customer-facing action.
  • If the workflow is misconfigured, the agent may investigate the wrong systems or miss important context.

Tips for efficiency

Get the most out of it

  • Include the user email or other identifiers in the initial request to speed up investigation.
  • Keep workflow instructions concise and specific so the agent can follow them consistently.
  • Use the agent for common, repeatable issues where the investigation path is predictable.
  • Collect feedback after each run to improve future executions automatically.

Ready to test Sagy?

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

Bring a real support issue, engineering investigation, or production problem. We can start by helping investigate it as a service, then map the workflow and turn what works into reusable operational knowledge and agents.

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sagy

AI workflow agents for teams running complex support, engineering, and operations work.

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