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

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