Build an ops and support copilot

~25 min TypeScript Python

What you’re building: Triage tickets and run runbooks, with human approval.

Primitives you’ll use: Sandbox, Agent and harness, Deployment (App Engine)

Agent prompt

Start with your coding agent

Choose what you are building. The brief names the product it is modelled on, maps it onto MIOSA, and lists the exact commands. Copy it into OSA, Claude Code, Codex, or Cursor.

Template coming soon

1 What are you building?

Leave it empty and your agent will propose 3 names, pick one, and use it for resources, the domain and branding.

3 Configure

Harness
Model
Scale
Extras

4 Your prompt

Reference pattern

Ops and support copilot, by Your company (/docs/guides/internal-business-os): an internal copilot that reads the queue, runs runbook checks and drafts replies, with a human approving.

How it works: It reads tickets or alerts, follows a runbook inside a sandbox, drafts the response, and waits for a person to approve before anything changes.

Key capabilities:

Reads the support or on-call queue

Runs runbook checks

Drafts replies and actions

Stops for human approval

This is a pattern, not a copy of one product. Open /docs/guides/internal-business-os, then design the smallest version that does the capabilities above.

How it maps onto MIOSA

Runbook checks in isolation -> a MIOSA sandbox with read-only credentials

Human approval -> agent approvals (`miosa agent approvals`)

Case history -> managed Postgres (`DATABASE_URL`)

Audit -> `miosa audit`

Goal

Build Triage tickets and run runbooks, with human approval. on MIOSA, as a multi-tenant product sold to my customers. Each customer is isolated in its own workspace; I meter usage and bill them.

Product name: propose 3 product names, pick one, and use it for resource names, the domain and branding. Until you pick, <product-name> stands for it in the commands below.

Scale: a prototype.

Set up

npm i -g @miosa/cli

miosa login && miosa whoami

miosa api-key create <product-name>-key --preset agent

export MIOSA_API_KEY="msk_u_..."

miosa org

miosa connections add models # your own model provider key

Resources

Sandbox: the agent's isolated Linux workspace

miosa create <product-name>-box --template agent-node --wait

Agents and harnesses: what a run can use

miosa agent harnesses

Deployment: a stable, versioned URL

miosa deploy create --from-sandbox <product-name>-box --name <product-name> --wait

Data and storage

Keep application data in managed Postgres with a row-level owner (member id or team), and keep an append-only audit table for agent actions.

Auth and tenancy

Your organization is the platform; customers never get a MIOSA account, they sign into YOUR product. One workspace per customer, created as they onboard, isolates their machines, runs and data.

miosa workspace create <product-name>-customer-1

Tag every machine and run with the customer id in `metadata`, and never query across customers. Meter usage per customer with GET /api/v1/usage and set who pays with PUT /api/v1/bill-to (/docs/platform/usage-and-billing).

Agent loop

Harness: OSA is MIOSA's own harness and works with any model provider you connect, including your own model.

Model: Anthropic (Claude). Calls use my own provider key (`miosa connections add models`); MIOSA platform keys are never used.

Sessions: one chat per project or conversation, so the agent keeps its context. The first `miosa prompt` on a sandbox uses `--new-chat` (a chat id is printed); every later turn passes `--chat <chat-id>`. `--reuse chat` keeps one new machine per chat so files persist.

Streaming: follow a run with `miosa run follow <run-id>` or client.runs.streamEvents(run.id), and steer or stop it with `miosa run steer` and `miosa run interrupt`.

Agents act as members: every run carries the asking member's id in `metadata`, uses credentials scoped to that member or team, and cannot exceed what the member may do.

Steps

1. Connect the queue (tickets or alerts) with read-only credentials, stored with the Secrets API, not in the sandbox `env`.

Check: The agent can read the queue and cannot write to it.

2. Encode each runbook as steps the agent follows inside a sandbox (checks only, no mutation).

miosa create <product-name>-box --template agent-python --wait

Check: A runbook run produces findings and no side effects.

3. Draft the reply or action, then STOP for approval before anything is sent or changed.

miosa prompt --sandbox <product-name>-box --harness osa --model <anthropic-model-id> --chat <chat-id> "Follow runbook <name> for ticket <id>; draft the reply; do not send"

miosa agent approvals

Check: Nothing leaves the building without an approval record.

4. Control access: members and roles, and an audit trail of what agents did.

miosa member add dana@<product-name>.com --role member

miosa audit

Check: A removed member loses access immediately.

5. Publish the internal UI.

miosa deploy create --from-sandbox <product-name>-box --name <product-name> --dir /workspace --port 3000 --run-command "npm start" --wait

Check: Members can open it and nobody else can.

Limits and costs

Connectors are the real work: budget time for each source system's auth, rate limits and data shape.

Set `idle_timeout_sec` so unused team sandboxes pause.

Prefer read-only access by default; grant writes per task.

Prototype: keep it to one machine at the default size, skip replicas and custom hostnames you do not need, and delete everything when you are done.

Acceptance checks

Members see only their team's data and tools.

Every agent action is attributable to a member in the audit log.

Nothing consequential happens without the approval the guide describes.

Each customer is isolated in its own workspace and usage is metered against them.

Everything it created can be deleted with nothing left running.

What your choices added

  • Build a product. a workspace per customer, per-customer metering and bill-to
  • Agent suggests a name. proposes 3 product names and picks one; commands use <product-name>
  • Harness: OSA. dispatches with `miosa prompt --harness osa`
  • Model: Anthropic. your own provider key
  • Prototype. one small machine, no extras, easy to delete

What you're building

an internal copilot that reads the queue, runs runbook checks and drafts replies, with a human approving

It reads tickets or alerts, follows a runbook inside a sandbox, drafts the response, and waits for a person to approve before anything changes.

Primitives you'll use: Sandbox · Agent and harness · Deployment (App Engine)

  • Reads the support or on-call queue
  • Runs runbook checks
  • Drafts replies and actions
  • Stops for human approval

This is a pattern, not a copy of one product. Start from the related MIOSA guide and build the smallest version that does the capabilities above.

What you need on MIOSA

Each row is one thing to create before you start. The number matches the step that uses it.

  1. Organization and API key Scopes every call; a workspace key is all a worker needs. miosa api-key create app-key --preset agent Docs
  2. Sandbox The isolated Linux workspace the agent writes code and runs commands in. miosa create app-box --template nextjs --wait Docs
  3. A workspace per customer Isolates each customer’s machines, deployments, and data as they onboard. miosa workspace create customer-1 Docs
  4. Branding and white-label Your name and slug on previews, deployments, and the desktop; customers never see MIOSA. miosa org Docs
  5. Usage metering and bill-to Usage per customer, and which account pays for new machines. miosa org bill Docs
  6. Agent and harness Turns a prompt into work: pick the harness and model a run uses. miosa agent harnesses Docs
  7. Deployment (App Engine) Publishes the app to an immutable, versioned URL with rollback. miosa deploy create --from-sandbox app-box --name app --wait Docs

Architecture

How it maps onto MIOSA

Runbook checks in isolation -> a MIOSA sandbox with read-only credentials

Human approval -> agent approvals (`miosa agent approvals`)

Case history -> managed Postgres (`DATABASE_URL`)

Audit -> `miosa audit`

Data and storage

Keep application data in managed Postgres with a row-level owner (member id or team), and keep an append-only audit table for agent actions.

Auth and tenancy

Your organization is the platform; customers never get a MIOSA account, they sign into YOUR product. One workspace per customer, created as they onboard, isolates their machines, runs and data.

miosa workspace create ops-support-copilot-customer-1

Tag every machine and run with the customer id in `metadata`, and never query across customers. Meter usage per customer with GET /api/v1/usage and set who pays with PUT /api/v1/bill-to (/docs/platform/usage-and-billing).

Agent loop

Harness: OSA is MIOSA's own harness and works with any model provider you connect, including your own model.

Model: Anthropic (Claude). Calls use my own provider key (`miosa connections add models`); MIOSA platform keys are never used.

Sessions: one chat per project or conversation, so the agent keeps its context. The first `miosa prompt` on a sandbox uses `--new-chat` (a chat id is printed); every later turn passes `--chat <chat-id>`. `--reuse chat` keeps one new machine per chat so files persist.

Streaming: follow a run with `miosa run follow <run-id>` or client.runs.streamEvents(run.id), and steer or stop it with `miosa run steer` and `miosa run interrupt`.

Agents act as members: every run carries the asking member's id in `metadata`, uses credentials scoped to that member or team, and cannot exceed what the member may do.

Steps

  1. Connect the queue (tickets or alerts) with read-only credentials, stored with the Secrets API, not in the sandbox `env`.

    Check: The agent can read the queue and cannot write to it.

  2. Encode each runbook as steps the agent follows inside a sandbox (checks only, no mutation).

    miosa create ops-support-copilot-box --template agent-python --wait

    Check: A runbook run produces findings and no side effects.

  3. Draft the reply or action, then STOP for approval before anything is sent or changed.

    miosa prompt --sandbox ops-support-copilot-box --harness osa --model <anthropic-model-id> --chat <chat-id> "Follow runbook <name> for ticket <id>; draft the reply; do not send"
    miosa agent approvals

    Check: Nothing leaves the building without an approval record.

  4. Control access: members and roles, and an audit trail of what agents did.

    miosa member add dana@ops-support-copilot.com --role member
    miosa audit

    Check: A removed member loses access immediately.

  5. Publish the internal UI.

    miosa deploy create --from-sandbox ops-support-copilot-box --name ops-support-copilot --dir /workspace --port 3000 --run-command "npm start" --wait

    Check: Members can open it and nobody else can.

Limits and costs

Connectors are the real work: budget time for each source system's auth, rate limits and data shape.

Set `idle_timeout_sec` so unused team sandboxes pause.

Prefer read-only access by default; grant writes per task.

Prototype: keep it to one machine at the default size, skip replicas and custom hostnames you do not need, and delete everything when you are done.

Acceptance checks

Members see only their team's data and tools.

Every agent action is attributable to a member in the audit log.

Nothing consequential happens without the approval the guide describes.

Each customer is isolated in its own workspace and usage is metered against them.

Everything it created can be deleted with nothing left running.

Next steps

Was this page helpful?