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Agent-neutral delivery for Linear

Issue in. Reviewed PR out.

Any coding agent builds it. A second agent from another provider reviews it. Every run leaves a receipt, and the whole control plane runs in your cluster or ours.

Book a 25-min pilot introExplore the workflow

25 minutes · live workspace · a straight answer on fit

Delivery workflowworkflow example
LinearExample issue

Add rate limiting to the public API

bounded autonomy
Implementer
#opus
Independent reviewer
#gpt
Execution
Isolated Kubernetes job

Delivery graph

issue → merged PR
  1. Implement, assigned to Opus. The implementation_ready outcome moves the run to Review.
  2. Review, assigned to GPT.
    • The review_approved outcome moves the run to Release.
    • The review_changes_requested outcome moves the run to Fix review. When Fix review succeeds, fix_review_ready returns the run to Review. The loop is limited to three review attempts before a human takes over.
  3. Release. The release_merged outcome completes the run.
  4. PR merged.

04/06#gpt reviews the fixed PR.

Runs the agents you already pay for, to one delivery standard.

  • Claude Code
  • Codex
  • Copilot
  • OpenCode
  • Gemini
Chapter 1 of 8: One issue. One governed outcome.

A workflow that finishes what your agents start.

Every outcome chooses the next step. Three rejected reviews, then a person, never an unlimited retry.

  1. Step 01

    Assign or label an issue.

    Assign the agent, comment /implement, or label it Automatable. The graph picks the agent.

    Add rate limiting to the public API
    Automatable/implement #opus
  2. Step 02

    Review, fix, review again.

    A second agent from another provider reviews. Three rejected reviews, then a person.

    Rejected reviews before handoff

    1. 1
    2. 2
    3. 3
    4. human
  3. Step 03

    Approved work ships.

    Release rebases, verifies CI and merges. The run keeps its receipt.

    1. rebase
    2. CI
    3. merge
    4. Done
Chapter 2 of 8: Proof, not promises

Built by the agents
it orchestrates.

This site, this pipeline and the control plane behind it ship through the same loop we sell. Every pilot call opens the live workspace.

01Our real backlog

02Independent review

03Live workspace on every call

See it on a pilot call

ThriveOnDev / our delivery loop

The loop that ships this product

in production

the merged code runs the next issue

Our own Linear issues run through the pipeline in a loop: an agent implements, a second agent reviews, and the PR merges into the ThriveOnDev control plane, which then runs the next issue.
Chapter 3 of 8: Control is the product

Every run.
Accounted for.

Every stage records its outcome, its agent and its evidence. The receipt stays with the PR, and the dashboard rolls receipts into delivery numbers.

Run receipt · example

Add rate limiting to the public API

PR merged

Outcome trace

Entry 2 of 5

needs review
Stage
Review · attempt 1 of 3
Agent / model
#gpt → Codex · GPT
Outcome
review_changes_requested
Evidence
Review with findings posted on the PR
Review attempts
2 of 3
Inference billing
billed at provider rates — markup $0.00
Routing policy

One alias. Any harness.

agent-neutral
Choose the implementer

Implement and fix review run on #opus; review runs on #gpt.

Measure delivery, not activity.

Your dashboard · populated by your runs

  • Merged PRs / week

    PRs the pipeline merged, week by week

  • No-rework rate

    Merged PRs that needed no human rework

  • Cost / merged PR

    Inference cost per merged PR, API-billed runs

  • Provider comparison

    Outcomes and cost by agent and model

Chapter 4 of 8: Run economics

No token markup — your keys, your rates.

We charge for the pipeline, not the tokens. You keep a direct relationship with your model provider, and every run prints its own receipt.

ThriveOnDev token markup

$0.00

Your tokens never pass through our margin.

Run receipt · billingone run
Inferencetokens × provider price
→ provider
Pipelineplatform fee · per workspace
→ ThriveOnDev
Token markupour share of your inference
$0.00

billed at provider rates — markup $0.00

Where a run’s inference bill goes
Provider rates, billed to your keys
100%
ThriveOnDev token markup
0%

  • Inference

    Your model provider

    Your API keys, at provider rates. We never sit in the middle.

    tokens × provider price → your invoice

  • Orchestration

    ThriveOnDev

    A platform fee for the pipeline, per workspace.

    never per seat · never per token

Chapter 5 of 8: One practice, not ten habits

One practice.
Every agent.

Skills, pipelines and review standards are versioned once in your repository and applied server-side, so every agent and every engineer works to the same standard.

  • Versioned in your repo
  • Applied server-side
  • Nothing to install on day one
Shared skills repository

One versioned practice

applies across providers

skills/

  • /implement
  • /review
  • /fix-review
  • /audit
  • Claude Code
  • Codex
  • Copilot
  • OpenCode
  • Gemini
Chapter 6 of 8: Technical sovereignty

Your infrastructure.
Your control plane.

The whole platform installs into your Kubernetes cluster with Helm. Source code and credentials stay inside the boundary; only inference leaves, to the provider you already use.

  • Whole platform, Helm
  • Namespace per tenant
  • Network policies
  • Per-run credentials
  • Pipeline audit trail
  • SSO · air-gapped path
See all controls
Discuss self-hostingDownload security overview
Linearissue · comment · status

Your cluster

ThriveOnDev control planeHelm
  • graphs
  • routing
  • audit
  • K8s job

    Isolated agent job

  • Tenant scope

    Repository + tools

code and credentials remain inside the boundary

Your model providerbilled to your API keys
Linear sends commands into your cluster. The ThriveOnDev control plane, isolated agent jobs, and the repository run inside the cluster boundary. The only call that leaves the boundary for inference goes to your model provider.
Chapter 7 of 8: The pipeline pilot

Prove it on
your own backlog.

Four weeks on your own backlog, measured in merged PRs, rework and cost per merged PR. Scope and success criteria are agreed before the first run.

weeks to evaluate
a real delivery pipeline

Four weeks, one decision
  1. Week 1ConnectLinear, agents, keys. Criteria agreed.
  2. Week 2RunYour backlog through the pipeline.
  3. Week 3TuneSkills, routing, review standards.
  4. Week 4DecideMerged PRs, rework, cost per PR.
From first call to first run

Start with a 25-minute fit check.

A live workspace walkthrough, your deployment needs, and a straight answer on fit. Pricing is agreed together.

  • Founder-led onboarding
  • Your backlog, agreed success criteria
  • Cloud or self-hosted
  • Pilot fee credited to year one
Book a 25-min pilot introDiscuss self-hosting

After the pilot, choose your home

Managed cloud

We operate the platform. You bring your agents and keys.

Your Kubernetes cluster

The whole control plane, execution, and data. Installed via Helm.

Chapter 8 of 8: Due diligence

Before your first run.

Straight answers on fit, agents, security and risk.

9 answers by topic

Something unanswered? info@thriveondev.com

Showing 9 of 9 questions.

We already have Copilot review, CodeRabbit, or Claude Code Review. Why add this?

Keep them. Review bots comment on a PR that already exists. ThriveOnDev runs the delivery around the agent: every PR it produces gets a mandatory review by a second agent from a different provider, a bounded fix loop that hands the issue back to a person after three rejected reviews, and a recorded outcome and cost for every run.

Link to this answer
Linear and our coding agents already create PRs. Why add this?

Keep them. What they don’t give you is evidence: merged PRs, rework, and cost per merged PR; an audit trail recorded by the pipeline, not self-reported by the agent; an enforced second-agent review; and the option to run it all in your own cluster.

Link to this answer
Which teams are a fit for the pipeline pilot?

AI-forward engineering teams of roughly 15–150 people on Linear with at least one coding agent — especially teams feeling review bottlenecks, cost opacity, or agent sprawl, or that must keep code inside their own Kubernetes.

Link to this answer
Which agents and models are supported?

Claude Code, Codex, Copilot, OpenCode, and Gemini. The shipped routing aliases today are #opus and #gpt; the versioned graph and delivery policy stay the same.

Link to this answer
Our developers each use AI differently. Does this help?

Yes. Skills, pipelines, and review standards are versioned once for the organization, and every agent follows them — so a new engineer inherits the team’s AI practice on day one.

Link to this answer
How does provider authentication work?

The platform routes each provider per its own terms automatically. Credentials stay per user and per tenant, accounts are never shared, and runs use your API keys at provider rates unless sanctioned subscription auth is enabled for that tenant.

Link to this answer
Can the whole platform run in our infrastructure?

Yes. Helm installs the control plane and agent execution into your Kubernetes cluster, with offline license validation, tenant-scoped jobs and credentials, SSO/OIDC, and an air-gapped path — source code does not need to leave your perimeter.

Link to this answer
How do we know AI is actually paying off?

Every run leaves a receipt: outcome, runtime, cost, and model. The dashboard rolls them up into merged PRs per week, rework rate, and cost per merged PR for API-billed runs, and pilot success is measured on your own delivery data.

Link to this answer
You are an early company. How do we reduce that risk?

Four weeks, a fixed scope, a named decision owner, and success criteria the product measures. You see the live workspace where we ship our own code, and self-hosting means you don’t depend on our cloud to keep running.

Link to this answer

Your next issue could start here

Put your agents
on the same path.

See the pipeline in action. A live issue-to-reviewed-PR run, its receipt, and a straight answer on fit.

Book a 25-min pilot intro

Or email info@thriveondev.com