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Agent-neutral delivery. Built for Linear.

Issue in. Reviewed PR out.

Your coding agents. One governed pipeline. Independent review, a receipt for every run, and the whole control plane 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.

Your agents. The same 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.

Each outcome picks the next step — with no unlimited retries.

  1. Step 01

    Assign or label an issue.

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

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

    Review, fix, review again.

    A second agent reviews. Three rejections, then a human.

    Rejected reviews before handoff

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

    Approved work ships.

    Release rebases, verifies CI, and merges.

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

Built by the agents
it orchestrates.

Our own issues run through this pipeline. Every pilot call opens the live workspace.

01Our real backlog

02Independent review

03Live workspace

See it on a pilot call

ThriveOnDev / our delivery loop

Dogfood delivery loop

in production
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.

Follow the decisions, inspect the review trail, and see what every run delivered. The evidence stays with the work.

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 just 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. Keep a direct relationship with your model provider and a receipt for every run.

ThriveOnDev token markup

$0.00

Your tokens never pass through our margin.

Inference
Your model providerYour API keys. Provider rates.
Orchestration
ThriveOnDevA platform fee for the pipeline.
Chapter 5 of 8: One practice, not ten habits

One practice.
Every agent.

Skills and review standards, versioned once — applied to every agent.

  • 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 runs in your cluster. Inference goes to your model provider, same as your IDE.

  • 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.

Merged PRs, rework, and cost per merged PR — measured on your work. Scope and success criteria agreed up front.

4

weeks to evaluate
a real delivery pipeline

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.

Early-stage does not mean vague.

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.

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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.

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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.

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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.

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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.

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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