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In development — looking for design partners

AI writes faster than your team can read.

We make sure what matters still gets read. Keep your agents — the review layer works on top of them.

Book an intro callSee what ships today

25-minute intro · live workspace walkthrough

Concept · example PR

Add rate limiting to the public API

An example AI-written PR in six fragments. Agents check the service code, tests, and docs. A person reads the migration, the API contract, and the ingress change, and each read is recorded.

For PRs from the agents your team already uses — or from a human.

  • Devin
  • Copilot
  • Claude Code
  • Codex
  • Linear's agent
In development — looking for design partners
Chapter 1 of 10: The review layer

Read the parts that matter. Keep the evidence.

What a person must read, what agents check — and proof it was read.

Concept · example PR

Add rate limiting to the public API

  1. 01Infrastructuredeploy/helm/values.yamlperson reads
  2. 02Databasedb/migrations/add_rate_limits.sqlperson reads
  3. 03Contractapi/openapi.yamlperson reads
  4. 04Service codesrc/limits/token-bucket.tsagents check
  5. 05Testssrc/limits/token-bucket.test.tsagents check
  6. 06Docsdocs/rate-limits.mdagents check
6 fragments: 3 for a person, 3 for agents.Illustration, not a product screenshot.
  • Must-read fragments, ranked by risk
  • A record of what was read
  • Design changes, by layer
  • New behavior, called out
Book a design-partner call
Chapter 2 of 10: The pipelineShipped today

And when you want to hand whole issues to agents:

Issue in.Reviewed PR out.

The agent-neutral delivery pipeline for Linear.

See how it worksExplore the control room
Delivery workflowworkflow example
LinearExample issue

Add rate limiting to the public API

bounded autonomy

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.

Chapter 3 of 10: One issue. One governed outcome.

From assignment to release.

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 4 of 10: Proof, not promises

The control plane ships its own code.

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

See it on a pilot call

ThriveOnDev / live workspace

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 5 of 10: Control is the product

Every run leaves a receipt.

Outcome, review trail, and cost for every run.

Your outcome dashboardawaiting your first run
Merged PRs / week
No data until your first runs.
No-rework rate
No data until your first runs.
Cost / merged PR
No data until your first runs.
Provider comparison
No data until your first runs.

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.

Chapter 6 of 10: Run economics

No token markup — your keys, your rates.

We charge for the pipeline, not the tokens.

ThriveOnDev token markup

$0.00

One run
  • inferenceYour model provideryour API keys · provider rates
  • pipelineThriveOnDevplatform fee
Each run bills inference to your model provider on your own API keys at provider rates, and the pipeline to ThriveOnDev as a platform fee.
Chapter 7 of 10: One practice, not ten habits

One team. One shared practice.

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 8 of 10: Technical sovereignty

Your code can stay in your perimeter.

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-hostedDownload 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 9 of 10: Two ways to start

Pilot the pipeline, or shape the review layer.

Scope and success criteria agreed up front. No public price list.

Offer 1 · pipeline pilotShipped today

Prove it on your own backlog.

Merged PRs, rework, and cost per merged PR — measured on your backlog.

  • 4 weeks, founder-led
  • cloud or self-hosted
  • fee credited to year one
Book a 25-min pilot introDiscuss self-hosting
Offer 2 · design partnershipIn development

Shape the review layer on your PRs.

For teams whose agent PRs get approved without being read.

  • 4 weeks, one repository
  • your must-read policy
  • fee credited to year one
Book a design-partner call
From first call to production

Start with a 25-minute fit check.

A 25-minute intro call with a live workspace walkthrough, then a four-week pipeline pilot on your backlog, then either managed cloud with a platform fee and no token markup, or an annual self-hosted install via Helm.
Chapter 10 of 10: 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?In development

Keep them. Review bots add comments for a person to read; the review layer cuts what a person must read down to the fragments that carry risk, and records that they were read. It is in development — what ships today is a mandatory review by a second agent from a different provider.

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.

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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.

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
The next useful step

Show us how your team reads agent PRs.

25 minutes: 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