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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

Who feels it

Developer

I no longer understand the code I own.

In development — looking for design partners

Each PR narrows to the fragments a person must read.

See the review layer

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
The review layer

Read the parts that matter. Keep the evidence.

Split each PR into what a person must read and what agents can check — and record that it was read.

Concept · example PR

Add rate limiting to the public API

  1. 01Infrastructuredeploy/helm/values.yamlNew ingress rate-limit annotationperson reads
  2. 02Databasedb/migrations/add_rate_limits.sqlNew table with a tenant foreign keyperson reads
  3. 03Contractapi/openapi.yamlPublic endpoints can now return 429person reads
  4. 04Service codesrc/limits/token-bucket.tsBucket refill logicagents check
  5. 05Testssrc/limits/token-bucket.test.tsRefill and burst casesagents check
  6. 06Docsdocs/rate-limits.mdLimits table for API usersagents check
6 fragments: 3 for a person, 3 for agents.An illustration of the concept, not a product screenshot.

  • Must-read fragments

    Ranked by risk layer.

  • Read acknowledgment

    A record of what was actually read.

  • Design-change view

    What changed in the system, by layer.

  • Behavior notes

    New edge cases, error paths, defaults.

Built on the pipeline that ships today. A design partnership is four weeks on one repository.

Book a design-partner call
Shipped today

And when you want to hand whole issues to agents:

Issue in.Reviewed PR out.

The agent-neutral delivery pipeline for Linear — with the cost and outcome of every run on one screen.

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.

04/06Back to review: #gpt reviews the fixed PR again.

  • Agent-neutral

    Route the right agent per task

  • Measurable delivery

    Cost and outcome per run

  • Your infrastructure

    Your Kubernetes or ours

  • Shared standards

    One practice across every agent

One issue. One governed outcome.

From assignment to release.

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

  1. Step 01

    Linear is the command surface

    Assign or label an issue.

    Assign the agent or comment /implement. Label issues Automatable to drain the backlog server-side.

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

    The delivery graph takes over

    Review, fix, review again.

    A second agent reviews; requested changes return through fix-review.

    Rejected reviews before handoff

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

    Humans keep authority

    Approved work moves to release.

    Release rebases, verifies CI, and merges. After three rejections, a human takes over.

    1. rebase
    2. CI
    3. merge
    4. Done
Proof, not promises

The control plane ships its own code.

Our own Linear issues are implemented, reviewed, and fixed by the agents ThriveOnDev orchestrates. Every pilot call opens the live workspace.

See it on a pilot call

ThriveOnDev / live workspace

Dogfood delivery graph

in production

  1. Issue selected in Linear

  2. Agent implements in an isolated job

  3. Second agent reviews and fixes

  4. Outcome and cost recorded

Control is the product

Every run leaves a receipt.

Outcome, review trail, and provider for every run — with API cost tied to delivery.

Your outcome dashboardawaiting your first run
Merged PRs / week
No data until your first runs. Outcome throughput
No-rework rate
No data until your first runs. Quality signal
Cost / merged PR
No data until your first runs. API-billed ROI
Provider comparison
No data until your first runs. Agent + model
Run receipt · example

Add rate limiting to the public API

PR merged

Outcome trace

Entry 2 of 5

rejected
Stage
Review · attempt 1 of 3
Agent / model
#gpt → Codex · GPT
Outcome
review_changes_requested
Evidence
Review with findings posted on the PR
Runtime
Recorded per run
Billing source
Customer API key
Run outcome
Merged 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
  1. Implement#opusClaude · Opus
  2. ReviewAlways the other agent#gptCodex · GPT
  3. Fix review#opusClaude · Opus

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

Supported harnesses

  • Claude Code
  • Codex
  • Copilot
  • OpenCode
  • Gemini

Durable recovery

Retries, timeouts, and dead-letter handling live in the graph.

Isolated execution

A tenant-scoped Kubernetes job per run.

Audit by default

One trace, recorded by the pipeline — not the agent.

Run economics

No token markup — your keys, your rates.

We charge for the pipeline, not the tokens. Your run receipt connects the cost to what shipped.

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.

Bring the agents you already use

  • Claude Code
  • Codex
  • Copilot
  • OpenCode
  • Gemini
One practice, not ten habits

One team. One shared practice.

Version the skills, pipeline, and review standard once. Apply them to every agent and provider.

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

One versioned practice

applies across providers

  • implement

    Build the change

  • review

    Check the result

  • fix-review

    Resolve review findings

  • audit

    Assess the codebase

  • Claude Code
  • Codex
  • Copilot
  • OpenCode
  • Gemini
Technical sovereignty

Your code can stay in your perimeter.

The whole platform in your cluster — control plane, planning, execution, and data. 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-hostedSecurity 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.
Two ways to start

Pilot the pipeline, or shape the review layer.

Scope, decision owner, 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.

  • Four-week, founder-led engagement
  • One Linear workspace and a scoped repository set
  • Live cloud or self-hosted deployment path
  • Success criteria measured by run telemetry
  • Pilot fee credited to the first-year contract
Pilot intro — 25 minSelf-hosted conversation
Offer 2 · design partnershipIn development

Shape the review layer on your PRs.

For teams whose agent PRs get approved without being read.

  • Four weeks on one repository
  • PRs from the agents you already use — or from people
  • Must-read policy written with your team
  • Fee credited to the first-year contract
Book a design-partner call
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 to a PR — more for a person to read. The review layer works the other way: it reduces what a human must read to the fragments that carry risk, and records that those fragments were actually read. It is in development with design partners; what ships today is the pipeline’s 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 — including the built-in pipelines and spend limits your tracker and providers added this year. What none of them give you is proof and custody: telemetry that shows what your agents delivered (merged PRs, rework, cost per merged PR — not cost per session), an audit trail recorded by the pipeline rather than self-reported by the agent, an enforced second-agent review on every run, and the whole platform installable in your own cluster. It is for teams that need evidence, not another trigger.

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

AI-forward engineering teams of roughly 15–150 people that already use Linear and at least one coding agent. The strongest fit is a team feeling review bottlenecks, cost opacity, agent sprawl, or a requirement to keep code and execution inside its own Kubernetes environment.

Link to this answer
Which agents and models are supported?

Claude Code, Codex, Copilot, OpenCode, and Gemini are supported. The currently shipped routing aliases 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 defined once at the organization level and versioned. Every agent and provider follows them, so a new engineer automatically 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. Policy-aware routing is a governance control: 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. The self-hosted package installs the control plane and agent execution into your Kubernetes cluster via Helm. It supports offline license validation, tenant-scoped jobs and credentials, SSO/OIDC, and an air-gapped deployment path, so source code does not need to leave your perimeter.

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

Every run produces a receipt with the outcome, runtime, cost, and model. The dashboard aggregates merged PRs per week, rework rate, and cost per merged PR for API-billed runs. Pilot success criteria are measured by the product itself, using your own delivery data.

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

The engagement is limited to four weeks with a fixed scope, a named decision owner, and success criteria the product measures. We show the live workspace where ThriveOnDev ships its own code, and the self-hosted option means you do not have to depend on our cloud for continued operation.

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.

Pilot intro — 25 min

Or email info@thriveondev.com