Issue in.Reviewed PR out.
The agent-neutral delivery pipeline for Linear. Implement, review, and fix in a governed loop — with the cost and outcome of every run on one screen.
25-minute intro · live workspace walkthrough
Add rate limiting to the public API
Delivery graph
issue → merged PR- Implement, assigned to Opus, succeeded. The implementation_ready outcome moves the run to Review.
- Review, assigned to GPT, running.
- 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.
- Release, queued. The release_merged outcome completes the run.
- PR merged, queued.
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
No token markup — your keys, your rates.
We charge for the pipeline, not the tokens. Runs bill to your own API keys at provider rates; where a provider's terms sanction it, your existing agent subscriptions can power runs too.
Policy-aware routing applies each provider's terms automatically. Credentials remain per-user and scoped to the tenant; accounts are never shared.
ThriveOnDev token markup
$0.00
Your provider bills inference. Your run receipt connects the cost to what shipped.
- Inference billing
- Provider rates
- Credential boundary
- Per user · per tenant
Bring the agents you already use
- Claude Code
- Codex
- Copilot
- OpenCode
- Gemini
The control plane ships its own code.
ThriveOnDev's own Linear issues are implemented, reviewed, and fixed by the agents it orchestrates. The same graphs, isolation, and run receipts we sell are how the product gets built.
Every pilot conversation includes a walkthrough of the live workspace — not a polished recording or a fabricated dashboard.
See it on a pilot callThriveOnDev / live workspace
Dogfood delivery graph
Issue selected in Linear
Agent implements in an isolated job
Second agent reviews and fixes
Outcome and cost recorded
The strongest proof point is operational: we can open the actual issue, agent session, PR review, and run receipt in one demo.
Every run leaves a receipt.
Follow the outcome, the review trail, and the provider behind it. Keep API cost connected to delivery.
A pilot agrees success criteria up front, then populates these views with your real delivery data — not generic benchmarks.
- Merged PRs / week
- Outcome throughput
- No-rework rate
- Quality signal
- Cost / merged PR
- API-billed ROI
- Provider comparison
- Agent + model
Outcome, runtime, cost, and model
- Agent / model
- Recorded per run
- Runtime
- Recorded per run
- Billing source
- Customer API key
- CI result
- Recorded per run
- Rejections
- Counted per issue
Outcome trace
- Implement#opus
completed
- Review#gpt
changes requested
- Fix review#opus
completed
- Review#gpt
approved
- Releaserelease_merged
PR merged
Merged PR
Aggregated
billed at provider rates — markup $0.00
One alias. Any harness.
Change the underlying agent or model without retraining the team, rewriting automations, or betting delivery on one lab.
Alias map
Deep reasoning
General delivery
The implementer and the reviewer are different agents — by pipeline design.
Durable recovery
Retries, timeouts, cleanup, and dead-letter handling are part of the graph — not an operator’s late-night checklist.
Isolated execution
Each run gets a tenant-scoped Kubernetes job with a defined repository and tool boundary.
Audit by default
The issue, graph version, agent events, review, outcome, and cost stay connected in one trace — recorded by the pipeline, not self-reported by the agent.
One team. One shared practice.
Version the skills, pipeline, and review standard once. Apply them to every agent and provider.
ThriveOnDev makes the team's AI practice a shared, versioned asset. The same skills, pipeline, and review standard apply to every agent and every provider. A new engineer installs nothing: no MCP servers, no local setup. Assign a Linear issue on day one; the team's skills and pipeline apply server-side.
One versioned practice
implement
Build the change
review
Check the result
fix-review
Resolve review findings
audit
Assess the codebase
Managed in your repo. Visible in the dashboard. The practice stays stable even when the agent or provider changes.
Your code can stay in your perimeter.
The whole platform in your cluster — control plane, planning, execution, and data. The only external call is to your model provider, same as your IDE.
AI traffic flows through one governed pipe instead of thirty laptops: org-level provider configuration, per-user credentials, and an audit trail for every run.
- Whole platform via Helm: control plane, planning, execution, and data
- Every tenant gets its own namespace with Pod Security enforcement — no privileged containers, one agent job per node
- Per-tenant network policies: an agent job cannot reach other tenants, and tenant boundaries are regression-tested in CI
- Scoped, short-lived credentials per run; HMAC-signed callbacks; audit trail recorded by the pipeline, not self-reported by the agent
- Offline license validation, SSO / OIDC, and an air-gapped deployment path
Customer cluster boundary
issue · comment · status
Isolated agent job
Repository + tools
code and credentials remain inside the boundary
From assignment to release.
Each outcome chooses the next step. Approval exits through release; requested changes return through fix-review, with no unlimited retries.
- 01
Linear is the command surface
Assign or label an issue.
Keep work where the team already plans it. Assign the agent or call /implement with a model alias. Label issues Automatable and the backlog drains while the team sleeps — dependency-aware, server-side, no laptop required.
/implement #opus - 02
The delivery graph takes over
Review, fix, review again.
A versioned graph runs implement, review, fix-review, then review again. Every outcome is recorded, and the loop is capped at three rejected reviews.
review_changes_requested → fix_review_ready → review - 03
Humans keep authority
Approved work moves to release.
A passed review dispatches release to rebase, verify CI, and merge the PR. After three rejections, the graph stops and returns the issue to a human.
review_approved → release_merged → Done
Prove it on your own backlog.
A focused pilot for AI-forward teams on Linear — designed to measure merged PRs, rework rate, cost per merged PR, and provider performance on your own 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
Consultative pricing; no public self-serve checkout. The pilot starts only after scope, decision owner, and measurable success criteria are agreed.
Before your first run.
Early-stage does not mean vague. The pilot exists to make the technical and commercial decision with real evidence.
Something unanswered? info@thriveondev.com
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.
Which teams are a fit for the design-partner 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.
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.
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.
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.
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.
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.
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.
See the pipeline in action.
In 25 minutes: a live issue-to-reviewed-PR run, the receipt behind it, and a straight answer on whether your team is a good pilot fit.