Compare
Every incumbent extends from the system it already owns.
Each vendor below ships a strong agent platform, and each works best when more of your lifecycle moves into it. This page is written from the seat of a company that runs several of them at once.
Vendor by vendor
What each does well, and where it comes from.
Where a product comes from shapes what it sees. That is a strength inside its own system, and a boundary outside it.
| Comes from | What it does well | What follows from where it comes from | |
|---|---|---|---|
| GitLab | The code host and CI. | A full agent platform inside GitLab: planning, coding, review, CI-fix and security agents; custom agents and organisation-defined flows; external agents such as Claude Code and Codex CLI; an MCP client; self-hosted models behind its AI gateway; and a context graph over code, merge requests, pipelines, deployments and vulnerabilities. | Its graph and its flows are complete inside GitLab. Work, review and deployment that live in other tools are harder for it to see and to govern. |
| Atlassian | The tracker and the wiki. | Agents in Jira: work items assigned to agents, a path from work item to plan to code to review with Rovo Dev, and the Teamwork Graph, which gives agents the organisation's context. | The process is Jira's workflow. A record of what was verified, against what and by whom, is outside the shape of that workflow. |
| GitHub | The code host. | The Copilot cloud agent, and Agentic Workflows: automations written in natural language, compiled to Actions, read-only by default, with writes limited to the safe outputs a workflow declares. | Its automation is scoped to a repository. An organisation's process, ownership routing and release record across repositories sit outside that scope. |
| Harness | CI/CD. | Worker Agents that run as pipeline steps, governed by OPA policies, role-based access, sandboxing and an LLM gateway. | It governs the pipelines it runs. What comes before the pipeline — the intent, the design, the plan and the decisions people make — sits outside it. |
| Engineering intelligence | Analytics. | Jellyfish, LinearB, Swarmia and DX turn delivery data into DORA, flow and AI-adoption dashboards that leaders read every week. | They report on the process; they do not run it. Some measure individuals; Continuumship reports by team and Area, never by person. |
- GitLab
- Comes from: The code host and CI.
- What it does well: A full agent platform inside GitLab: planning, coding, review, CI-fix and security agents; custom agents and organisation-defined flows; external agents such as Claude Code and Codex CLI; an MCP client; self-hosted models behind its AI gateway; and a context graph over code, merge requests, pipelines, deployments and vulnerabilities.
- What follows from where it comes from: Its graph and its flows are complete inside GitLab. Work, review and deployment that live in other tools are harder for it to see and to govern.
- Atlassian
- Comes from: The tracker and the wiki.
- What it does well: Agents in Jira: work items assigned to agents, a path from work item to plan to code to review with Rovo Dev, and the Teamwork Graph, which gives agents the organisation's context.
- What follows from where it comes from: The process is Jira's workflow. A record of what was verified, against what and by whom, is outside the shape of that workflow.
- GitHub
- Comes from: The code host.
- What it does well: The Copilot cloud agent, and Agentic Workflows: automations written in natural language, compiled to Actions, read-only by default, with writes limited to the safe outputs a workflow declares.
- What follows from where it comes from: Its automation is scoped to a repository. An organisation's process, ownership routing and release record across repositories sit outside that scope.
- Harness
- Comes from: CI/CD.
- What it does well: Worker Agents that run as pipeline steps, governed by OPA policies, role-based access, sandboxing and an LLM gateway.
- What follows from where it comes from: It governs the pipelines it runs. What comes before the pipeline — the intent, the design, the plan and the decisions people make — sits outside it.
- Engineering intelligence
- Comes from: Analytics.
- What it does well: Jellyfish, LinearB, Swarmia and DX turn delivery data into DORA, flow and AI-adoption dashboards that leaders read every week.
- What follows from where it comes from: They report on the process; they do not run it. Some measure individuals; Continuumship reports by team and Area, never by person.
Where we overlap
Where we overlap, and what the control plane adds.
Some of what these vendors ship looks like part of Continuumship. Here is what the control plane adds, including for a team that lives in one vendor.
| Where we overlap | What the control plane adds |
|---|---|
| Flows of agents and people — GitLab flows, Harness pipelines, GitHub Agentic Workflows | Your organisation's delivery process, with owners, gates and a ledger, running across every tool, with their flows and pipelines as steps inside it. Inside one vendor too, the decisions, the guardrails and the evidence sit in one record your auditors can read. |
| A context graph — GitLab Orbit, the Teamwork Graph | A system graph of interfaces, consumers and owners across vendors that never holds your code. Code-level indexing stays on your side, run by you. |
| Self-hosted models and your own keys | Routing by your rules, with budgets and a usage ledger per work item, whichever tool the work started in. |
| Policy as code — Harness and OPA | Guardrails over what is required before the pipeline as well as inside it, with your own policy engine accepted as a capability. |
- Flows of agents and people — GitLab flows, Harness pipelines, GitHub Agentic Workflows
- What the control plane adds: Your organisation's delivery process, with owners, gates and a ledger, running across every tool, with their flows and pipelines as steps inside it. Inside one vendor too, the decisions, the guardrails and the evidence sit in one record your auditors can read.
- A context graph — GitLab Orbit, the Teamwork Graph
- What the control plane adds: A system graph of interfaces, consumers and owners across vendors that never holds your code. Code-level indexing stays on your side, run by you.
- Self-hosted models and your own keys
- What the control plane adds: Routing by your rules, with budgets and a usage ledger per work item, whichever tool the work started in.
- Policy as code — Harness and OPA
- What the control plane adds: Guardrails over what is required before the pipeline as well as inside it, with your own policy engine accepted as a capability.
What is different
A control plane that belongs to none of your tools.
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01
Across your tools, not inside one
Jira, GitLab, a pipeline from 2019 and Argo CD are steps in one process. None of them has to become the centre for the process to hold.
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02
Your machines, not our custody
Runs execute on your runners. Our code-host credentials cannot read repository contents, and source, diffs, secrets and model transcripts never leave your machines.
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03
Provenance on every claim
Every check says who measured it — Verified by Continuumship, Measured by runner, Reported by agent — and the evidence chain runs from the accepted intent to a signed attestation.
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04
Guardrails and the process, as code
Rules no team can remove. Flows, routing and budgets as definitions in your repository, changed through a plan a person decides, from your own agent over MCP.
Keep, adopt, consolidate
Nothing is replaced until you decide.
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Connect
Your tracker stays the system of record. Teams keep working where they work; Continuumship holds the decisions and the evidence, reached by links.
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Adopt
Teams that choose to work natively move to the Flow Board and the work item. Others stay where they are.
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Consolidate
One product, when you decide a tool has been outgrown — chosen per capability and per Area, never forced.
Show us the work that does not live in one tool.
Start with a thirty-day shadow pilot on one Area. We connect, observe how you ship, and read the report with you.