What changed in the comparison
A tracker used to be a shared UI: humans read it, decided what to do next, and did the work elsewhere. Agents invert the arrangement. When Claude Code, Codex or Cursor sessions take tasks directly, the tracker stops being a display and becomes dispatch infrastructure, with the same correctness demands as any queue that concurrent workers consume. Two agents taking the same ticket is not a process hiccup anymore; it is a race condition you pay for in tokens and duplicate PRs.
Where Linear and Jira are genuinely better
Start with the honest half. Linear’s planning experience is the best in the category: fast, keyboard-driven, opinionated, and generally loved by the product teams who live in it. Jira carries approval workflows, compliance surface and an ecosystem two decades deep; if your organization runs on Jira rituals, they are load-bearing. None of that is what an agent-first queue competes with, and a tracker pitch that pretends otherwise should cost that tracker your trust.
The gap is narrower and more specific: what happens between “task exists” and “PR merged” when the thing doing the work is software.
What breaks when agents work the backlog
Human-first trackers give agents the same interface they give people: read issues, write issues. Linear’s MCP is exactly that, CRUD on a tool built for humans clicking. An agent can see the backlog, but nothing stops two agents taking the same issue, and the dependency graph is decoration that dispatch never consults.
Assignment, not claims
Assigning presumes a human dispatcher. Agents need to pull, atomically, with the server refusing doubles.
No concept of dying
A person on vacation still exists. A crashed agent just stops, and its assigned ticket looks taken forever.
Edges nobody enforces
Blocked-by relations render as warnings for humans. Nothing prevents dispatching the blocked task to an agent.
Each gap is invisible with one agent and painful with three. The failure modes and their fixes are worked through step by step in the parallel agents guide and the agent swarm guide; the short version is that correctness has to be enforced where the tasks live, by the server. Two more gaps surface a little later: a restarted agent starts from zero because nothing durable recorded its progress, and when every agent writes through one bot token, nobody can say whose agent did what.
The agent-first checklist
Whatever tracker you evaluate, these five properties decide whether agents can work it unattended. They are yes-or-no questions a vendor can answer in a sentence each.
Retasc is built as five yeses: that is its whole thesis, and the compare page holds the feature-by-feature version against Linear, Jira, CrewAI, Devin and Temporal. Pricing follows the same inversion, metered per action instead of per seat, since agents are not seats. Adding human teammates costs nothing, because there are no seats to buy.
Run both: a reversible migration
The practical answer for most teams is a split: humans keep planning where they plan today, agents pull implementation work from a queue that enforces the checklist. Retasc makes the split cheap in both directions, in and out. A Linear workspace imports in one pass: issues, threaded comments, labels, attachments, relations and identifiers, original timestamps preserved so history reads true. Re-importing reconciles in place instead of duplicating, so Linear stays live while agents trial the queue. GitHub Issues flow in through a signed webhook and close back at the source when done. The whole org exports as JSON or CSV anytime.
If the trial convinces you, the queue becomes the system of record for executable work. If it does not, you re-export and nothing is lost.
Data safety is part of the same calculus. Issue and comment content, titles, bodies and activity, is encrypted at rest under a key minted for your org alone, and deleting the org destroys that key. It is not end-to-end encrypted, since the server holds the keys to run the queue, but your roadmap never sits in plaintext beside another tenant’s. The product page covers how.
Questions that come up
Is an agent-first tracker a Linear alternative for human-only teams?
Mostly no. If no agents work your backlog, Linear’s planning experience is the product and you should keep it. The evaluation only flips when AI coding agents start taking tasks: then dispatch correctness, leases and identity outweigh planning polish, and that is the job agent-first trackers are built for.
Can I try an agent-first tracker without leaving Linear?
Yes. The dashboard imports a Linear workspace in one pass and re-import reconciles instead of duplicating, so Linear stays live while agents trial the queue. Everything comes across: issues, threaded comments, labels, attachments, relations, identifiers, original timestamps. If the trial fails, the whole org exports as JSON or CSV.
Does an agent-first tracker replace Jira workflows?
No. Jira’s approval flows, compliance surface and ecosystem are a different job. An agent-first queue takes over one thing: dispatching executable work to agents with server-enforced correctness. Many teams keep the human planning tool and point the agent queue at the implementation backlog.
What makes a tracker agent-first?
Five properties: claims are atomic so two agents can never take the same task; claims are leases that expire and reclaim when an agent dies; the dependency graph is enforced at dispatch, not merely displayed; work resumes across runtimes from durable checkpoints; and every action carries the identity of a human principal plus a runtime.
The feature-by-feature comparison lives on the compare page. Trying the split takes three commands and no payment setup; signup seeds the first $10 of usage.