Devin Alternative: When Ready to Merge Is Not Done

An engineering lead evaluating Devin is usually not asking whether it can write code. It can, and Cognition has built a serious product around that. The harder question arrives a few weeks into a rollout, when a dozen sessions have produced a dozen pull requests and someone in finance or audit asks a simple thing: which of these tasks are actually done, and how do we know? That question is about the unit of record, not about the agent.

Devin organises work around the session. A ticket in Linear or Jira, a Slack thread or a schedule starts a session; the session runs on its own machine, opens a pull request and reports back. Cognition's When to Use Devin guide sets the end state plainly: "Ideally, you just need to see that CI passes and the PR is approved." For many teams that is exactly the right bar. For a team that has to show what was asked, what was checked and who accepted it, the bar sits in a different place.

This guide compares Devin with Cognibl, from DILR.AI, for the engineering lead or VP Engineering who signs off agent work. It holds one altitude: the session as the unit of record against the task as the unit of record, and who defines done in each. It cedes the grading of Devin's recordings and test plans to our guide to coding agent artefacts, Linear's own agent-session model to our Linear alternative for AI agents, workflow validators to the Jira with Rovo comparison, sandboxing to our cloud agent isolation guide, and the full contents of a proof to the proof of done guide.

This guide is shipped by the team behind Cognibl, a work tracker where people and AI agents share one board and a task reaches done only once proof is attached. Or see DATS, our five-stage method for placing AI inside engineering and operations.

What is Devin, and what does it do well?

Devin is Cognition's autonomous coding agent. It takes a task from a prompt, a ticket or a chat thread, works in its own cloud machine with a shell, editor and browser, and returns a pull request. Its strengths are autonomy on scoped engineering work, broad integrations across GitHub, GitLab, Linear, Jira, Slack and Teams, and a review product that helps it respond to comments and failing checks.

Cognition describes Devin on its homepage as an autonomous software engineer that runs in the cloud or on your machine, tests in its own browser and keeps going until the pull request is ready to merge. The surrounding product has grown well beyond the agent itself. Cognition announced on 14 July 2025 that it had signed a definitive agreement to acquire Windsurf, the agentic IDE, and the documentation now covers a command line client, cloud sessions, a review product and an automations layer.

Three things deserve credit before any comparison. First, Devin is honest about scope: its guidance says that if a task would take you three hours or less, Devin can most likely do it, and that longer work should be split into smaller sessions. Second, it treats success criteria as part of the task. Its pre-task checklist asks whether you can describe clear success criteria and says good tasks have a clear start and end, with explicit criteria such as passing tests, matching an existing pattern or a green CI run. Third, it governs the machine well. Devin's security profiles restrict network, MCP, git and GitHub CLI access, bind at enterprise, organisation, automation and session level, and govern child sessions through the same chain as their parent, so a restriction cannot be escaped by delegating work.

None of that is in dispute here. The comparison that matters for an AI developer productivity programme is narrower: when the session ends, what is the record, and who decided the work was finished?

Where does a Devin task actually live?

A Devin task lives in a session. A Linear assignment, a Jira work item, a Slack mention, a pull request comment or a schedule each starts a session, and the session holds the plan, the activity and the conversation. The ticket that triggered it receives updates, a session link and a pull request link, and in Devin's integration documentation the work itself runs inside the session rather than on the ticket.

The integration documentation makes the shape clear. Devin's Linear integration lists three ways to start a session from a ticket: assign Devin, add a skill label, or mention Devin in a comment. Once work starts, Devin posts to Linear's agent session interface: an activity feed, plan tracking, follow-up messages, a stop signal that puts Devin to sleep, the pull request URL and a link back to the Devin session. Automation triggers use edge detection, so they fire only when a ticket moves from not matching the conditions to matching them, for example when a status changes.

The Jira integration follows the same pattern. Assigning a work item to Devin starts a session that uses the item's summary and description as the task. Assigning it again after Devin has finished starts a new session. Each comment that mentions Devin starts its own session, and when an automation's session finishes, its final message is posted back on the work item as a comment.

Read together, the ticket is the trigger and the mailbox. The status of the ticket starts work; the session does the work; a comment and a link come back. That is a sensible design for throughput. It also means that the question "is this task done?" is largely answered at the ticket and pull request layer, by whoever moves the ticket and whoever approves and merges the pull request. Our overview of what Cognibl is starts from the opposite premise.

Who decides that Devin's work is done?

In Devin's documented workflow, done is decided by review and merge. Devin works until the pull request is ready to merge, Devin Review with Auto-Fix responds to comments and failing checks, and a reviewer approves. Cognition's guidance sets the ideal end state as passing CI and an approved pull request. Devin's documentation describes no step that refuses a ticket's done status for missing evidence.

That is a deliberate and coherent position. Devin's guidance says that with Devin Review and Auto-Fix enabled, Devin responds to review comments, fixes flagged bugs and iterates on CI failures, so pull requests are ready to merge by the time you look at them. Devin Review itself lets a reviewer comment, approve, request changes, merge and toggle auto-merge from one page, and its documentation lists an Autoapprover among its beta features. On the repository side, Devin's GitHub integration guide recommends branch protection rules on the main branch so that all required checks pass before Devin can merge changes. Merging, in other words, is something a Devin deployment can be configured to permit.

The gap is not in the code review. It is in what "approved" is approved against. A green CI run proves the tests that exist passed. An approval proves a person looked. Neither, on its own, records which acceptance criteria the task carried, which of them the evidence covers, or whether the change that merged is the change the ticket asked for. Those are the questions a task verification guide puts to any agent, and they matter more when reviewers are sceptical of AI output to begin with.

They are. In the Stack Overflow 2025 Developer Survey, more developers said they actively distrust the accuracy of output from AI tools (46%) than said they trust it (33%), and 66% named "AI solutions that are almost right, but not quite" as their biggest frustration. An almost-right change is exactly the kind that passes CI and earns a tired approval. A done decision that rests on approval alone inherits that risk.

What survives after a Devin session ends?

Quite a lot survives a Devin session: the pull request, the session itself, which can be resumed, a final comment on the triggering ticket, and Session Insights, which analyses completed sessions. What Devin's documentation does not describe is a task record that holds the acceptance criteria, the evidence and the done decision together, independent of the session that produced the work.

Each surviving piece has a specific job. The Devin command line can resume a cloud session by URL or ID, and closing the terminal does not stop a session running on its VM. Session Insights is available for completed sessions, classifies each by size from XS to XL, and flags L and XL sessions as unhealthy, with an issue timeline and an improved prompt for next time. Devin's GitHub guide notes that Devin responds to comments on pull requests its sessions are working on, as long as the session has not been archived.

The most telling line is in Devin's Memory and Dreaming documentation. Memory saves preferences, corrections, decisions and gotchas as notes that carry across sessions. Its own table lists what is not saved, and two entries stand out: summaries of sessions, and task state, such as pull request numbers or statuses. Memory is also personal to each user, not shared with teammates or the organisation, and sessions started by automations do not read or write it.

That is a sound design choice for an agent's working memory, which should hold durable lessons rather than stale state. It does confirm where task state is expected to live: somewhere else. In a Devin deployment that place is usually the ticketing tool, where the status was moved by a person or an integration, and the pull request, where the approval sits. The evidence that connects the two, including any test recording Devin attaches to its pull request, is spread across the session, a CI run and a review thread.

For a single change that is fine. Across a quarter of agent work it becomes an audit exercise, the problem our recurring agent tasks guide runs into with scheduled runs. The same logic underpins our AI execution office, where a senior team runs agent delivery against agreed definitions of done.

How does Cognibl record agent coding work instead?

Cognibl, from DILR.AI, makes the task the unit of record. A person assigns a task to a named agent from the same list used for colleagues; the agent works on a branch, opens a pull request and attaches proof of work. The task reaches done only once that proof is attached, the same rule applies to people, and a merge alone never completes the task.

The mechanics on the Cognibl product page are specific. Agents run on Claude Code or Codex, each in its own machine. A run follows five steps: the task is assigned to an agent from the assignee list; the agent moves the task to In progress; it pushes a branch, runs the repository's tests and opens a pull request early; it attaches proof of work, a CSV plus screenshots taken while testing the result in a headless browser; and a closing comment says what it did, after which a person decides. A run that ends without proof moves the task to Blocked with the comment "Run ended without proof." so somebody looks at it.

The done gate is a refusal, not a convention. Proof in Cognibl is a CSV describing the run, referencing the artefact, screenshots and hashes, plus a coverage report where tests are part of the claim. Without it, the status change is refused and the refusal says what is missing. Proof is versioned, and records are append-only and hash-chained. Every call an agent makes, whether allowed, refused or failed, is written by the gateway to a trace that is redacted at capture, rather than reported by the agent.

Merging is separated from completion. Agents push through Cognibl's git proxy, which refuses a push to the base branch, and merge is a console button that only a person can press. A merge moves the task one status forward, never straight to done. That single rule is the difference this post is about: in Cognibl, an approved pull request is evidence the task may be finished, not the decision that it is.

Two more parts of the product matter in a Devin comparison. The first is the instruction library. Devin's Skills documentation describes SKILL.md files that follow the open Agent Skills standard; Cognibl keeps a versioned skills and agents library in the same SKILL.md format, where every edit is a new version and nothing is deleted. The second is tool access. Cognibl agents reach tools through an MCP gateway that is deny by default, so a toolset that has not been enabled is refused rather than silently missing, and cognibl.com states that connections are read-only by default, with an admin able to let agents write to Google Workspace. Designing that boundary is part of an AI placement diagnostic.

Devin and Cognibl side by side

The table below compares what each product documents on its live pages and documentation as of 2 November 2026. It is a comparison of models, not a feature scorecard, and where a row says "not described" it means the vendor's documentation does not describe the capability, not that a team could never build it.

Two rows deserve a note. "Where success criteria live" is closer than it looks: Devin's playbook guide suggests a Specifications section that describes postconditions, what should be true once Devin is done, and says playbooks are moving to Skills, with new accounts starting on Skills from 24 October 2026. Both products let a team write down what done means. The difference is enforcement: in Devin the criteria steer the session, while in Cognibl the criteria shape a proof that Cognibl checks before the status can change.

The runtime row cuts the other way. Devin is its own agent with its own model choice, review product and environment tooling. Cognibl does not ship a proprietary coding agent; it runs the coding tools that model makers ship. A team that wants one vendor to own the agent end to end gets more from Devin. The broader category is set out on our strategy category page.

When is Devin the better choice?

Devin is the better choice when the main problem is getting more scoped engineering work done, not proving that work was done. Teams that already trust their CI suite, run strong code review and track completion in Jira or Linear will find Devin's autonomy, integrations, review product and security profiles a strong fit, and may not need a second system of record.

Several situations favour Devin outright. If your reviewers are the control, and your auditors accept a merged, approved pull request as evidence of completion, Devin's documented end state already matches your process. If you want an agent you can start from Slack, Teams, Linear, Jira, GitHub and GitLab, with automations on each, Devin documents that surface in detail. If your platform team wants granular machine controls, Devin's security profiles, with mandatory profiles that clamp mid-session network grants, are a mature design.

Devin also wins on breadth of the agent. It ships its own review product, a command line client, Session Insights that suggest an improved prompt, and personal Memory that learns preferences. Cognibl's answer to those is thinner: two AI flows, proof validation and project status, that summarise and flag but never decide, and delivery metrics that measure verified work. Teams that want the richest single-agent experience should weigh that honestly.

The case for Cognibl starts when completion has to be shown to someone who was not in the review: a client on a fixed-scope engagement, an internal audit function, a regulator asking how AI-produced changes are controlled, or a delivery lead comparing agent and human throughput. That is a governance question as much as a tooling one, and our AI operating model work is where we usually help teams decide which side of it they are on.

What is the best Devin alternative for teams that need proof in 2026?

The best Devin alternative in 2026 depends on what you are replacing. For a team that wants a different autonomous agent, the real alternatives are other coding agents, including OpenAI Codex, which Cognibl itself runs. For a team whose problem is proving completion, Cognibl is the stronger fit, because it gates done on attached proof and keeps merging with a person.

It is worth separating the two decisions, because buyers often blur them. Choosing an agent is a question about model quality, environment setup, latency and cost per task. Choosing a system of record is a question about who defines done, what evidence counts and where it is kept. Devin is a strong answer to the first question and a partial answer to the second. Cognibl is not trying to win the first; its agents run on Claude Code and Codex. It is built for the second, as our guide to AI agent work management sets out.

Use four criteria to decide. First, who signs off completion today, and would they accept an approval as evidence? Second, can you point to the acceptance criteria for a merged change six months later? Third, do people and agents face the same bar, or does agent work get a lighter review? Fourth, is the record written by the system or reported by the agent? A team that answers "the reviewer, no, no, reported" has a proof problem rather than an agent problem.

The honest concession: if your answers are "the reviewer, yes, yes, written by our CI", Devin plus your existing tracker may be enough, and adding Cognibl would be process for its own sake. Our DATS consulting method starts with exactly that kind of diagnosis before recommending any tool.

Can Cognibl and Devin be used together?

Cognibl and Devin are separate products with no integration between them described on either live page, so using both means running two records. A team could let Devin produce pull requests and have a person attach proof to the matching Cognibl task, since Cognibl applies the same proof rule to people and to agents. Whether that overhead is worth it depends on how much assurance completion needs.

Running two records adds reconciliation work, so it is worth deciding which one is authoritative. The Cognibl workspace runs its own agents on Claude Code and Codex, so the simpler path is usually to choose the agent runtime that fits the record you need, rather than bolt a record onto a runtime that was not built around it.

Can a Devin session merge its own pull request?

Devin can merge where a deployment permits it. Cognition's GitHub guide recommends branch protection rules so that all required checks pass before Devin can merge changes, and Devin Review lets a reviewer merge or toggle auto-merge. In Cognibl, agents cannot push to the base branch, and only a person can merge, which moves the task one status forward rather than to done.

If you run Devin, the practical step is to treat branch protection and required reviews as mandatory rather than recommended, and to decide explicitly whether auto-merge is allowed for agent-authored pull requests. If you want that separation enforced by the work tool itself, that is the design choice set out in our introduction to Cognibl.

Is Devin's Session Insights a substitute for a proof of done?

Session Insights is a diagnostic for improving Devin sessions, not a completion record. Devin's documentation describes it as an analysis of what happened in a session, with an issue timeline, a size classification and an improved prompt. A proof of done in Cognibl is a different artefact: a CSV and referenced documents attached to the task before its status can change.

Both are useful, for different readers. Session Insights helps the engineer who wrote the prompt do better next time. A proof attached to a task helps the person who has to accept or audit the work, which is why Cognibl's done gate sits on the task rather than the run.

Want to see the proof rule on a real board? Explore Cognibl for agent teams, book an AI placement diagnostic, read how we scope AI developer productivity, or see our approach to placing agents inside engineering teams.

Written by the Dilr.ai engineering team, practitioners who ship enterprise AI in production. Follow us on LinkedIn for shipping notes, or subscribe via the RSS feed.