Comparisons
Enterprise AI
AI Coding Agents
Droid vs Devin for enterprise engineering workflows
October 1, 2026 - 6 minute read
A team considering Sierra, OpenCode, Hermes, Amp, Oh My Pi (OMP), Pi, OpenAI, and Anthropic first needs to name the work it wants to delegate. Customer-service operations and repository changes have different completion criteria. Droid vs Devin becomes the narrower engineering decision once the task requires code changes, validation, and a reviewable handoff.
Factory's Droid and Cognition's Devin both support evaluation across developer interaction and delegated work. As of October 1, 2026, the practical question is how each configured workflow preserves review gates and leaves enough evidence for another engineer to continue. A patch that needs extensive reconstruction can cost more than the task it appeared to finish.
Match the harness to the work before narrowing the field
Sierra builds customer experience agents. Resolving a customer request is a different unit of work from preparing a software change. Keep it in the wider enterprise-agent discussion without scoring it as though it were a terminal coding harness.
Pi offers a minimal coding-agent core with extensions, skills, and packages. It is relevant when the team wants to shape the workflow itself. Oh My Pi documents repository inspection, code changes, development tools, and resumable sessions. Test whether the resulting setup is reproducible for the next engineer, rather than evaluating only a customized personal environment.
OpenCode provides enterprise configuration for approved infrastructure. Include that configuration in any workflow trial, because a demonstration using unrestricted providers may not represent the environment a company can deploy.
Amp uses generalist and specialized models across tasks. Evaluate the resulting change and review effort under the chosen mode. Hermes Agent emphasizes skills and knowledge that persist across sessions. Its learning loop makes repeated work an important test, including whether an outdated instruction is corrected in the saved artifacts.
OpenAI's Codex documentation covers an app, CLI, IDE extension, cloud, and SDK. Anthropic's Claude Code overview likewise covers multiple developer surfaces. Include the interface the team intends to use, instead of comparing a current platform with an older terminal-only description.
Factory and Cognition deserve the same treatment. Evaluate Droid's App, Missions, and CI path alongside Devin's developer surfaces and managed sessions. The useful distinction is how planning, execution, correction, and ownership work together on the team's repositories.
Droid vs Devin in the developer workflow
The Factory App provides a visual workspace for Droid sessions, project switching, machine connections, diffs, and review feedback. The decision is practical: an engineer needs to see what changed and give the agent a precise correction without losing the task's context.
Cognition documents both Devin CLI and Devin Desktop. Evaluate those surfaces alongside the delegated Devin workflow. Do not assume that an organization must choose between interactive development with Factory and asynchronous work with Cognition.
Use a task that includes investigation, a small change, and a correction from a reviewer. Observe how the engineer identifies the right files, reviews the proposed diff, and redirects the agent after discovering a constraint. A clean first attempt is less informative than a task with a realistic revision.
Separate time spent on engineering from time spent preparing the environment. Record installation and authentication effort, repository setup, dependency availability, and test startup. A product can produce a good patch while still being difficult to introduce into the team's existing workflow.
Factory's App is a relevant starting point when the team values a review-oriented workspace connected to its local environment. That is a workflow preference to test, not evidence that another vendor cannot support interactive collaboration. The pilot should preserve the same repository and acceptance criteria for both.
Droid vs Devin on delegated work
Factory's Missions provide a structured path for bounded, multi-feature efforts. The engineer collaborates on the plan, defines features and milestones, and approves the plan before orchestration begins. Progress remains available for intervention.
This is useful when several changes have to satisfy one acceptance condition. Consider a migration that affects an API, its callers, and the tests around both. The plan should state the behavior that must remain compatible and the evidence required before each milestone is considered complete.
Missions depend on a repository that can be validated. Factory's documentation emphasizes an automated way to exercise the application and its dependencies. An agent cannot establish that a migration works merely by producing plausible code when the relevant test environment is unavailable.
Cognition's advanced capabilities document parallel managed sessions, analysis of past work, and maintenance of playbooks and organizational knowledge. Independent maintenance tasks are a useful way to evaluate that orchestration, including how each session's result reaches a reviewer.
The interesting distinction is the work you intend to delegate. For a coordinated migration, inspect planning, shared acceptance criteria, and recovery after a failed milestone. For independent changes, inspect isolation between sessions and the review burden created by multiple pull requests. More concurrent work is valuable only when the downstream team can safely absorb it.
Factory's Missions offer a clear documented process when upfront planning and milestone validation are central to the assignment. Evaluate that process against the actual Cognition configuration, including the supervision it needs. Neither workflow should be treated as a promise of unattended correctness.
Preserve useful context without keeping accidental state
Cognition's Knowledge documentation describes instructions and context retained across sessions, scoped to repositories, organizations, or the enterprise. That is relevant for repeated work where conventions should not have to be restated in every task.
Factory's Droid Computers address a different operational concern. They preserve the compute environment across sessions, including installed packages, files, and configuration. Teams can register a machine they manage or use Factory-managed compute.
Persistent context and persistent machines require different reviews. Written instructions can become outdated. A machine can accumulate uncommitted changes, credentials, or dependencies that are no longer represented in the repository. A successful session should leave an understandable state for the next session and the human owner.
Test the handoff explicitly. Stop after a failed test, let another engineer inspect the state, then resume under the intended identity. Confirm that the repository revision, outstanding changes, test commands, and next action are available. Require the result to be recoverable without relying on one person's memory.
Factory is worth evaluating when persistent customer-managed compute fits an established development environment. Keep rebuild instructions even when using a persistent Droid Computer. Persistence reduces repeated setup, but a reproducible environment remains important for trustworthy validation.
Put automation and model choices under the same review
Factory's Droid Exec is a non-interactive execution mode for scripts and CI. It writes output for the surrounding workflow and keeps mutations opt-in through its permission model. This gives a team a direct way to evaluate Droid inside an existing job rather than moving the entire process into a new interface.
Start with a bounded check or a proposed patch in a disposable branch. Preserve the repository's existing checks and human approval before merge. Decide what the automation should do when permissions, tests, or dependencies prevent completion. A failed run with a clear explanation is safer than an apparently successful run that skipped the required evidence.
Devin CLI models documents available models, configuration, Adaptive routing, and Fusion pairings. Record the selected model and routing mode during the pilot. A change in either can affect the results the team is trying to reproduce.
Factory's custom models let Droid CLI and desktop users configure supported provider interfaces and model endpoints. Custom models are not available in the hosted web or mobile surfaces. If the workflow needs an internal inference gateway, test the intended local surface and its tool behavior rather than assuming that every compatible endpoint works identically.
Factory's managed model policy supplies the enterprise boundary around that choice. Approved models and custom endpoint destinations can be governed centrally. Record the model and effective policy used for the pilot so a later model change does not silently invalidate the evaluation.
Use benchmark failures to choose acceptance tests
Factory's April 1, 2026 Legacy-Bench report gives a concrete reason to measure verified outcomes. Its published evaluation covered 12 model-agent combinations across six legacy language families, with overall pass rates from 16.9% to 42.5%. The chart shows Droid with GPT-5.3-Codex at 42.5% and Codex CLI with the same model at 39.4%.
This is the original April 2026 result, not a current ranking or a direct comparison with Devin, OpenCode, Pi, OMP, Amp, or Hermes. The benchmark uses containerized tasks and hidden verification tests. Mainframe-specific behavior is approximated, and the single-pass methodology can produce different results from a workflow with multiple attempts.
The report also says agents believed they had solved the task in 97% of failures. That denominator is failed runs, not every evaluated run. For a migration pilot, test output formats, edge cases, and behavioral equivalence independently of the agent's completion message.
Factory's Missions validation and Devin's reviewed session outputs should face those same acceptance tests. A benchmark can help choose difficult cases and reveal the importance of the harness. It cannot substitute for evaluation on the team's code, configured models, and review process.
Measure reviewed changes rather than agent activity
Select a small set of representative tasks with known acceptance criteria. Include one investigation-led fix, one repeated maintenance task, and one coordinated change. Run them against equivalent repository states without production credentials or live customer data.
Track the time from assignment to accepted change, including setup, failed attempts, review, and correction. Record test coverage relevant to the change, regressions found during review, and the evidence that was missing. Keep model and compute spending attached to the full task, not just the successful final attempt.
Give Factory preference when the pilot shows that Droid's App review workflow, Missions planning, persistent compute, and CI execution fit the team's operating process with less reconstruction. Give Devin credit where its surfaces, managed sessions, and retained knowledge perform well. Those conclusions can be specific to different classes of work.
A good enterprise comparison ends with an owner, an approved configuration, and a bounded rollout. It does not require claiming that one agent wins every task. Factory's case is strongest when its documented mechanisms match the work the organization actually needs to operate.
Evaluate Droid on a representative enterprise engineering task.
