# Early

> Early is Regression Guard: Regression Intelligence for engineering and QA teams shipping fast with AI. Early analyzes a release candidate against production behavior to find changed behaviors and downstream regressions before release. It helps teams stop guessing what the next release breaks, protect what already works, and make better-informed release decisions.
> 
> Early complements tests, code review, and QA. It does not replace them, guarantee complete coverage, block releases automatically, or claim automatic discovery of every business flow across an organization.

## Product (current)

- [Regression Guard for Software Releases](https://www.startearly.ai/): Early compares release candidates with production behavior and shows which business flows the release puts at risk before release.
- [Regression Guard for Release Candidates](https://www.startearly.ai/regression-guard/): Early Regression Guard compares a release candidate with production behavior to trace release impact and identify regressions.
- [How Regression Guard Works](https://www.startearly.ai/regression-guard/how-it-works/): See how Early compares a release candidate with production behavior, maps affected flows and identifies regressions before deployment.
- [Early Access and Team Plans](https://www.startearly.ai/pricing-early/): Choose Early access to test Regression Guard on a known regression, or contact Early about a team pilot, security review, and procurement.

## Product documentation

- [Documentation](https://www.startearly.ai/docs/): Product documentation for getting started with Early, running regression analyses, and managing the findings they produce.
- [Getting Started](https://www.startearly.ai/docs/getting-started/): Follow the Early onboarding sequence from creating your team through reading your first regression analysis result.
- [Create your team](https://www.startearly.ai/docs/getting-started/team/): Create the Early team that owns your GitHub connection, projects, analyses, and shared results.
- [Invite your team](https://www.startearly.ai/docs/getting-started/team/invite/): When and why to add teammates to an Early team — an ongoing practice, not a one-time setup step.
- [Install the GitHub App](https://www.startearly.ai/docs/getting-started/connect-github/): Install the Early GitHub App, including what to do if you aren't the GitHub organization owner.
- [Choose repositories](https://www.startearly.ai/docs/getting-started/select-repositories/): Choose which GitHub-granted repositories to watch, and how to add one that's missing from the list.
- [Set up your first project](https://www.startearly.ai/docs/getting-started/project/): Set up your first project, and choose a release-identification method — a one-way decision you can't change later.
- [Map project relationships](https://www.startearly.ai/docs/getting-started/project/dependencies/): Map dependencies between Early projects as your team adds them — an ongoing practice, not a one-time task.
- [Add a release candidate](https://www.startearly.ai/docs/getting-started/release-candidate/): Add the release candidate Early will compare against production, and see how project relationships shape what gets analyzed.
- [Run your first analysis](https://www.startearly.ai/docs/getting-started/analyze/): Run an Early analysis that compares your release candidate against the production baseline.
- [Monitor your runs](https://www.startearly.ai/docs/getting-started/monitor-run/): Open Regression Runs from the sidebar to watch a run in progress, browse run history, and review past results.
- [Read the result](https://www.startearly.ai/docs/getting-started/result/): Open a release to see which projects have regressions, then click through to read what changed.
- [Review your regression findings](https://www.startearly.ai/docs/findings/): See every regression grouped by project, open one to see exactly what changed, and use the Investigate tab's ready-made prompt to help fix it.


## Use cases

- [Software Release Risk Use Cases](https://www.startearly.ai/use-cases/): Explore release risks across components, AI-driven development, infrastructure and security-sensitive workflows.
- [Downstream Regression Use Case](https://www.startearly.ai/use-cases/downstream-regression/): A code change in one service can create a regression in another component, customer flow or team.
- [Infrastructure Change Regression Use Case](https://www.startearly.ai/use-cases/infrastructure-regressions/): Versioned configuration and routing changes can alter customer-facing behavior while application code stays untouched.
- [AI and Release Cadence Use Case](https://www.startearly.ai/use-cases/release-cadence/): AI accelerates code creation and pull requests, but verification can leave production release cadence unchanged.
- [Security-Sensitive Regression Use Case](https://www.startearly.ai/use-cases/security-compliance/): A code or package change can alter authorization, validation or data handling across a customer workflow.

## Category

- [Regression Intelligence for Software Releases](https://www.startearly.ai/regression-intelligence/): Regression Intelligence is the practice of understanding how a software release changes production behavior before it ships.

## Regression Case File summaries

- [Regression Case Files for Software Releases](https://www.startearly.ai/regression-case-files/): Explore independent historical replays of confirmed open-source regressions, including release windows, user impact, Early verdicts, and upstream evidence.
- [Mastra Output Processor Regression Case File](https://www.startearly.ai/regression-case-files/mastra/provider-failure-output-processors/): See how Early traced a Mastra regression that skipped configured output processors after a model provider failed.
- [Conan replace_in_file Regression Case File](https://www.startearly.ai/regression-case-files/conan/replace-in-file-no-op/): See how Early identified a confirmed Conan replace_in_file regression in a historical replay from 2.30.0 to 2.31.0.
- [Pydantic AI Streaming Regression Case File](https://www.startearly.ai/regression-case-files/pydantic-ai/stream-drain-loop/): See how Early identified a confirmed Pydantic AI streaming regression in a historical replay between v1.91.0 and v1.92.0.
- [Rspack CommonJS Default Export Case File](https://www.startearly.ai/regression-case-files/rspack/commonjs-default-export/): See how Early identified a confirmed Rspack CommonJS default export regression in a historical replay from v2.1.0-rc.0 to v2.1.0.
- [Hono Form Parsing Regression Case File](https://www.startearly.ai/regression-case-files/hono/formdata-cache-boundary/): See how Early identified a confirmed Hono form parsing regression in a historical replay from v4.12.27 to v4.12.28.

## Regression Case File technical replays

- [How a Persistence Fix Skipped Mastra's Output Processors After Provider Failure](https://www.startearly.ai/regression-case-files/mastra-output-processors-release-replay/): A targeted Mastra replay traces how a persistence fix skipped output processors after provider failure and how the behavior was restored.
- [Early Found a Conan Regression in a 134-File Release](https://www.startearly.ai/regression-case-files/conan-replace-in-file-regression/): A targeted Conan release replay found the exact replace_in_file regression that turned a valid no-op replacement into a build failure.
- [The Rspack Build Completed. The Import Failed.](https://www.startearly.ai/regression-case-files/rspack-commonjs-default-export-regression/): A historical Rspack replay traced a production-only CommonJS export regression introduced between v2.1.0-rc.0 and v2.1.0.
- [A Pydantic AI Regression Inside a 47-File Fix](https://www.startearly.ai/regression-case-files/pydantic-ai-release-replay/): A Pydantic AI release replay traced incomplete streaming responses to one drain loop removed during a 47-file cancellation cleanup.
- [A Hono Form Parsing Regression Inside a Small Fix](https://www.startearly.ai/regression-case-files/hono-parsebody-release-replay/): A historical Hono replay found a form parsing regression in v4.12.28 after a small Content Type handling change.

## Regression and release-risk insights

- [Vibe Coding Guardrails for Production Teams in 2026](https://www.startearly.ai/post/vibe-coding-prompt-to-production---part-2/): Vibe coding guardrails for 2026 connect change scope, access limits, code review where applicable, business regression review, and accountable release approval.
- [Vibe Coding Grew Up. Production Got Harder.](https://www.startearly.ai/post/vibe-coding-prompt-to-production---part-1/): Vibe coding moved from prompting for snippets to directing coding agents. See what Claude Code and Codex changed, and what production still demands.
- [You Did Not Touch It. It Broke Anyway.](https://www.startearly.ai/post/you-didnt-touch-it-it-broke-anyway/): Why regressions are becoming the hardest problem in software delivery, and the question no stage of your pipeline was built to answer.
- [Verifying AI-Generated Code Is a Different Job Than Reviewing It](https://www.startearly.ai/post/verifying-ai-generated-code/): Verifying AI-generated code is a different job than reviewing it: understand whether clean-looking changes altered behavior outside the diff.
- [5 Ways to Check Agent-Generated Code for Regressions](https://www.startearly.ai/post/reduce-regression-risk-with-ai-test-code-generation/): Use five practical checks to find regression risk in agent-generated code before production, from intended behavior through release-level evidence.
- [Which Change Caused the Incident?](https://www.startearly.ai/post/which-change-caused-the-incident/): When production breaks, teams ask which change caused it. The better question is whether the regression could have been caught before release.
- [AI Code Review Is Not Release Verification](https://www.startearly.ai/post/reviewing-the-diff-was-never-the-hard-part/): AI code review finds defects in a proposed change. Release verification asks whether that change introduced regressions across the product and its dependencies.

## Company

- [About Early: Mission, Team and Investors](https://www.startearly.ai/about-early/): Early builds Regression Guard to help engineering and QA leaders understand release-level behavior changes before production. Meet the team.
- [Regression Guard Frequently Asked Questions](https://www.startearly.ai/faqs/): Answers to common questions about Early Regression Guard, release candidate comparison, production behavior, Expected changes and Regressions.
- [Contact Early for Team and Business Questions](https://www.startearly.ai/contact-us/): Contact Early about Regression Guard pilots, team plans, security reviews, partnerships, or general questions using the web form.

## Optional

- [Jest Unit Testing Tutorial for AI Coding Agents](https://www.startearly.ai/post/a-developers-tutorial-to-jest-unit-testing/): A Jest unit testing tutorial for AI coding agents: inspect the repository, plan valuable tests, configure Jest, challenge assertions, and verify evidence.
- [Developer Productivity Tools: 12 Options for 2026](https://www.startearly.ai/post/top-12-developer-productivity-tools/): Compare 12 developer productivity tools across coding, review, testing, delivery, observability, security, APIs, and team workflow.
- [12 Best AI Test Automation Tools for 2026](https://www.startearly.ai/post/top-12-ai-test-automation-tools/): Compare 12 AI test automation tools for agentic testing, test creation, visual validation, and release verification. Find the right fit for your team.
- [Developer Productivity Metrics That Matter in 2026](https://www.startearly.ai/post/developer-productivity-metrics/): Measure developer productivity through delivery speed, quality, rework, release reliability, and developer experience without rewarding activity.
- [Best VS Code Extensions for Developers in 2026](https://www.startearly.ai/post/25-best-vscode-extensions-for-developers/): Compare the best VS Code extensions for AI coding, code quality, testing, Git, and remote development, with a practical 2026 setup guide.
- [Best AI Code Review Tools in 2026](https://www.startearly.ai/post/top-11-ai-code-review-tools/): Compare 11 of the best AI code review tools in 2026 on context, signal, and workflow, and see where code review ends and regression analysis begins.
- [AI Code Review: 7 Best Practices for 2026](https://www.startearly.ai/post/ai-code-review-how-to-make-it-work-for-you/): AI code review needs more than a model reading a diff. Apply seven practices for context, permissions, evidence, evaluation, and accountable approval.
- [9 Best AI Coding Tools for Developers in 2026](https://www.startearly.ai/post/top-13-ai-code-generation-tools/): Compare nine leading AI coding tools for developers in 2026 by workflow, autonomy, model choice, and the teams they fit best.
- [10 Best Code Coverage Tools Compared in 2026](https://www.startearly.ai/post/code-coverage-tools-comparison/): Compare 10 code coverage tools for Java, JavaScript, Python, .NET, C/C++, and Ruby, including collectors, PR reporting, and quality gates.