# Baz

> Baz is an engineering review platform for implementation plans, pull requests, security findings, and merge decisions, reading your whole codebase to catch what other tools miss. Enterprise-grade, self-hosted available.

Baz is an engineering review platform for implementation plans, pull requests, security findings, and merge decisions. Purpose-built agents - code review, spec review, advanced security, SRE, and an automated fixer - operate with full repository and production context, grounded in a retrieval engine that connects ticket intent, architecture, and change context to the exact code behind each task.

## Product
- [Planner](https://baz.ai/planner): How Baz finds the decisions behind your codebase and steers agents to build it your way.
- [Baz AI Code Review - Automated Agents & Precision Review](https://baz.ai/product/agents): Automated AI agents review code with full repository context. Reduce review time and catch bugs before merge. Specialized agents for security, correctness, and compliance.
- [Baz Review Sessions - Transparent Code Review Execution](https://baz.ai/product/sessions): Inspect every agent execution with full visibility. See which agents ran, what code they analyzed, and trace findings to their source. Reviewer, Fixer, Merger, Planner, security, and SRE runs, all inspectable.
- [Baz Review Workspace - Pull Request Management Interface](https://baz.ai/product/pull-requests): Complete PR review workspace: view changes, resolve comments, validate merge readiness, and approve all in one place. Reduce review rounds and ship faster.
- [Baz Insights - Code Review Metrics & Dashboards](https://baz.ai/product/insights): Measure code quality and delivery velocity with Insights. Track agent performance, bugs caught, review metrics, and quantify rework savings.
- [Baz Automation Controls - Review Workflow Configuration](https://baz.ai/product/configuration): Configure automated review workflows: set triggers, auto-resolve, auto-approve rules. Automate routine reviews while keeping critical decisions human-driven.
- [Baz Merger - Autonomous Merge Decisions With Stats](https://baz.ai/product/merger): Merger evaluates open pull requests for merge readiness and clears stale queues autonomously where it's safe to. Merger Agent Stats shows the track record behind every verdict.
- [Pricing](https://baz.ai/pricing): Seat-based pricing plus usage-based Engineering Work Credits.
- [Enterprise](https://baz.ai/enterprise): Controls and deployment options for large organizations.

## Documentation
- [Getting Started](https://baz.ai/docs/getting-started): Ensure your team makes the most of Baz.
- [Organization Setup](https://baz.ai/docs/basics/organization-setup): To connect Baz to your account, follow these steps.
- [Configure with GitHub](https://baz.ai/docs/basics/configure-with-github): Connect Baz to your GitHub organization and repositories.
- [Configure with GitLab](https://baz.ai/docs/basics/configure-with-gitlab): Connect Baz to your GitLab projects.
- [Configure with Azure DevOps](https://baz.ai/docs/basics/configure-with-azure-devops): Connect Baz to your Azure DevOps organization and repositories.
- [Plugins](https://baz.ai/docs/basics/plugins): Install the Baz plugin in Claude Code, OpenAI Codex, or Cursor.
- [Claude Code](https://baz.ai/docs/basics/plugins/claude-code): Install the Baz plugin in Claude Code.
- [OpenAI Codex](https://baz.ai/docs/basics/plugins/codex): Install the Baz plugin in the OpenAI Codex CLI.
- [Cursor](https://baz.ai/docs/basics/plugins/cursor): Set up Baz in Cursor with the exploration rule and the MCP server.
- [CLI](https://baz.ai/docs/basics/plugins/cli): Install and use the Baz CLI for AI-assisted manual code review in the terminal.
- [Integrations](https://baz.ai/docs/basics/integrations): Tools Baz integrates with, including issue trackers, chat, and design tools.
- [Preview Environments](https://baz.ai/docs/basics/integrations/preview-environments): How to set up, connect, and use preview environments in Baz
- [Sandbox](https://baz.ai/docs/basics/integrations/sandbox): How to set up, connect, and use a Sandbox - Baz hosted environment
- [Jira](https://baz.ai/docs/basics/integrations/jira): Baz’s Jira Integration
- [Linear](https://baz.ai/docs/basics/integrations/linear): Baz's Linear Integration
- [Datadog](https://baz.ai/docs/basics/integrations/datadog): Baz can use Datadog as the observability source for the SRE agent.
- [Sentry](https://baz.ai/docs/basics/integrations/sentry): Baz can use Sentry as the observability source for the SRE agent.
- [Figma](https://baz.ai/docs/basics/integrations/figma): Baz’s Figma Integration
- [Slack](https://baz.ai/docs/basics/integrations/slack): Baz's Integration for Slack
- [Monday](https://baz.ai/docs/basics/integrations/monday): Baz's Monday Integration
- [Shortcut](https://baz.ai/docs/basics/integrations/shortcut): Baz's Shortcut Integration
- [Azure DevOps - Boards](https://baz.ai/docs/basics/integrations/azure-devops-boards): Baz's Azure DevOps Boards Integration
- [Youtrack](https://baz.ai/docs/basics/integrations/youtrack): Baz integration guide for Youtrack
- [Fibery](https://baz.ai/docs/basics/integrations/fibery): Baz integration guide for Fibery
- [Notion](https://baz.ai/docs/basics/integrations/notion): Baz's Notion Integration
- [Google Docs](https://baz.ai/docs/basics/integrations/google-docs): Baz can use Google Docs as a connected documentation source.
- [Baz Agents](https://baz.ai/docs/agents/baz-agents): How Baz's AI review agents analyze pull requests across the full repository.
- [Customized Agents](https://baz.ai/docs/agents/customized-agents): Recommended and custom AI reviewers that enforce your team's engineering standards.
- [Recommended Reviewers](https://baz.ai/docs/agents/customized-agents/recommended-reviewers): How Baz turns recurring PR feedback into reusable AI reviewers.
- [Custom Reviewers](https://baz.ai/docs/agents/customized-agents/custom-reviewers): Your prompts power new code review agents.
- [Working with Agents](https://baz.ai/docs/agents/working-with-agents): Baz Reviewer performs a comprehensive code review of open pull request.
- [Skills & Instructions](https://baz.ai/docs/agents/skills-and-instructions): Which agent rule files, Skills, and Cursor rules Baz reads, how it turns them into coding guidelines, and exactly how they are enforced on a pull request.
- [Planner](https://baz.ai/docs/capabilities/planner): Generate a repository-aware implementation plan before code is written, then review and approve it.
- [Pull Requests](https://baz.ai/docs/capabilities/pull-requests): The main review workspace in Baz.
- [Secure AI Code](https://baz.ai/docs/capabilities/secure-ai-code): The Baz security agents: Basic Security Patterns, Advanced Security with SAST-inside, and Fixer.
- [Sandboxed Application Review](https://baz.ai/docs/capabilities/sandboxed-application-review): Let Spec Reviewer launch the application from the Change branch inside an isolated sandbox and validate the running implementation against the specification.
- [Merge Automation](https://baz.ai/docs/capabilities/merge-automation): Merger, auto approve, and auto resolve: how Baz acts on merge readiness instead of only reporting it.
- [Sessions](https://baz.ai/docs/capabilities/sessions): Sessions provide visibility into every Baz agent execution, from reviewer runs to Fixer, Merger, Planner, security scans, and SRE.
- [Evaluation](https://baz.ai/docs/insights/evaluation): How agents are performing across every review: acceptance, coverage, and Merger's track record.
- [Interactions](https://baz.ai/docs/insights/interactions): Every response your team gave a Baz review comment, filterable and exportable.
- [Engineering Impact](https://baz.ai/docs/insights/engineering-impact): Baz's effect on bugs caught, bugs reported, and how long changes sit in review.
- [Enterprise Hosting](https://baz.ai/docs/account/private-mode): Deployment options and enterprise controls: Baz Cloud, Private Mode, and self-hosted, plus SSO, admin controls, and procurement.
- [Notifications](https://baz.ai/docs/account/notifications): Stay updated on activity across your code reviews from the Notifications Center.
- [Configurations](https://baz.ai/docs/account/configurations): Every setting under Baz Settings, Configurations: what it does and what it defaults to.
- [Billing](https://baz.ai/docs/account/billing): Manage your subscription, payment information, invoices, and usage limits.
- [Security, privacy and compliance](https://baz.ai/docs/account/security-privacy-and-compliance): How Baz secures infrastructure, code, and customer data, and how to report a security concern.

## Blog
> Perspectives and product thinking from the Baz team.
- [Awesome Reviewers, rebuilt: instructions across AI and infrastructure domains](https://baz.ai/resources/blog/awesome-reviewers-rebuilt-around-ai-and-infrastructure-domains): We rebuilt awesomereviewers.com around the domains people actually build in, gave every instruction a date derived from its source discussions, and made the whole corpus fetchable as raw text. Here is the refresh, and the road that led to it.
- [SAST-inside: expanding the hypothesis space of agentic security review](https://baz.ai/resources/blog/sast-inside-expanding-the-hypothesis-space-of-agentic-security-review): SAST-inside adds a second source of vulnerability hypotheses to Baz Advanced Security. Specialized static checks nominate candidate conditions in the changed code, and the agent investigates each one against the repository before it becomes a finding.
- [Spec Reviewer now runs your application before reviewing it](https://baz.ai/resources/blog/spec-reviewer-now-runs-your-application-before-reviewing-it): Spec Reviewer can now launch the application from the Change branch inside an isolated sandbox, open it in a real browser, and validate the running implementation against the specification. Private application review, which runs that sandbox inside your own Kubernetes environment, is available as a limited technical preview.
- [Announcing Azure DevOps Support](https://baz.ai/resources/blog/announcing-azure-devops-support): Baz now connects directly to Azure DevOps, bringing agentic code review to teams that build on Azure Repos.
- [How to know when an autonomous merge agent is working](https://baz.ai/resources/blog/how-to-know-when-an-autonomous-merge-agent-is-working): Merger Agent Stats tracks evaluated PRs, ready-to-merge rate, merged-without-human-input rate, outcomes, and throughput, so trust in Merger is backed by numbers.
- [Code review needs an inbox](https://baz.ai/resources/blog/code-review-needs-an-inbox): PR Inbox adds a My pull requests tab to Changes, with Waiting for review and My open PRs sections, so developers stop hunting repo lists to find what needs their attention.
- [Usage-based AI needs budget controls](https://baz.ai/resources/blog/usage-based-ai-needs-budget-controls): Account settings now show live usage, let admins set spend limits, and break down Fixer costs, so usage-based pricing comes with real guardrails.
- [Approve the plan before the code exists](https://baz.ai/resources/blog/approve-the-plan-before-the-code-exists): Plans generated by Planner now go through a dedicated approval workflow, with versions, section comments, and Mermaid diagrams, before implementation starts.
- [Agent work should be inspectable](https://baz.ai/resources/blog/agent-work-should-be-inspectable): Baz Sessions now trace Planner, Merger, Spec Reviewer, and full repo security scans, not just PR reviews.
- [Security review should not wait for the next pull request](https://baz.ai/resources/blog/security-review-should-not-wait-for-the-next-pull-request): Full codebase security scans find risk already sitting in the repository, tracked as Sessions with severity-tagged findings and fixer PRs.
- [Bringing agentic code review to Azure Repos](https://baz.ai/resources/blog/bringing-agentic-code-review-to-azure-repos): Baz now supports admin-scoped project and repository selection for Azure Repos, so Azure DevOps teams get the same agentic review workflows as GitHub.
- [Measuring the review loop before and after Baz](https://baz.ai/resources/blog/measuring-the-review-loop-before-and-after-baz): Engineering Impact's pickup time and time-to-merge graphs now support a pre-Baz baseline, so teams can see review velocity change, not just its current state.
- [Session Logs: the execution record for Baz agents](https://baz.ai/resources/blog/session-logs-the-execution-record-for-baz-agents): Session Logs give every Baz run a structured execution record. They show how a run was triggered, where it executed, its current state, major completed stages, outcome, and cost. Instead of exposing raw internal logs, Baz presents a timeline of product events so teams can inspect reviewer runs, fixer runs, scheduled scans, benchmark runs, and future Codex or Claude Code workflows from one place.
- [Auto Fix and Auto Resolve Cut PR validation time](https://baz.ai/resources/blog/auto-fix-and-auto-resolve-cut-pr-validation-time): Auto Approve issues a formal PR approval once every Baz comment is resolved and the corresponding fixes are verified against linters and CI. Auto Resolve closes review threads only after the system correlates a verified transform to the original comment and confirms required checks are green. Both features are governed by admin-configurable rules, supported-language lists, and confidence thresholds, and they emit immutable metadata, reverts, and telemetry to help teams reduce manual validation and advance toward an autonomous codebase.
- [Advanced Security Reviewer: security review for the cyber-capable model era](https://baz.ai/resources/blog/advanced-security-reviewer-security-review-for-the-cyber-capable-model-era): Baz Advanced Security Reviewer uses cyber-capable models, Agent Harness, and Context Broker to find exploitable security issues in code changes with real codebase context.
- [cyber.md: AI-native posture that speaks agent](https://baz.ai/resources/blog/cyber-md-ai-native-posture-that-speaks-agent): cyber.md is a proposed Markdown-based security posture file that helps coding agents preserve security intent during normal development workflows. Rather than acting as a vulnerability report or attack playbook, cyber.md captures protected assets, trust boundaries, coding invariants, defensive patterns, and testing expectations in a format agents can consume safely and contextually.
- [Getting Ready for Cyber-Capable Models Like Mythos](https://baz.ai/resources/blog/getting-ready-for-cyber-capable-models-like-mythos): Our short take on why cyber-capable models like Mythos will disrupt classic code management. We argue that the real shift is from diff inspection to consequence analysis: models now reason across migrations, shared types, schemas, and interfaces to surface logical bugs, breaking changes, and downstream impact.
- [SRE Agent: Baz listens to production and opens the fix PR for you](https://baz.ai/resources/blog/sre-agent-baz-listens-to-production-and-opens-the-fix-pr-for-you): We just shipped a new Baz coding agent, SRE, that listens to traces and logs from APM (Application Performance Monitoring), maps them back to the exact code that caused them, and opens concise fix pull-requests.
- [A complete guide to building autonomous repos in GitHub](https://baz.ai/resources/blog/a-complete-guide-to-building-autonomous-repos-in-github): This guide shows how to introduce autonomy into pull request workflows safely - covering configuration, guardrails, and staged authority so teams can automate without creating irreversible risk.
- [Baz #1 in Precision in independent Code Review Bench](https://baz.ai/resources/blog/baz-1-in-precision-in-independent-code-review-bench): Baz #1 in Precision in independent Code Review Bench
- [Baz CLI: Interactive code reviews](https://baz.ai/resources/blog/baz-checkout-live-local-and-intelligent-code-validation): Today we are introducing the Baz CLI. The Baz CLI introduces an interactive review loop in the terminal, helping developers walk through changes, review them against requirements, ask questions, and resolve issues faster before merge.
- [One agent to control all Rule Files](https://baz.ai/resources/blog/one-agent-to-control-all-rule-files): My clear, practical take on the agent rule file problem: why Markdown sprawl and token bloat happen, and how Baz fixes it by turning agent prompts into a single, versioned policy that is translated into tool-specific rule files for consistent, auditable agent behavior.
- [Fixer Agent - Stop wasting time on routine PR fixes](https://baz.ai/resources/blog/fixer-agent-stop-wasting-time-on-routine-pr-fixes): Fixer automates the compose→verify→commit loop for small PR fixes by running code inside an ephemeral sandbox that reproduces the repo environment and uses lockfile-aware dependency installs.
- [Introducing Private Mode](https://baz.ai/resources/blog/introducing-private-mode): When Private Mode is enabled, Baz analyzes your changes locally and does not store or retain your source code. This is designed for teams with strict security, compliance, or data residency requirements who still want deep, structured code review.
- [Pixel Perfect by Baz - Make designs and implementations match. Every time.](https://baz.ai/resources/blog/pixel-perfect-by-baz): Automated visual review that surfaces missing UI elements and tooltip regressions and maps them to code.
- [Introducing Fixers: Environment-compatible auto-fixing agents](https://baz.ai/resources/blog/fixer): Fixer is a sandboxed auto-fix agent that applies and executes PR fixes inside an ephemeral, isolated runtime. It runs format, lint and build checks and only commits changes when validation succeeds, saving reviewers hours reworking incoming feedback.
- [Spec Reviewer Design Spotlight](https://baz.ai/resources/blog/spec-reviewer-design-spotlight): Spec Reviewer Design Spotlight turns screenshot evidence into a single, review-ready artifact for each requirement.
- [The AI Coding Loop Hits  Runtime](https://baz.ai/resources/blog/the-ai-coding-loop-hits-runtime): Developers found flow in the IDE, now agents are chasing truth in execution.
- [From Bots to Maintainers: What Bot Comments Teach Us About AI Code Review](https://baz.ai/resources/blog/from-bots-to-maintainers-what-bot-comments-teach-us-about-ai-code-review)
- [Spec Reviewer: Bringing Figma and Jira Context into Every Pull Request](https://baz.ai/resources/blog/spec-reviewer-bringing-figma-and-jira-context-into-every-pull-request): By embedding design and product understanding directly into review, Baz removes an entire layer of back-and-forth between design, engineering, and QA.
- [Engineering Intuition at Scale: The Architecture of Agentic Code Review](https://baz.ai/resources/blog/engineering-intuition-at-scale-the-architecture-of-agentic-code-review)
- [What The Top Open Source Projects Teach Us About AI Code Review](https://baz.ai/resources/blog/what-the-top-open-source-projects-teach-us-about-ai-code-review): What do pull request trends and comments from projects like LangChain, Kubeflow, Terraform, PyTorch, and Spring tell us?
- [Code Review Comments to Dev Standards: 4 Internal Code Prompts We Turned Into AI Reviewers](https://baz.ai/resources/blog/from-code-review-comments-to-dev-standards-4-internal-code-prompts-we-turned-into-ai-reviewers): Every engineering team has unspoken rules, the kind of standards you’ve repeated in PRs a hundred times but never bothered to write down. With AI code reviewers, those patterns don’t need to stay scattered.
- [5 Ways to Measure the Impact of AI Code Review](https://baz.ai/resources/blog/5-ways-to-measure-the-impact-of-ai-code-review): How to track outcomes, developer sentiment, and real ROI for your AI
- [Code reviews are broken: How GitHub’s poor UX Is hurting developers](https://baz.ai/resources/blog/code-reviews-are-broken): Why GitHub's outdated code review experience is frustrating developers and stifling progress. Explore the key flaws in its workflow and what needs to change for modern development.
- [From Review Thread to Team Standard: How We Built AwesomeReviewers](https://baz.ai/resources/blog/from-review-thread-to-team-standard-how-we-built-awesomereviewers): AwesomeReviewers demonstrates how code review discussions can turn into guidelines that capture the team’s collective wisdom. In this post, we’ll share how it works under the hood, and what we learned building it.
- [Building an AI Code Review Agent: Advanced Diffing, Parsing, and Agentic Workflows](https://baz.ai/resources/blog/building-an-ai-code-review-agent-advanced-diffing-parsing-and-agentic-workflows): Enhancing AI-driven code reviews with Git Diff, Difftastic, and Tree Sitter. Discover how syntax-aware diffing, structured parsing, and historical tracking improve AI’s ability to understand and optimize code changes.
- [AI Code Review: Baz is now available, backed by $8million Seed Round](https://baz.ai/resources/blog/ai-code-review-baz-is-now-available-backed-by-8million-seed-round): We're finally making Baz available for everyone. An AI Code Reviewer that helps teams build great software in the age of AI.
- [Baz Reviewer: One More Step Towards Automated Code Review](https://baz.ai/resources/blog/baz-reviewer-one-more-step-towards-automated-code-review): Explore Baz Reviewer, an AI-powered tool that enhances code reviews by providing context-aware, actionable feedback.
- [Beyond LGTM: Master Code Reviews with AI](https://baz.ai/resources/blog/beyond-lgtm-master-code-reviews-with-ai): A streamlined approach to code review - focused on clarity, precision, and outcomes - transforms the process into a faster, more effective workflow that drives stable releases.
- [Copilot vs. Code Quality: Are Current Metrics Enough to Define AI Success?](https://baz.ai/resources/blog/copilot-vs-code-quality): Copilot vs. Code Quality: Are Current Metrics Enough to Define AI Success?
- [How to Achieve Structured Output in Claude 3.7: Three Practical Approaches](https://baz.ai/resources/blog/how-to-achieve-structured-output-in-claude-3-7-three-practical-approaches): Achieving reliable structured output in production can be tricky, especially with extended thinking mode. By using approaches like no thinking mode, careful prompting, or combining reasoning with a separate structuring model, you can effectively work around these limitations and integrate structured outputs into your GenAI applications.
- [Meet Bazzy: The AI Code Review Assistant That Understands Your Code Like Your Team's Best Reviewer](https://baz.ai/resources/blog/meet-baazzy-the-ai-code-review-assistant): Bazzy is the AI code review assistant that provides context-aware suggestions by understanding complex, multi-repo, multi-language code bases. Streamline your code reviews with faster insights, fewer errors, and seamless collaboration.
- [Practical AI: 5 Real-World Code Review Wins](https://baz.ai/resources/blog/practical-ai-5-real-world-code-review-wins): Examining five real-world scenarios that demonstrate how AI already helps developers maintain coding standards, reduce downstream issue, and deploy faster.
- [Scaling AI feedback onto Codebase Context: A Primer on RAG Code Reviews](https://baz.ai/resources/blog/scaling-ai-feedback-onto-codebase-context-a-primer-on-rag-code-reviews): Discover how Baz Reviewer leverages Retrieval-Augmented Generation (RAG) to transform code review. Learn how dynamic retrieval, semantic analysis, and AI-powered collaboration help developers detect redundancy, enforce best practices, and streamline reviews.
- [The Next Evolution in Code Review: Tracing-Driven Insights](https://baz.ai/resources/blog/the-next-evolution-in-code-review-tracing-driven-insights): Learn how integrating telemetry, OpenTelemetry, and AI-driven insights can enhance code reviews and optimize development workflows. Explore how Baz is leading this evolution in software development practices.
- [Too Many (Unsung) Heroes – The AI Echo Chamber](https://baz.ai/resources/blog/too-many-unsung-heroes-the-ai-echo-chamber): AI-generated content is flooding the internet, and models are increasingly being trained on their own outputs. This self-reinforcing cycle is creating an echo chamber - one where originality diminishes, clichés reign, and the quality of AI-generated insights declines over time.
- [Where AI succeeds and fails in predicting breaking changes in code review](https://baz.ai/resources/blog/where-ai-succeeds-and-fails-in-predicting-breaking-changes-in-code-review): Skilled code reviewers play a crucial role in development, but even the best can miss breaking changes. AI has the potential to enhance code reviews, but existing tools fall short.
- [Getting the Right Context: Why AI Code Assistants Fall Short in Complex Codebases](https://baz.ai/resources/blog/why-ai-code-assistants-fall-short): AI code assistants like Copilot promise efficiency but fall short in multi-repo, multi-language projects due to context gaps. Learn how tracing and observability can bridge these gaps, improving code reviews and productivity.
- [Why Your AI Code Gen Doesn’t Understand Diffs](https://baz.ai/resources/blog/why-your-code-gen-ai-doesnt-understand-diffs): Traditional diffs make code reviews noisy and inefficient. AST diffing offers a smarter way to detect meaningful changes, reduce false positives, and improve developer workflows. Learn how Baz is streamlining code review with AST-based analysis.

## News
> Announcements, releases, and company updates.
- [Baz Is Now Available on AWS Marketplace](https://baz.ai/resources/news/baz-is-now-available-on-aws-marketplace): Baz is now listed on the AWS Marketplace, so organizations that procure software through AWS can subscribe to Baz against their existing AWS billing and committed spend.
- [Baz Announces Planner and an Extended Seed Round Co-Led by Battery Ventures and boldstart ventures](https://baz.ai/resources/news/baz-announces-planner-and-extended-seed-round): At AI Engineer World's Fair, Baz announced Baz Planner - a gateway that eliminates entire classes of bugs and vulnerabilities in code planning - alongside $9M in new funding, bringing total raised to $17M.
- [Baz is building agents for the autonomous codebase with Datadog LLM Observability](https://baz.ai/resources/news/baz-is-building-agents-for-the-autonomous-codebase-with-datadog-llm-observability): How Baz uses Datadog LLM Observability to run autonomous agents safely in production - with end-to-end trace coverage, human-in-the-loop validation, and ~80% faster root cause analysis.
- [How Baz improved its AI Agent Code Review accuracy using Amazon Bedrock AgentCore](https://baz.ai/resources/news/how-baz-improved-its-ai-agent-code-review-accuracy-using-amazon-bedrock-agentcore): Baz wanted to automate this missing layer of verification, bringing intent, behavior, and implementation into a single review workflow
- [LSports cuts time-to-merge by 26% and ships 5.3x more deployments after fast-tracking Baz as their LLM-as-a-Judge for code](https://baz.ai/resources/news/lsports-cuts-time-to-merge-by-26-and-ships-5-3x-more-deployments-after-fast-tracking-baz-as-their-llm-as-a-judge-for-code): LSports - one of the most technologically advanced engineering organizations in real-time sports data - moved early and decisively on agentic coding tooling, partnering with Baz to deploy an LLM-as-a-Judge layer across 241 repositories. The result: a 5.3x increase in deployment frequency, a 7.1-point lift in merge rate, and a 26% reduction in P90 lead time on their slowest, most complex pull requests. Over the same four-month window (Dec 2025-Mar 2026), Baz caught 214 real bugs - including 211 production-impacting issues and 3 security vulnerabilities.
- [SpotDraft scales high-signal code review with Baz](https://baz.ai/resources/news/spotdraft-scales-high-signal-code-review-with-baz): Replaces CodeRabbit with production-aware reviews
- [PayEm reduced bugs by 50% in 2 months with Baz](https://baz.ai/resources/news/payem-reduced-bugs-by-50-in-2-months-with-baz): After introducing code generation agents into their SDLC, the R&D team at PayEm began looking for a solution to ensure code quality and increase development velocity.
- [Spec Reviewer now embeds requirements directly into code review](https://baz.ai/resources/news/spec-reviewer-now-embeds-requirements-directly-into-code-review): Today we’re extending the same model to Spec Reviewer. Unmet requirements now appear inline, pinned to the exact code paths that cause them, and reviews include screenshots from the agent’s sandbox session showing the evaluated behavior.
- [Baz Spec Reviewer: Bring Product Requirements Into Backend Review](https://baz.ai/resources/news/baz-spec-reviewer-bring-product-requirements-into-backend-review): Baz’s Spec Reviewer for Backend loads a running environment and verifies backend behavior against the constraints found in Jira tickets, technical requirements, and product specifications.
- [Introducing Awesome Reviewers: A Public Library of AI-Powered Prompts Based on the Top Open Source GitHub Repos](https://baz.ai/resources/news/introducing-awesome-reviewers-a-public-library-of-ai-powered-prompts-based-on-1-000-github-repos): A public resource of reusable code review prompts extracted from real-world open-source projects. Designed to help anyone from OSS maintainers to AI agent builders embed best practices into their workflows without starting from scratch.
- [Launching the New Baz Evaluations: From Metrics to Action](https://baz.ai/resources/news/launching-the-new-baz-evaluations-from-metrics-to-action)
- [AI Code is Everywhere - Baz Gives Pull Requests the Overhaul GitHub Won’t with $8M Seed Round](https://baz.ai/resources/news/ai-code-is-everywhere-baz-gives-pull-requests-the-overhaul-github-wont-with-8m-seed-round): Developers are increasingly leaning on AI to write and test code, Baz makes sure code changes never break running applications.
- [Turn Past PRs Into Code Review Agents: Introducing Custom Reviewers by Baz](https://baz.ai/resources/news/turn-past-prs-into-code-review-agents-introducing-custom-reviewers-by-baz): Introducing Custom Reviewers, powered by your team’s own pull request history. Paired with the Prompt Playground, this lets you encode, test, and evolve your team’s engineering brain into a system of principles that AI can actually use.
- [Baz Releases: RAG-powered AI Code Review, deduplications, and improved workflow customization](https://baz.ai/resources/news/baz-releases-rag-powered-ai-code-review-deduplications-and-improved-workflow-customization): Discover how Baz Reviewer leverages Retrieval-Augmented Generation (RAG) to transform code review. Learn how dynamic retrieval, semantic analysis, and AI-powered collaboration help developers detect redundancy, enforce best practices, and streamline reviews.
- [Hello World from Baz: Transforming Code Reviews with AI-Powered Context](https://baz.ai/resources/news/hello-world): Discover Baz, an AI-driven source control tool designed to simplify code reviews and empower developers. Experience context-rich insights, streamlined collaboration, and smarter code management with Baz.
- [Introducing Module Memory: Smarter, More Contextual Code Reviews](https://baz.ai/resources/news/introducing-module-memory-smarter-more-contextual-code-reviews): Discover how Module Memory enhances code reviews through structured, persistent context. See functional shifts, reduce cognitive load, and improve AI-assisted workflows - see this in action with the Change Request Graph in Baz.
- [AI Code Review in Any IDE, Securely with Baz MCP Server](https://baz.ai/resources/news/review-any-code-in-any-ide-securely-with-baz-mcp-server): Get high-signal, secure AI code reviews in any IDE with Baz’s new MCP server. No repo integration needed. Just connect, diff, and review. Works with Cursor, Claude, VS Code, and more.

## Research
> Deeper investigations into agents and infrastructure.
- [Ship: Inference Routing for Agentic Code Review](https://baz.ai/resources/research/ship-inference-routing-for-agentic-code-review): We evaluated Martian's Ship as an execution layer beneath Baz's code review pipeline. The question was narrow: can we change how model calls are executed without changing the review behavior the agent architecture produces?
- [The vulnerability classes that don't come back](https://baz.ai/resources/research/vulnerability-classes-that-dont-come-back): Six classes of vulnerability slipped past traditional tooling for years. A study of 28 high-confidence findings from advanced security review shows why cyber-capable models now catch them at review time - before they ship.
- [Model vs. SAST](https://baz.ai/resources/research/model-vs-sast): Internal Baz research on how newer cyber-capable models are changing AppSec detection for pull-request review.
- [Can Cyber-Capable Models Fix AppSec?](https://baz.ai/resources/research/can-cyber-capable-models-fix-appsec): A technical analysis of why modern AppSec continues to miss critical vulnerabilities despite an abundance of tools and where upcoming cyber-capable models can help.
- [Improving cross-repo context for Baz code-reviewers](https://baz.ai/resources/research/improving-cross-repo-context-for-baz-code-reviewers): Why diff-local review breaks down at scale, and how cross-file, semantic, and cross-repo chaining improve review quality for structural bugs, org-wide security policies, and multi-service engineering teams.
- [The anatomy of code review accuracy](https://baz.ai/resources/research/the-anatomy-of-code-review-accuracy): How Baz rewired its AI code reviewer from a demo into a production system by treating retrieval, decomposition, extraction, and evaluation as first-class engineering problems.
- [Spec Agents as Design Reviewers](https://baz.ai/resources/research/spec-agents-as-design-reviewers): Spec Agents are our attempt to move that work into the agent layer. Assuming you built the required testing infrastructure, the goal is to have an agent that reads the spec, walks the product, and returns a clear yes or no with evidence.We’ve been running this model across a cohort of early users and watched how they behaved end to end.
- [Extending OpenTelemetry to Pinpoint Code Elements: Our Journey to Close the Gap](https://baz.ai/resources/research/extending-opentelemetry-to-pinpoint-code-elements-our-journey-to-close-the-gap): Enhancing OpenTelemetry's capabilities to pinpoint specific code elements responsible for system behaviors.
- [Ordering Files for AI-Assisted Development](https://baz.ai/resources/research/ordering-files-for-ai-assisted-development): Explore how ordering files and understanding code grammar can enhance AI-assisted development.

## Press
> Baz in the press, covered by outlets and industry newsletters.
- [Baz: Review-Driven Development for Agentic Software Lifecycles](https://baz.ai/resources/press/baz-review-driven-development-for-agentic-software-lifecycles-intellyx): Intellyx on Baz's review-driven development approach for agentic software lifecycles
- [Startups to know: Baz brings governance, planning, specs to AI-generated code](https://baz.ai/resources/press/startups-to-know-baz-brings-governance-planning-specs-constellation): Startups to know: Baz brings governance, planning, specs to AI-generated code
- [Baz Planner Released](https://baz.ai/resources/press/baz-planner-released-devopsdigest): Baz Planner Released
- [Baz Planner Targets AI Code Governance at the Source](https://baz.ai/resources/press/baz-planner-targets-ai-code-governance-at-the-source-eci-research): Baz Planner Targets AI Code Governance at the Source
- [This Week in Enterprise](https://baz.ai/resources/press/this-week-in-enterprise-siliconangle): siliconANGLE's This Week in Enterprise
- [Baz launches Planner with USD $9 million seed raise](https://baz.ai/resources/press/baz-launches-planner-with-usd-9-million-seed-raise-it-brief): Baz launches Planner with USD $9 million seed raise
- [July 1, 2026](https://baz.ai/resources/press/july-1-2026-tel-aviv-diary): Tel Aviv Diary, July 1, 2026
- [Venture Capital Newsletter](https://baz.ai/resources/press/venture-capital-newsletter-wsj-pro): WSJ Pro's Venture Capital newsletter
- [Baz Technologies Raises $9M in Extended Seed Funding](https://baz.ai/resources/press/baz-technologies-raises-9m-in-extended-seed-funding-finsmes): Baz Technologies Raises $9M in Extended Seed Funding
- [Bridgecrew founder Guy Eisenkot's Baz raises $9 million as AI coding race intensifies](https://baz.ai/resources/press/bridgecrew-founder-guy-eisenkots-baz-raises-9-million-ctech): Bridgecrew founder Guy Eisenkot's Baz raises $9 million as AI coding race intensifies
- [Term Sheet](https://baz.ai/resources/press/term-sheet-fortune): Fortune's Term Sheet newsletter
- [Exclusive: Agentic coding startup Baz brings code reviews to the planning stage as it extends seed funding to $17M](https://baz.ai/resources/press/baz-brings-code-reviews-to-the-planning-stage-siliconangle): Exclusive: Agentic coding startup Baz brings code reviews to the planning stage as it extends seed funding to $17M
- [Baz Hits $17M Seed, Pushes AI Code Review to Planning Stage](https://baz.ai/resources/press/baz-hits-17m-seed-pushes-ai-code-review-to-planning-stage-ai-weekly): Baz Hits $17M Seed, Pushes AI Code Review to Planning Stage
- [Baz Raises $9 Million To Advance Agentic Coding Platform](https://baz.ai/resources/press/baz-raises-9-million-to-advance-agentic-coding-platform-pulse2): Baz Raises $9 Million To Advance Agentic Coding Platform
- [Baz Raises $9M Seed Extension](https://baz.ai/resources/press/baz-raises-9m-seed-extension-the-saas-news): Baz Raises $9M Seed Extension
- [Baz releases Baz Planner, which uses four specialized AI agents to analyze code at the planning stage, and extends its seed funding by $9M to $17M](https://baz.ai/resources/press/baz-releases-baz-planner-techmeme): Baz releases Baz Planner, which uses four specialized AI agents to analyze code at the planning stage, and extends its seed funding by $9M to $17M
- [Venture Capital & Startup Funding Roundup, June 29, 2026](https://baz.ai/resources/press/venture-capital-startup-funding-roundup-june-29-2026-techstartups): Venture Capital & Startup Funding Roundup, June 29, 2026

## Company
- [About Baz](https://baz.ai/about): The team and principles behind Baz.
- [Careers](https://baz.ai/careers): Open roles at Baz.
- [Contact](https://baz.ai/contact): Talk to the Baz team.
- [Security & compliance](https://baz.ai/docs/account/security-privacy-and-compliance): How Baz protects source code and data.