6 JetBrains Central Alternatives for Agent Governance

This comparison covers Augment Cosmos, Cursor 3, Google Antigravity, Emdash, Baton, and Devin as JetBrains Central alternatives. Augment Cosmos is a unified cloud agents platform for running governed agents across the software development lifecycle. Every alternative here provides publicly accessible agent orchestration or governance capabilities, while JetBrains Central remains in a closed Early Access Program in Q2 2026.

I evaluated these six alternatives after JetBrains announced Central on March 24, 2026, and slated its Early Access Program for Q2 2026 with a limited group of design partners. Engineering leaders managing agent fleets across large or legacy repositories need timeline certainty before they standardize on a control plane, and pricing risk compounds it. JetBrains AI users faced AI pricing criticism after credits depleted roughly ten times faster than expected following a mid-contract change.

I assessed each platform across six selection criteria:

• Governance controls: Policy enforcement, approval gates, auditability, and observability
• Multi-agent orchestration: Agent coordination, parallel execution, and shared context across the software development lifecycle
• Compliance evidence: SOC 2 Type II, ISO/IEC 42001, and procurement-ready documentation
• Deployment flexibility: Cloud, self-hosted, VPC, local-first, or open-source distribution
• Identity and access: SSO, SCIM, RBAC, repository scoping, and tool permissions
• IDE coverage: JetBrains IDEs, VS Code, Vim/Neovim, CLI, and IDE-agnostic workflows

My scoring lens treated governance as more than an admin dashboard. A useful control plane should show who can invoke an agent, which repositories and tools it can reach, whether policy gates run before execution, how it logs actions, and where it processes code. I weighted documented controls over roadmap language, since Central's core limitation is availability, and kept orchestration separate from governance, because coordinating many agents does not by itself give enterprises audit trails, identity controls, or certification evidence.

That distinction matters for mixed organizations. Builders prioritize multi-repo execution, pull-request automation, and fast branch isolation; Operators and Multipliers need SSO, SCIM, RBAC, SIEM integration, and procurement-ready compliance documents. Open-source options give local control but shift enterprise enforcement onto internal platform teams, and public-preview tools teach workflows while leaving regulated teams exposed to rate limits, unpriced tiers, and thin governance docs. I used those tradeoffs to place each alternative rather than rank every product against one universal buyer.

JetBrains Central baseline. JetBrains Central is a control and execution plane that adds governance, observability, and cost attribution on top of agent workflows. JetBrains announced it on March 24, 2026, and The New Stack describes Central as a governance execution platform for agent work.

Central organizes three core capabilities for engineering organizations managing agents at scale:

• Governance and control: Policy enforcement, identity and access management, observability, auditability, and cost attribution for agent-driven work
• Agent execution infrastructure: Cloud agent runtimes and computation provisioning so agents run reliably across development environments
• Model routing and shared repository context: Shared semantic context across repositories plus task routing to appropriate models or tools

JetBrains frames its governance rationale around scale. Central connects JetBrains IDEs, third-party IDEs, CLI tools, and web interfaces, and supports external agents including Claude Agent, Codex, and Gemini CLI.

Access limits evaluation. Central is closed EAP in Q2 2026, limited to design partners. The Console provides cost visibility and Air provides agent execution, but policy enforcement, audit controls, and centralized BYOK management remain in development.

Why teams compare alternatives now. Access, ecosystem fit, deployment controls, and pricing all remain unresolved, while every alternative here is publicly accessible or open source. Four factors push teams to look elsewhere:

• Access. Most teams cannot enter the program; JetBrains' EAP risk clause places use at the user's own risk, and AI EAP terms disclose that inputs may reach external AI providers.
• Ecosystem fit. JetBrains AI Enterprise scopes the native agent experience to JetBrains IDEs, Android Studio, and VS Code, leaving Neovim, Emacs, and mixed toolchains out.
• Pricing. A community support thread records credit complaints about credits draining with minimal usage after a mid-contract change.
• Governance. IDE-native tooling does not cover every control enterprises expect, such as compliance evidence, policy enforcement, audit and observability, deployment model, and identity integration.

If your team manages org-wide agent workflows across large or legacy codebases, compare platforms by repository scoping, approval gates, audit logs, and deployment model before selecting a pilot. The six profiles below cover each alternative across governance, orchestration, deployment, compliance, and pricing.

1. Augment Cosmos: Unified Cloud Agents Platform with Governance

Augment Cosmos is a unified cloud agents platform for running coordinated agents across the software development lifecycle, with spec and intent review checkpoints that put a plan in front of a human before agents write code. I evaluated Cosmos as an agentic development environment for coordinating agent work beyond a single IDE.

Cosmos runs each agent inside a defined Environment that scopes where it executes and what it can touch, while its Agent Runtime handles scheduling and isolation. Agents draw on Augment's Context Engine to understand the task, then return a spec for human review before they independently write, test, and review code. Parallel agents fan out only after that spec clears review, and every run lands as an auditable, replayable Session. When I evaluated Cosmos's spec and intent review checkpoint on a multi-service refactoring task, the platform surfaced a scope problem for review before any agent touched code.

The governance model separates Cosmos from pure orchestration layers. Teams set policies for where human judgment is required, and Cosmos enforces those checkpoints, with rules that encode enterprise coding standards agents inherit. Enterprise deployment adds SIEM integration, data residency, access controls, audit trails, and SSO/OIDC/SCIM, plus customer-managed keys. In that same Cosmos evaluation, the reviewable spec mattered because Augment's Context Engine processes entire codebases across 400,000+ files through semantic dependency analysis before agents fan out.

Cosmos runs across laptops, Dev-VMs, and the cloud, with daemons bridging environments so a session that starts in the Auggie CLI resumes on the web. Developers reach it through VS Code, JetBrains, and the CLI, and the same CLI supports interactive sessions and CI/CD automation. Cosmos is model-agnostic: bring your own keys across Anthropic, OpenAI, Bedrock, Vertex, and open-source models, with Prism routing each task to the right model for roughly 20-30% lower model cost without giving up quality.

On compliance, Cosmos documents SOC 2 Type II and ISO/IEC 42001:2023 coverage, dated July 2024 and August 2025 respectively. Teams evaluating certification fit should map those claims against enterprise audit requirements before procurement.

Pricing:

Choose Cosmos if you need spec and intent review checkpoints with policy enforcement and you operate in a regulated industry requiring both SOC 2 Type II and ISO/IEC 42001 across mixed IDE toolchains.

2. Cursor 3: Agent Workspace with Multi-Repo Orchestration

I evaluated Cursor 3 as a unified workspace for building software with agents. The documented fit centers on multi-repo layouts, cloud subagents running in separate VMs on separate branches, and Enterprise self-hosted cloud agents. Cursor describes Cursor 3 as a workspace you can switch back to the Cursor IDE from at any time.

Cursor uses an agent scaling model with Planners, Workers, and a Judge. Planners continuously explore the codebase and create tasks, spawning sub-planners for specific areas so planning becomes recursive and parallel. Workers execute tasks, and a Judge agent determines whether to continue at the end of each cycle.

Cloud subagents run in VMs on their own branches, so the local workspace stays clean. Nested subagents let a reviewer delegate review tasks to a test-writer, which can delegate further. Each level keeps its own prompt and model. When I evaluated Cursor 3 for multi-repo branch workflows, operational isolation gave the main control boundary.

On governance, Cursor's Teams tier ($40/user/mo) includes SAML/OIDC SSO, centralized admin, shared team context, usage analytics, and team-wide privacy mode.

The Enterprise tier adds SCIM seat management, audit logs, service accounts, and an AI code tracking API. It also provides access controls for repository, model, MCP, auto-run, browser, and network, per Cursor pricing. Cursor lists SOC 2 Type II on its security page; it does not list ISO/IEC 42001.

Cursor concentrates its integrations on GitHub and GitLab automation. Changelog 3.8 adds GitHub triggers, and Bugbot is the built-in review bot for GitHub and GitLab. In the open-source benchmark, Cursor Bugbot scored 60% precision, 41% recall, and a 49% F-score. That benchmark uses golden comments and publishes dataset and evaluation scripts. Enterprise teams can use self-hosted cloud agents at Enterprise tier.

Pricing:

Choose Cursor 3 if you want GitHub automation triggers, GitHub/GitLab Bugbot reviews, and a VS Code-fork IDE experience with self-hosted cloud agent orchestration at Enterprise tier.

3. Google Antigravity: Free Multi-Agent Platform

I evaluated Google Antigravity as a free public-preview platform for spawning and orchestrating multiple agents in parallel through a Manager Surface. Google announced the Antigravity launch on November 18, 2025, with Antigravity 2.0 announced May 19, 2026.

Antigravity offers two surface modes. The Editor View is an AI-powered IDE with tab completions and inline commands. The Manager Surface acts as mission control for spawning, orchestrating, and observing multiple agents across multiple workspaces in parallel. In version 2.0, agents can spin subagents from a single prompt, and multi-agent orchestration runs tasks in parallel for refactoring, unit test generation, and service scaffolding.

Antigravity's documented enterprise controls stop at project-level security policies. Official docs describe only project security policies. The enterprise guidance focuses on Google Cloud credential login and data-handling terms. It does not list team-level audit logs, SSO, RBAC, or policy enforcement features.

When I evaluated Antigravity for small-team experimentation, the Manager Surface made parallel work easy to observe, but project-level policies set the governance ceiling. Antigravity pricing lists an Organization tier, but Google had not priced it as of May 2026. Antigravity does not document SOC 2 Type II or ISO/IEC 42001.

Enterprise data protection requires Cloud credential login and accepting GCP terms. Under that setup, Antigravity keeps enterprise prompts, responses, code, and telemetry inside private environments, though documented rate limits remain a product constraint.

Pricing: Individual is free during public preview with basic weekly rate limits. Google AI Pro and Ultra offer more generous limits. The Organization tier remains unpriced.

Choose Antigravity if you want a free multi-agent manager for individual or small-team experimentation and you do not yet need documented enterprise governance controls.

4. Emdash: Open-Source Agentic Development Environment

I evaluated Emdash as an open-source desktop application for orchestrating multiple AI coding agents in parallel, with each agent isolated in its own Git worktree. The Emdash AGENTS file describes it as a cross-platform Electron app that runs agents locally or over SSH. Arne Strickmann and Raban von Spiegel built it as a Y Combinator profile company.

Its design philosophy is deliberately minimal: it launches agents in isolated worktrees, shows diffs, and leaves coordination between agents to the developer. The architecture is local-first. Emdash stores app state in local SQLite state, sends no code to Emdash servers, and includes optional disableable telemetry. When I evaluated Emdash for local parallel agent runs, local-first storage defined the privacy boundary.

The platform supports 20+ providers including Claude Code, Codex, Cursor, OpenCode, Gemini, Amp, Devin, Qwen Code, Droid, and GitHub Copilot.

Issue tracker integrations cover Linear and Jira, plus GitHub, Asana, and Trello, with full context provided to agents. Emdash uses Apache 2.0 licensing with zero platform cost.

Emdash documents Git worktree isolation and local-first storage, but no role-based access controls, audit trails, policy enforcement, or certifications. Worktree isolation keeps parallel runs from interfering, but it is not a policy-layer control plane.

Pricing: Free under Apache 2.0.

Choose Emdash if you want a free, local-first orchestration layer with 20+ provider integrations and you accept that enterprise governance requires custom work.

5. Baton: Lightweight Open-Source Orchestration Framework

I treated Baton as an early-stage open-source AI agent orchestration framework. Conductus Labs distributes Baton as npm packages under an MIT license. I limited this profile to the Baton Framework by Conductus Labs Ltd and could not resolve disambiguation from other tools named "Baton" with high confidence. Engineering leaders should treat this profile as incomplete.

What documentation confirms is the structural design. Conductus Labs distributes Baton's cognitive thinking patterns as .yml files, including Strategic, Adaptive, Analytical, Collaborative, Creative, and Growth. It provides 25 pre-defined agent definitions across Engineering & Technical, Quality & Testing, Design & UX, Content & Documentation, Business & Management, and Framework Specialists domains. The npm monorepo includes baton-core, baton-agents, baton-cognitive-patterns, baton-knowledge, and baton-workflows.

Maturity is early. The baton-framework repository shows 37 commits, with 50 commits and 2 releases on the baton-agents repository. The public repositories document package structure, cognitive patterns, agent definitions, and MIT licensing. They do not document LLM provider integrations, governance primitives such as audit logs or approval workflows, deployment model, SSO, or SOC 2 / ISO/IEC 42001, and I found no third-party reviews. When I evaluated Baton as a code-defined framework, package structure was the useful unit rather than production governance.

Pricing: Free under MIT license. No commercial pricing tiers.

Choose Baton if you want a lightweight, code-defined orchestration framework for experimentation and you can accept the absence of documented governance features and the need for direct vendor engagement to verify capabilities.

6. Devin: Autonomous AI Software Engineer with Enterprise Controls

I evaluated Devin as an execution-first autonomous AI software engineer by Cognition. Devin operates in a sandboxed cloud environment and submits pull requests for review. Users assign tasks through task entry points including Slack, GitHub issue, or natural language.

Devin generates a plan, executes it, runs tests, and submits PRs.

Devin documents execution-first governance through SAML SSO, SCIM, role-based access controls, per-repository and per-tool scoping, audit logs, VPC deployment, custom IdP integration, and teamspace isolation. Admins control Devin access, which repositories and tools it accesses, and what actions it takes, with least-privilege defaults. Session isolation uses ephemeral sandboxes that Devin destroys when sessions end, short-lived scoped credentials, redacted secrets in logs, and network egress restricted by domain allowlist. When I evaluated Devin for execution-first task handoff, those least-privilege controls were the main reason to trust autonomous runs. Devin holds SOC 2 Type II (2024) and ISO/IEC 27001:2022 for information security, though not the AI-specific ISO/IEC 42001.

Devin names at least 10 integrations through native Devin integrations, including GitHub, GitLab, Bitbucket, Slack, Microsoft Teams, Linear, and Jira natively, plus Confluence, Notion, Datadog, and more.

Pricing:

Choose Devin if you want fully autonomous task execution with documented per-repo and per-tool scoping and you can structure work into clearly scoped, verifiable tasks.

JetBrains Central Alternatives Comparison Table

The table compares all six alternatives against JetBrains Central across the governance dimensions enterprise teams evaluate.

Among the six, only Cosmos holds ISO/IEC 42001, while SOC 2 Type II is held by Cosmos, Cursor, and Devin. For regulated industries requiring both standards, Cosmos is the only vendor in this set with documented coverage.

Read the table by your first non-negotiable. If it is compliance evidence, start with the SOC 2 Type II and ISO/IEC 42001 rows. If it is developer workflow, start with IDE integration and deployment, since a VS Code fork, a desktop app, or an IDE-agnostic task runner can slow adoption when it does not match how your engineers work. If it is agent blast radius, focus on the governance and deployment rows, where spec gates, branch isolation, and sandbox scoping reduce different kinds of operational risk.

It also shows why JetBrains Central is hard to evaluate today, because its stated direction covers policy, audit, cost attribution, execution infrastructure, and shared context while those claims stay tied to a closed EAP.

Keep a shortlist to two pilots. Regulated teams should compare Cosmos and Devin first, since both document SOC 2 Type II and expose enterprise controls, while Cosmos adds ISO/IEC 42001 and spec and intent review checkpoints. Teams standardizing on GitHub/GitLab automation can weigh Cursor 3 against Cosmos or Devin depending on whether they need spec approval or autonomous execution, and platform teams can use Antigravity, Emdash, or Baton to learn orchestration patterns before revisiting enterprise controls.

Match Your Governance Requirements to a Generally Available Platform

JetBrains Central promises a unified governance and execution plane, but availability, pricing, and GA timing remain unresolved, and engineering leaders cannot stake org-wide governance on a roadmap. Map your non-negotiable controls, including SSO, audit logging, policy gates, and sandbox isolation, against the platforms that are generally available or publicly accessible today, then pilot the two that match your IDE ecosystem and compliance mandates. For teams that need spec and intent review checkpoints and agents that run across AI-first workflows, the deciding factor is how each platform handles review, repository access, and audit evidence. Augment Cosmos pairs its Context Engine with policy enforcement, so agents start from repository context and humans approve the plan before execution.

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