{ "schemaVersion": "0.1", "reviewedAt": "2026-09-17", "publisher": "LockedIn Labs", "url": "https://aisdlc.ai/enterprise-ai-sdlc-platforms", "disclosure": "AISDLC.ai is published by LockedIn Labs, which owns SprintLoop. SprintLoop is included as a publisher-affiliated product under the same documentation and evaluation criteria. This is a source-based shortlist, not an independent ranking or a completed hands-on benchmark.", "evidenceBasis": "public-source-review", "ordering": "alphabetical-not-ranked", "platforms": [ { "id": "claude-code", "name": "Claude Code", "category": "Coding environment", "description": "Claude Code is a coding agent that can be configured for an enterprise deployment. Anthropic's AI-native SDLC playbook is a methodology and course, not a separately evaluated enterprise control-plane product.", "controls": "Team and Enterprise organizations can distribute server-managed settings, while endpoint management can protect device policies at the operating-system level. Managed permissions and sandbox settings govern supported agent actions; policy reach and failure behavior differ between local, cloud and third-party-provider sessions.", "validationQuestion": "Can an unmanaged device, alternate provider or modified client reach enterprise repositories and deployment credentials without passing an external policy gate?", "limits": "Documentation review only. Anthropic explicitly describes server-managed settings as client-side controls rather than a security boundary. Direct provider routing can skip server-managed delivery; endpoint management or an appropriate gateway is needed for those cases. The Bash sandbox supports macOS, Linux and WSL2, not native Windows, and does not itself isolate all other tool surfaces.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Configure server-managed settings", "url": "https://code.claude.com/docs/en/server-managed-settings" }, { "title": "Configure the sandboxed Bash tool", "url": "https://code.claude.com/docs/en/sandboxing" }, { "title": "The AI-native SDLC playbook — Introduction", "url": "https://academy.claude.com/courses/ai-native-sdlc-playbook/introduction" } ] }, { "id": "cursor", "name": "Cursor", "category": "Coding environment", "description": "Cursor offers editor, CLI and Cloud Agent workflows with enterprise identity, device management and administration options.", "controls": "Enterprise documentation describes centrally enforced sandbox, model, repository, BYOK and network controls, with SSO/SCIM and managed device policies. Enterprise audit logs cover authentication and administrative actions; hooks and OpenTelemetry provide separate paths for development-activity evidence.", "validationQuestion": "Which centrally enforced settings cover the editor, CLI and Cloud Agents, and can the organization reconstruct every material agent action rather than only administrative events?", "limits": "Documentation review only. Many controls require the Enterprise plan. Local Run Modes do not govern Cloud Agents. Rules are non-deterministic steering, and standard audit logs do not include agent responses or generated code; configure and validate the additional activity evidence pipeline.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Enterprise", "url": "https://cursor.com/docs/enterprise" }, { "title": "Security and Privacy Hardening", "url": "https://cursor.com/docs/enterprise/security-hardening" }, { "title": "Compliance and Monitoring", "url": "https://cursor.com/docs/enterprise/compliance-and-monitoring" } ] }, { "id": "devin", "name": "Devin Enterprise", "category": "Agent platform", "description": "Cognition's engineering agent executes in a Devbox; current deployment documentation offers Enterprise Cloud and Customer Dedicated Deployment. Its reasoning service remains in Cognition's cloud in both models.", "controls": "Documented account- and organization-level custom roles cover repository permissions, integrations, sessions, secrets, MCP servers and audit-log access. Customer Dedicated Deployment uses a Cognition-managed, customer-isolated VPC with AWS PrivateLink or an IPSec tunnel to enterprise resources. Enterprise controls for Devin Desktop and CLI can constrain models, permissions, MCP/ACP and networking above local user/project configuration. Organization overrides replace root values; policy updates may take up to 15 minutes.", "validationQuestion": "Can a representative agent identity reach an unapproved repository or deployment credential, and can an organization override weaken the enterprise baseline without a separately authorized decision?", "limits": "Dedicated deployment and Enterprise Assured with customer-managed keys require a commercial discussion. Current canonical docs do not list the customer-hosted VPC option still appearing in older search results. Local controls are distinct from cloud Devbox deployment controls.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Enterprise Deployment", "url": "https://docs.devin.ai/enterprise/deployment/overview" }, { "title": "Custom Roles & RBAC", "url": "https://docs.devin.ai/enterprise/security-access/custom-roles" }, { "title": "Local Agent Controls", "url": "https://docs.devin.ai/enterprise/local/controls" } ] }, { "id": "factory", "name": "Factory (Droid)", "category": "Agent platform", "description": "Factory documents Droid as a local agent runtime for developer machines, CI runners and controlled enterprise environments, with cloud, hybrid and airgapped deployment patterns.", "controls": "Organization-managed settings define model and MCP allowlists, autonomy ceilings, command controls, hooks and sandbox policy. Documented sandbox modes cover individual commands or the whole Droid process; organization deny rules cannot be removed through lower-level configuration.", "validationQuestion": "What blocks execution if the managed policy is missing, invalid or stale, and which controls still apply when a developer uses an older client or another coding tool?", "limits": "Documentation review only. Factory says a malformed system-managed settings file resolves to an empty policy and logs the failure; validate this before relying on fail-closed enforcement. Sandbox reads allow all paths unless explicitly denied, and permitted network destinations can still receive data. Confirm enterprise entitlement and the selected deployment architecture with Factory.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Factory for Enterprise", "url": "https://docs.factory.ai/enterprise" }, { "title": "Enterprise Controls & Managed Settings", "url": "https://docs.factory.ai/enterprise/hierarchical-settings-and-org-control" }, { "title": "Sandbox", "url": "https://docs.factory.ai/autonomy-and-safety/sandbox" } ] }, { "id": "github-copilot", "name": "GitHub Copilot", "category": "Coding environment", "description": "GitHub Copilot combines developer assistance with a cloud agent integrated into repositories and pull-request workflows. Its enterprise value includes controls at the source-control boundary as well as coding-client policy.", "controls": "Enterprise and organization policies control feature and model access on supported surfaces. Copilot cloud agent is constrained by branch protections and required checks, cannot approve or merge its own pull requests, and provides attributable commits, session logs and administrative audit events.", "validationQuestion": "Do repository rules, approval requirements and deployment gates still reject an unauthorized change if it arrives through a different user, bot, client or automation?", "limits": "Documentation review only. Copilot policies generally follow the assigned license and do not all cover every client surface. Cloud-agent firewall configuration and validation tools can be changed by administrators; document which settings are locked and test the actual runner/network setup.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "GitHub Copilot policies for enterprises and organizations", "url": "https://docs.github.com/en/copilot/concepts/enterprise/policies" }, { "title": "Risks and mitigations for GitHub Copilot cloud agent", "url": "https://docs.github.com/en/copilot/concepts/agents/cloud-agent/risks-and-mitigations" }, { "title": "Customizing or disabling the firewall for GitHub Copilot", "url": "https://docs.github.com/en/copilot/how-tos/copilot-on-github/customize-copilot/customize-the-firewall" } ] }, { "id": "gitlab-duo-agent-platform", "name": "GitLab Duo Agent Platform", "category": "Delivery platform", "description": "Agents and flows operate inside GitLab's development lifecycle, with GitLab.com, Self-Managed and Dedicated offerings. The platform is generally available from GitLab 18.8; individual features have separate maturity and licensing requirements.", "controls": "Documented tool governance supports allow, ask and deny decisions at execution time, with project rules constrained to be at least as strict as group rules. Governance is marked beta; background-flow and MCP enforcement have version and feature-flag dependencies. MCP server blocking for UI chat does not control IDE/CLI local server configuration. Self-hosted model deployment uses a self-hosted AI Gateway. Online licenses still require billing connectivity; offline deployment has a separate add-on and agreement.", "validationQuestion": "Does the same denied operation fail through UI chat, IDE/CLI and background runners on the installed version, including an MCP route to the same resource?", "limits": "Do not conflate the generally available platform with beta governance. Check the installed GitLab version, credits, plan, feature flags and execution surface; some docs describe 19.4 capabilities. GitLab Duo Enterprise add-on compatibility requires 18.10 or later. Runner rules support Allow or Deny, not Ask; a tool without a configured runner rule defaults to Allow. The aggregate MCP search tool needs its own explicit rule because narrower search-tool rules do not apply to it.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "GitLab Duo Agent Platform", "url": "https://docs.gitlab.com/user/duo_agent_platform/" }, { "title": "Agent tool governance", "url": "https://docs.gitlab.com/user/ai-governance/tool-governance/" }, { "title": "Self-hosted models", "url": "https://docs.gitlab.com/administration/gitlab_duo_self_hosted/" } ] }, { "id": "harness", "name": "Harness Software Delivery Agent", "category": "Delivery platform", "description": "Harness positions Software Delivery Agent within its autonomous SDLC platform for CI/CD and infrastructure delivery. Worker Agents are configured as pipeline steps rather than standalone coding IDEs.", "controls": "Worker Agent documentation defines instructions, a model-provider connector and executable pipeline configuration; enterprise delivery controls must be assessed around those executed steps. The product describes pipeline RBAC, Open Policy Agent policies, deployment freezes and audit trails governing agent-driven delivery. Security-test policy documentation distinguishes warning-and-continue from error-and-exit, with policy sets evaluated after configured scan steps. A configured warning is not a release block.", "validationQuestion": "Can an agent or human deploy the same artifact outside the governed pipeline, and do production credentials, policy administration and emergency overrides preserve an attributable approval boundary?", "limits": "Worker Agents are documented, but deployment mode, enabled modules, connectors and commercial entitlements must be checked for the actual tenant. No hands-on enterprise rollout or claim of all-path enforcement is established by this review.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Software Delivery Agent — AI Agent for CI/CD & IaC", "url": "https://www.harness.io/products/software-delivery-agent" }, { "title": "Harness Agents reference", "url": "https://developer.harness.io/docs/platform/harness-ai/core-capabilities/in-your-pipelines/harness-agents-references/" }, { "title": "Policy as Code for Security Tests", "url": "https://developer.harness.io/docs/platform/governance/policy-as-code/policy-as-code-for-security-tests" } ] }, { "id": "kiro", "name": "Kiro", "category": "Coding environment", "description": "Kiro provides spec-driven development agents across IDE, CLI and cloud surfaces, with enterprise identity and administration.", "controls": "Administrators can deploy permission policies to OS-protected paths used by local IDE and CLI clients; restrictive admin rules take precedence over personal permissions. The documentation specifies fail-closed behavior for malformed policy documents and separate governance controls for models, MCP and web tools.", "validationQuestion": "Which exact policies apply to local, web and headless sessions, and what prevents users with local administrator rights or alternative credentials from operating outside them?", "limits": "Documentation review only. Local policies require a restart; unknown capabilities are skipped with a warning, so fleet version compatibility matters. A device policy does not by itself establish identical cloud enforcement. Enterprise authentication, region support and required versions must be verified for the deployment.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Permission policies", "url": "https://kiro.dev/docs/enterprise/governance/permissions/" }, { "title": "Authentication", "url": "https://kiro.dev/docs/cli/authentication/" }, { "title": "Permissions", "url": "https://kiro.dev/docs/cli/chat/security/" } ] }, { "id": "openai-codex", "name": "OpenAI Codex", "category": "Agent platform", "description": "Codex combines local coding clients and hosted coding tasks. Enterprise administration distinguishes workspace access, local runtime requirements, cloud environments and permissions in connected systems.", "controls": "Managed requirements can restrict supported local clients' approvals, permission profiles, filesystem/network access and feature availability; configuration defaults are a separate, overridable mechanism. Requirements can be delivered through cloud configuration, system configuration and supported device management. Login-method and approved-workspace restrictions must be managed locally. Cloud tasks use hosted environments and repository connections; workspace membership alone does not authorize repository or connected-system actions.", "validationQuestion": "Can the enterprise demonstrate effective policy for CLI, IDE and hosted runs, including alternate authentication methods, and prove that repository and production deployment authorization remains enforced outside the agent?", "limits": "Policy keys depend on supported client versions and plans. Cloud refresh can affect a later start rather than the running process. Do not treat local requirements as universal cloud or organization-wide deployment authorization. Current enterprise documentation redirects from developers.openai.com to OpenAI's learn.chatgpt.com documentation.", "affiliated": false, "reviewedAt": "2026-09-17", "sources": [ { "title": "Admin rollout guide", "url": "https://learn.chatgpt.com/docs/enterprise/admin-setup" }, { "title": "Managed configuration", "url": "https://learn.chatgpt.com/docs/enterprise/managed-configuration" }, { "title": "Codex cloud", "url": "https://learn.chatgpt.com/docs/cloud" } ] }, { "id": "sprintloop", "name": "SprintLoop", "category": "Delivery platform", "description": "SprintLoop describes a portfolio record connecting plans, agent-run records, model approvals, policies and named acceptance. It belongs to LockedIn Labs, the publisher of this comparison; it has not been independently benchmarked here.", "controls": "The public site describes approved-model registers, recorded agent stages and commits, advisory or blocking policy dispositions and named human acceptance. The security page describes database-enforced tenant isolation, append-only verdicts and permission checks at the workspace MCP endpoint. These are vendor-described controls, not runtime findings from this review. The homepage explicitly positions SprintLoop as the register and acceptance desk for external harnesses, not an agent execution engine; it says it does not execute builds or dispatch lanes.", "validationQuestion": "Which decisions actually block execution in SprintLoop's own endpoints versus an external harness, and can a demonstrated end-to-end run prove that rejected model, tool and acceptance actions cannot proceed through another credential path?", "limits": "Public-site review only. No tenant, authentication, enforcement, benchmark or deployment test was performed. Product pages describe US-hosted customer records. The homepage says SprintLoop does not hold model keys or run agents, while the security page discusses runtime credential resolution and model-boundary enforcement; buyers should reconcile that scope in a demonstration.", "affiliated": true, "reviewedAt": "2026-09-17", "sources": [ { "title": "SprintLoop — Project portfolio management (PPM) software", "url": "https://sprintloop.ai/" }, { "title": "Security and tenant isolation", "url": "https://sprintloop.ai/security" } ] } ] }