Agentic Index
Cursor vs OpenAI Codex (2026)
Both vendors last verified
Both carry frontier models into development and they run in different places. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Cursor is an AI first editor with agentic assistance, multi file editing and code review, from a free plan and Plus at $8 a month. Codex is OpenAI's agentic coder with command line, API and GitHub Action support, running code in a cloud sandbox for pull request review, multi file edits, test execution and branch management, included in ChatGPT plans from Free and Plus at $20 up to Pro from $100. Cursor keeps the developer in the loop, and Codex runs in a sandbox where nobody is watching until the pull request appears.
On the Agentic Index coding agent ranking, neither Cursor nor OpenAI Codex clears the bar, which asks for all five merge loop capabilities documented in full. Neither documents knowledge grounding and RAG in full, nor testing, debugging and optimization. 3 of the 96 vendors in the lane clear it. See the coding agent ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Cursor and OpenAI Codex are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Cursor if
- The editor is where your team wants the help, not a sandbox they have to go and check on.
- A free plan and a flat monthly price for Cursor's own models is simpler than usage billing.
- Frontier model choice inside the editor is the flexibility you want.
Choose OpenAI Codex if
- Running in a cloud sandbox means agents work without occupying a developer's machine.
- GitHub Action support puts the agent in your pipeline rather than your editor.
- You are already on a ChatGPT plan, which makes this incremental.
| Feature | C Cursor |
O OpenAI Codex |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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CursorIntegrations & Tool Calling MCP servers connect the agent to external tools and data over stdio, SSE or Streamable HTTP with OAuth for remote servers, installed one click from the Cursor Marketplace or configured in mcp.json, and team admins can distribute servers through a team marketplace; tools, prompts, resources and elicitation are supported. Sourcecursor.com/docs/mcpread 2026-09-29 |
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OpenAI CodexIntegrations & Tool Calling MCP connects outside services, with approved server lists under enterprise control, and plugins and connectors extend the agent with tool calls that have side effects, which require approval when they advertise destructive annotations. Third party integrations cover GitHub, GitLab, Slack and Linear. The agent runs shell commands under an OS sandbox, uses a web search tool with cached, live and disabled modes, and reaches the browser and computer use surfaces. Sourcelearn.chatgpt.com/codex/extend/mcp, /codex/third-party and /codex/agent-approvals-securityread 2026-08-30 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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CursorWorkflow Orchestration The agent delegates to subagents that each run in their own context window, in the foreground or in the background and in parallel, with built in Explore, Bash and Browser subagents and custom subagents defined with their own prompts, tool access and models, in the editor, the CLI and Cloud Agents. Sourcecursor.com/docs/subagentsread 2026-09-29 |
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OpenAI CodexWorkflow Orchestration The agent sustains long autonomous sessions across many sequential tool calls, with configurable subagents, git worktrees for parallel work, projects and chats organizing multi step work, long running work and scheduled tasks, and the desktop app running agent threads side by side. The cloud agent works asynchronously in isolated containers so many tasks proceed in parallel, and multi agent tooling is among the stable CLI surfaces. Sourcelearn.chatgpt.com/codex/agent-configuration/subagents, /codex/environments/git-worktrees and /codex/long-running-workread 2026-08-30 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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CursorTriggers & Channel Coverage Automations run cloud agents in the background on a schedule (preset or cron) or in response to events, a GitHub pull request opened or marked ready, Slack messages, Linear issue changes, PagerDuty incidents, Sentry errors and webhooks, with no person starting the run; Bugbot also reviews every pull request update automatically. Sourcecursor.com/docs/cloud-agent/automations and docs/bugbotread 2026-09-29 |
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OpenAI CodexTriggers & Channel Coverage Codex runs in the ChatGPT desktop app, a Codex CLI, an IDE extension, ChatGPT on the web, Codex cloud, and a Remote mode driven from a phone. Scheduled tasks run work on a timetable, long running work is supported, a GitHub Action and non-interactive mode cover CI, and third party integrations reach GitHub, GitLab in beta, Slack and Linear, with notifications closing the loop. Sourcelearn.chatgpt.com/codex/automations, /codex/github-action and /codex/third-partyread 2026-08-30 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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CursorKnowledge Grounding & RAG The agent finds code with Instant Grep, an exact match and regex search engine that builds and queries its index on the developer's machine, and an Explore subagent that runs parallel searches per task; files, folders and past chats are attached per conversation with @ mentions. Cursor does not store embeddings of the codebase for search, so context is gathered on each run, and the local grep index exists to speed up search. Sourcecursor.com/docs/agent/tools/search and help/customization/contextread 2026-09-29 |
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OpenAI CodexKnowledge Grounding & RAG Context is assembled for each run. The agent reads the repository, resolves @ mentioned files, calls MCP servers for outside sources and uses a web search tool whose cached mode draws on a web index OpenAI maintains. AGENTS.md and Rules give the agent standing instructions. OpenAI does not describe an index that Codex keeps over the customer's own code or documents. Sourcelearn.chatgpt.com/docs/agent-configuration/agents-md, /docs/extend/mcp and the docs llms.txt indexread 2026-09-29 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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CursorMemory & State Persistence Past agent transcripts can be searched from the Agents Window and attached to a new conversation with @Chats, and checkpoints snapshot modified files within a session. Cursor does not describe a memory layer of its own, project rules act as standing instructions, and subagents start without prior conversation history. Sourcecursor.com/help/customization/context and docs/agent/overviewread 2026-09-29 |
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OpenAI CodexMemory & State Persistence Codex writes its own memories: once enabled, it turns useful context from eligible prior chats into local memory files under CODEX_HOME, redacting secrets and updating in the background after a chat goes idle. Memories live in the local Codex home, /memories chooses per chat whether a chat may read existing memories or feed future ones, and managed configuration can pin the feature on or off. Unused memories retire from consolidation after memories.max_unused_days, 30 by default and capped at 365, memories.max_rollout_age_days bounds the chats considered, and the files can be opened for review. The feature is off by default. AGENTS.md holds standing rules, separate from memory. Sourcelearn.chatgpt.com/docs/customization/memories and /docs/config-file/config-referenceread 2026-09-29 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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CursorHuman Oversight & Guardrails Run Modes decide when the agent stops for approval on shell, MCP and fetch calls: Allowlist runs only listed actions without asking, Auto-review sandboxes shell commands and sends other calls to a classifier that can ask the user to approve, plain English block instructions route named actions to approval, and a team configuration set in the dashboard overrides user and project settings; browser tools require approval by default. Sourcecursor.com/docs/agent/security/run-modes and docs/agent/tools/browserread 2026-09-29 |
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OpenAI CodexHuman Oversight & Guardrails Two independent layers govern the agent: sandbox mode controlling what it can technically do, and approval policy controlling when it must ask, with presets from read-only through workspace-write to danger-full-access and a granular policy that keeps chosen approval categories interactive while auto rejecting others. An Auto-review reviewer agent can evaluate eligible approval requests against a published policy checking for data exfiltration, credential probing, security weakening and destructive actions, failing closed on parse or session errors. Destructive MCP and app tool calls always require approval, .git, .agents and .codex are protected read-only inside writable roots, and enterprise managed requirements override local settings. Sourcelearn.chatgpt.com/codex/agent-approvals-security and /codex/sandboxing/auto-reviewread 2026-08-30 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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CursorSecurity, Identity & Governance Cursor holds a SOC 2 Type II attestation and AIUC-1, ISO/IEC 27001:2022 and ISO/IEC 42001:2023 certifications, with certificates and reports available on request at trust.cursor.com, and offers SSO, SCIM and enforced multi factor authentication. SAML and OIDC SSO come on Teams, and SCIM, repository and model access controls and audit logs on Enterprise. Sourcecursor.com/security and cursor.com/pricingread 2026-09-29 |
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OpenAI CodexSecurity, Identity & Governance OpenAI's trust portal at trust.openai.com lists SOC 2 Type 2, SOC 3, ISO/IEC 27001, 27017, 27018, 27701 and 42001, CSA STAR and FedRAMP 20x, with the SOC 2 report scoped to the API Platform, ChatGPT Enterprise, ChatGPT Edu and ChatGPT Team, the plans and API key path Codex runs on, though the portal does not name Codex separately. Access controls include managed configuration that overrides local settings, roles and workspace permissions, SCIM and EKM on Enterprise, workload identity federation, service accounts, plugin, connector and skill controls, approved MCP server lists, a Compliance API with audit events, Private Link, IP allowlisting and mutual TLS. Sourcetrust.openai.com and learn.chatgpt.com/docs/enterprise/managed-configurationread 2026-09-29 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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CursorObservability & Auditability Hooks fire on every step of the agent loop (preToolUse and postToolUse, shell and MCP execution before and after, file reads and edits, subagent start and stop, agent responses and thoughts), auditing is one of their named uses, and Enterprise admins distribute team hooks from the dashboard. The Admin API returns per request usage events with model, tokens and conversation ID, and its audit log covers logins and user and settings changes, with no agent actions among them. Cursor does not ship a log sink for the per step hook record, so the customer builds one to keep that record as an audit trail. Sourcecursor.com/docs/hooks and docs/account/teams/admin-apiread 2026-09-29 |
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OpenAI CodexObservability & Auditability Opt in OpenTelemetry export emits an event catalog that includes codex.tool_decision, recording approved or denied and whether the source was configuration or the user, codex.tool_result with duration, success and output snippet, codex.api_request, codex.sse_event and codex.user_prompt, with counter and duration histogram metrics, exportable over OTLP to a collector the customer controls. Enterprise adds a Compliance API and audit events, workspace analytics and an Analytics API. Telemetry is off by default and prompt text is redacted unless explicitly enabled. Sourcelearn.chatgpt.com/codex/agent-approvals-security and /codex/enterprise/compliance-apiread 2026-08-30 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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CursorDeployment & Data Residency Self-Hosted Machines move Cloud Agent tool execution to machines the customer manages, such as a Linux VM in the customer's AWS account, a Kubernetes cluster or its own devices, with the checkout, build cache and machine local credentials staying there (Team Pools on Enterprise). Cursor still runs the agent loop, inference and planning, and Enterprise customers can enroll in US only data residency. Sourcecursor.com/docs/cloud-agent/self-hosted and help/security-and-privacy/regionsread 2026-09-29 |
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OpenAI CodexDeployment & Data Residency Local environments run entirely on the developer's machine through the CLI, IDE extension and desktop app under an OS enforced sandbox using Seatbelt on macOS, bwrap and seccomp on Linux and a native Windows sandbox or WSL2, with dev container support and a published secure devcontainer reference; cloud environments run in isolated OpenAI managed containers with configurable internet access and domain allow lists. Enterprise adds Private Link, IP allowlisting, mutual TLS, IP egress ranges and Amazon Bedrock as a model provider. Sourcelearn.chatgpt.com/codex/environments/modes, /codex/agent-approvals-security and /codex/amazon-bedrockread 2026-08-30 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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CursorPrebuilt Agents, Templates & Packs Plugins bundle rules, skills, agents, commands, MCP servers and hooks, and the Cursor Marketplace lists manually reviewed official plugins installed per project or user from Customize, including Amplitude, Datadog, Figma, Linear, Sentry, Stripe, Supabase and Vercel plugins. Teams and Enterprise can run private team marketplaces, and three subagents ship built in. Sourcecursor.com/docs/plugins and cursor.com/marketplaceread 2026-09-29 |
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OpenAI CodexPrebuilt Agents, Templates & Packs Skills and plugins extend Codex, with dedicated build guides, a plugin architecture and submission path, enterprise plugin management, and plugin and skill controls for administering approved catalogs, and a Claude Code plugin can be submitted as well. Subagents are configurable per project, and Codex Micro ships as a packaged capability. Sourcelearn.chatgpt.com/codex/skills-and-plugins, /codex/build-plugins and /codex/enterprise/plugin-managementread 2026-08-30 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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CursorModel Flexibility & Routing Cursor's own models, Grok and Composer, run from a weekly usage pool on every paid plan. On Premium and above, users also pick Anthropic, OpenAI and Google models at API list price from prepaid credit. On Teams and Enterprise, Cursor Router picks among Cursor and third party models by cost, balance or intelligence, and admins allow or block models. Plus is limited to Grok models. Sourcecursor.com/docs/models-and-pricing and cursor.com/pricingread 2026-10-25 |
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OpenAI CodexModel Flexibility & Routing Local Codex clients let the customer choose the model maker, accepting custom model providers in config.toml, each defined by base URL, wire API and authentication, and OpenAI's own example configures Mistral's API and a local Ollama endpoint beside an OpenAI proxy. OSS mode runs Codex against a local open source provider, Ollama or LM Studio, chosen per run with --local-provider or set as the default. Within OpenAI's own family the model is selectable per session and per workspace. Amazon Bedrock is a built in provider that serves OpenAI models on AWS. Sourcelearn.chatgpt.com/docs/config-file/config-advanced (Custom model providers, OSS mode) and /docs/modelsread 2026-09-29 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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CursorAPIs, SDKs & MCP Extensibility The Cloud Agents API at api.cursor.com, with Basic or Bearer authentication on user or service account keys, creates, lists, follows up, streams, cancels, archives and deletes agents and their runs and returns artifacts, and TypeScript and Python SDKs are published beside it. Sourcecursor.com/docs/cloud-agent/api/endpointsread 2026-09-29 |
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OpenAI CodexAPIs, SDKs & MCP Extensibility Developers build on a Codex SDK, an App Server, an MCP Server, a GitHub Action and a non-interactive mode, alongside MCP client support with approved server lists, hooks that fire on lifecycle events, Site tools via WebMCP, Record and Replay, and a Python SDK with auth. The CLI is open source in Rust at github.com/openai/codex with a published default reviewer policy, and the documentation offers an llms.txt index and a markdown version of every page. Sourcelearn.chatgpt.com/codex/codex-sdk, /codex/mcp-server and /codex/open-sourceread 2026-08-30 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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CursorTesting, Debugging & Optimization Bugbot reviews each pull request for bugs, security issues and quality problems and publishes a CI check status, and a custom verifier subagent can run the tests and report what passed. Both test the customer's code, and Cursor does not describe a harness, scored test set or optimization loop for evaluating the agent's own behavior. Sourcecursor.com/docs/bugbot and docs/subagentsread 2026-09-29 |
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OpenAI CodexTesting, Debugging & Optimization Codex reviews code changes before commit, a Codex Security plugin and CLI run scans, deep scans and bulk scans in CI with a security workbench and finding export, and the agent runs tests and iterates on failures; Auto-review evaluates individual agent actions against a risk policy at runtime. Those tools check the customer's code and gate the agent's actions, and there is no harness to evaluate, replay or regression test the agent's own behavior. Sourcelearn.chatgpt.com/codex/code-review and /codex/securityread 2026-08-30 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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CursorBrowser & Computer Use The browser tool lets the agent control a browser pane inside Cursor, navigating, clicking, typing, scrolling and taking screenshots to test applications, audit accessibility and check visual changes, through Cursor's own MCP server, with approval required by default and an admin origin allowlist; a built in Browser subagent runs these steps, and self hosted workers run computer use tools. Sourcecursor.com/docs/agent/tools/browser and docs/subagentsread 2026-09-29 |
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OpenAI CodexBrowser & Computer Use Browser and Computer Use let the agent work in a browser and operate a computer, alongside a browser extension for signed in browser workflows and Appshots. Browser and Computer Use activity is treated as its own traffic surface outside the command network proxy, with its own feature settings and workspace policies, and managed configuration lets administrators set feature requirements for browsers and Computer Use. Sourcelearn.chatgpt.com/codex/computer-use, /codex/browser and /codex/agent-approvals-securityread 2026-08-30 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | C Cursor |
O OpenAI Codex |
|---|---|---|
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Entry price Lowest public entry point |
Free plan, Plus at $8 a month with Grok models only, Premium at $30 a month, Super at $100, Ultra at $200, Teams from $30 per user a month, and Enterprise at custom pricing. | Included in ChatGPT plans. Free is $0 and Go is $8 a month, with GPT-6 Luna in the desktop app subject to rollout. Plus is $20, Pro starts at $100 ($100, $200 or $500), and Business is $20 per user a month billed annually ($25 monthly). Enterprise and Edu are sold through sales, and an API key pays per token. |
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Pricing confidence How public the numbers are |
Public, partial | Public, partial |
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Billing Primary billing axis |
A monthly plan with a weekly usage pool for Cursor's own models, Grok and Composer. Models from other makers are paid from prepaid credit at API list price on Premium and above. | A ChatGPT plan subscription with an included Codex usage quota, plus usage beyond the quota paid with ChatGPT credits or, on the API key path, at per token API rates. |
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Variable cost Workload / overage exposure |
Medium variable cost | High variable cost |
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Free tier / trial Try before you buy |
Free tier
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Free tier
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Buying motion Self-serve vs sales call |
Mixed | Self-serve |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Cursor
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· Human approval / guardrails
Cursor 3.24 makes Auto-review stop for approval before the agent runs a recursive, force, wildcard or mirror delete whose target could resolve differently than written, and commands that match a user's block instructions now always show the approval card.
Source -
· Agent capability
Cursor launched Rollouts and Security Review for Teams and Enterprise customers.
Source -
· Deployment / data residency
Cursor introduced self-hosted machines, allowing users to keep codebases, build outputs, and secrets on internal infrastructure while the agent handles tool calls locally.
Source
OpenAI Codex
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· MCP / tool calling / API
Codex CLI 0.164.0 lets enterprise MCP connections authenticate with eligible Codex account grants or a dedicated sign in, including OAuth clients that require secrets.
Source -
· Deployment / data residency
Codex CLI 0.161.0 lets the Bedrock Mantle provider accept AWS GovCloud regions, so Codex can send inference to OpenAI models hosted in Bedrock GovCloud.
Source -
· Agent capability
OpenAI shipped GPT-6.1 Sol into Codex on desktop and CLI, describing it as near top tier performance at lower cost for long running work, and made it the default model in the CLI.
Source
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