Cursor vs. Windsurf: what agentic code editing changes for identity

At a glance

What this is: This is a comparison of Cursor and Windsurf as agentic code editors, with the key finding that their different levels of planning and delegation change how much control developers retain.

Why it matters: It matters because AI-assisted development tools are becoming part of identity-adjacent workflows, and IAM teams need to understand where human review, delegated access, and autonomous execution begin to blur.

👉 Read Descope's comparison of Cursor and Windsurf for agentic code editing

Context

Agentic code editors are development tools that can plan and execute multi-step changes rather than simply suggesting the next line of code. That shift matters for identity governance because it changes how much trust is placed in a software agent to act across files, commands, and contexts without step-by-step human approval.

In practice, the article compares two workflow models: one that keeps a review gate at the center and one that makes the agent the primary interface. For IAM, NHI, and security architecture teams, the important question is not which editor is faster, but which operating model can be governed without losing visibility into delegated actions.

Key questions

Q: How should security teams govern AI coding tools that can edit code autonomously?

A: Security teams should govern AI coding tools as delegated execution systems, not as simple productivity assistants. Define which actions require human approval, what context the tool may inspect, and which terminal or file operations are prohibited. Then log prompts, diffs, and command activity so review, accountability, and rollback remain possible after the session ends.

Q: Why do agentic code editors change the risk model for IAM and security teams?

A: Agentic code editors change the risk model because they can act across multiple files and commands within one session, which compresses the time between intent and execution. That shortens the window for human review and makes traditional approval cadences less reliable. IAM teams should treat that shift as delegated access that needs explicit governance.

Q: What do security teams get wrong about review loops in AI-assisted development?

A: Teams often assume a review loop automatically means control is preserved. In practice, the agent may still make substantial changes before the developer sees them, especially when planning is hidden or optional. The control issue is not whether review exists, but whether review happens early enough to constrain the change path.

Q: What is the difference between agentic assistance and autonomous execution in development tools?

A: Agentic assistance still keeps the human in the decision path, even if the tool can suggest or prepare large changes. Autonomous execution means the system can choose actions, select tools, and continue without approval gates between steps. For governance, that difference determines whether existing review and access controls are sufficient.

Technical breakdown

Agentic coding workflow and review gates

Cursor and Windsurf both use agents to work across a codebase, but they structure control differently. Cursor keeps a review-driven loop where the model can propose broad edits, yet the developer still approves changes before they land. Windsurf puts the agent closer to the center of execution, with planning, file traversal, and terminal activity happening as part of a more continuous session. That difference is not cosmetic. It changes how intent, context, and approval are represented in the workflow, which affects traceability, accountability, and the ability to intervene before changes propagate.

Practical implication: treat agentic editing tools as delegated execution paths and define where human approval is mandatory before changes reach production.

Context awareness, prompt scope, and codebase discovery

The article shows two distinct ways agentic tools consume context. Cursor depends more on explicit scoping through file references, snippets, and manual guidance, which makes the operator responsible for narrowing the model’s view. Windsurf leans more heavily on the agent discovering the relevant context itself before acting. That creates a different governance problem: the quality of the output depends not only on the prompt, but on what the agent decides is relevant enough to inspect. In identity terms, this is about how much authority is embedded in runtime discovery versus preselected scope.

Practical implication: decide whether your controls will be based on explicit scoping, implicit discovery, or a mix of both.

Delegation versus autonomy in developer tools

The comparison highlights a broader distinction that matters beyond software editing: delegated assistance is not the same as autonomous execution. Cursor behaves more like a tightly scoped assistant that still expects the human to steer and review. Windsurf behaves more like an agent that maintains continuity through the task and advances work with fewer checkpoints. For security teams, that distinction matters because once the system starts choosing its own sequence of actions inside a session, traditional review assumptions become weaker. The governance question shifts from tool capability to whether the control model still assumes a stable human operator.

Practical implication: classify AI coding tools by execution model, not by branding, before deciding how they fit into access review and change control.

NHI Mgmt Group analysis

Agentic coding tools are becoming a governance problem before they become a productivity problem. The article is really about how much execution authority a developer is willing to delegate to software that can inspect code, choose actions, and keep moving through a task. That matters because identity controls were built around human-paced approval loops, not around systems that can advance work continuously once given a prompt. Practitioners should treat these tools as an identity governance category, not just an IDE feature.

Human review does not disappear when an agent is present, but its timing changes. Cursor keeps the developer inside the change loop, while Windsurf reduces the number of intervention points during execution. That difference affects auditability, rollback confidence, and who is accountable when generated changes are broader than expected. Security and engineering leaders should not ask only whether the tool is capable, but whether the operating model preserves enough decision points to remain governable.

Agentic development introduces a new form of delegated access risk. A coding agent can touch files, run commands, and carry context across a session in ways that resemble privileged non-human activity, even when the user remains nominally in charge. That places these tools on the same governance continuum as other high-trust identities that act on behalf of a person. The practical conclusion is that access boundaries, session controls, and change approval rules need to reflect the agent's actual execution pattern.

Review-cadence assumptions were designed for human-initiated change requests. That assumption fails when the actor is an agent that can plan, edit, and continue executing within the same session because the change path is no longer stable enough to be reviewed after the fact. The implication is that teams must rethink whether their existing governance model is built for a person steering a tool or a tool effectively steering the work.

Runtime context is now part of the trust boundary. The more the agent decides what to inspect on its own, the more the governance problem shifts from static permissions to dynamic interpretation of scope. That makes provenance, context selection, and action logging central to any credible control model. Practitioners should align agent governance with how the system actually discovers and uses context.

From our research:

• 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
• That is why teams should pair OWASP Agentic Applications Top 10 guidance with explicit session governance before agentic tools become standard developer infrastructure.

What this signals

Agentic development is pushing identity governance toward session-level control. When a tool can keep working after the prompt is given, the relevant control point is no longer just provisioning. It becomes the set of boundaries around what the agent can inspect, change, and execute before a human sees the result. Teams should expect change-control and access-review processes to converge here.

With 98% of companies planning to deploy even more AI agents within the next 12 months, per AI Agents: The New Attack Surface report, governance gaps will widen faster than policy updates can close them. That is especially true where development agents are embedded in everyday engineering workflows and inherit broad contextual access. Programmes that still treat these tools as optional productivity add-ons will miss the access-risk reality.

The practical signal for security leaders is that agentic coding tools should be evaluated the same way other high-trust non-human identities are evaluated. That means tighter scoping, stronger logging, and a clear decision on where human approval is required before code changes are allowed to propagate.

For practitioners

• Define an approval boundary for agentic code changes Require explicit review before any agent-driven edit can merge, especially when the tool can modify multiple files or invoke terminal commands. Treat plan mode, diff review, and execution mode as separate control points rather than one continuous workflow.
• Classify agentic editors as delegated identities Document what the tool can read, change, and execute during a session, then map those rights to change management and privileged access expectations. If the tool can act beyond simple autocomplete, it needs governance comparable to other high-trust non-human identities.
• Instrument session-level logging for agent actions Capture the prompt, context sources, file changes, and terminal activity tied to each session so reviewers can reconstruct intent after the fact. This is especially important when the agent can continue without frequent checkpoints.
• Limit agent context to least-required scope Use explicit file references, workspace scoping, and task boundaries to prevent the agent from discovering or modifying unrelated parts of the codebase. The goal is to reduce unintended drift, not just improve model accuracy.

Key takeaways

• Agentic code editors do more than speed up development. They alter where authority, review, and accountability sit inside the delivery process.
• The core governance issue is not whether an editor uses AI. It is whether the system can continue executing before a human has a chance to constrain it.
• Teams should classify these tools as delegated non-human identities and govern them with scoped access, logging, and pre-merge approval boundaries.

Key terms

• Agentic Code Editor: An agentic code editor is a development tool that can plan, edit, and sometimes execute tasks across a codebase instead of only suggesting text. In practice, it behaves like a delegated work partner, so identity and approval boundaries matter as much as coding quality.
• Delegated Execution: Delegated execution is when a tool performs actions on behalf of a user within a defined trust boundary. For AI-enabled development, it means the system can change files, run commands, or carry context forward, which requires governance similar to other high-trust non-human identities.
• Review Gate: A review gate is a control point where a human must inspect and approve work before it continues or is committed. In agentic workflows, review gates are only effective if they happen early enough to constrain the agent's path, not just after the fact.
• Context Scope: Context scope is the set of files, data, and instructions an AI tool is allowed to inspect while doing work. For agentic systems, scope is a control surface because broader discovery can increase both accuracy and unintended exposure if it is not deliberately bounded.

What's in the full article

Descope's full blog post covers the operational detail this post intentionally leaves for the source:

• Side-by-side walkthrough of the Cursor and Windsurf workflows across planning, execution, and review.
• Detailed feature comparison of context handling, model selection, enterprise controls, and pricing tiers.
• Observed differences in generated UI behavior, code volume, and review burden from the test task.
• Developer-focused decision guidance for choosing between a review-driven workflow and an agent-first workflow.

👉 Descope's full post covers the workflow differences, code quality trade-offs, and context handling details.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.