Six weeks ago, one agentic coding tool had shipped with a documented plan-approval mechanism. Today there are four.
That’s not a coincidence. It’s a design response to a specific failure mode, the one where an AI agent rewrites half a codebase without asking, and the developer discovers the damage after the fact. Plan-mode approval exists because production teams got burned. The question isn’t whether the mechanism matters. It does. The question is whether the four tools that now claim it are actually building the same thing.
They aren’t.
The Convergence Signal
xAI entered the agentic coding market on May 25 with Grok Build, framing its plan-review-approve loop as a core architectural feature rather than a safety overlay. According to xAI, Grok Build generates a full implementation plan before executing any code, displays proposed changes as Git diffs, and requires explicit developer approval at the planning stage. That’s an upstream approval gate, the human decides before the agent acts, not after.
Claude Code and OpenAI Codex Goal Mode, which shipped earlier in this cycle, both offer approval mechanisms, but positioned differently. Codex Goal Mode presents plans for approval before execution as well, per OpenAI’s published documentation. Claude Code’s approval flow is more granular: it gates on specific tool-use calls (file writes, shell commands, web fetches) rather than on a pre-execution plan. The distinction isn’t trivial. A plan-level gate is coarser. A tool-use gate is more precise. Which one you want depends on how much you trust the plan and how much you trust the execution.
Cursor Composer 2.5 sits in a different category. It operates within the IDE rather than as a standalone CLI, and its approval model is diff-review, you see what changed, accept or reject. That’s a post-execution gate, not a pre-execution one. It’s the most familiar interaction model for developers already comfortable with version control, and it’s the one with the most independent usage data behind it.
What the Architecture Differences Actually Mean
The practical distinction comes down to one question: at what point does the developer’s judgment enter the loop?
Pre-execution plan approval (Grok Build, Codex Goal Mode) requires the developer to evaluate an abstract plan, a description of what the agent intends to do. That’s harder than reviewing a diff. A developer can look at a diff and see exactly what changed. Evaluating a plan requires predicting whether the agent’s intended approach will produce the output the developer actually wants. It demands more cognitive engagement at the front end, but it also means bad approaches get caught before any code runs.
Post-execution diff review (Cursor Composer 2.5, and partially Claude Code) requires less upfront judgment but puts the developer in the position of auditing work already done. There’s a documented behavioral tendency, in humans reviewing AI output, to approve diffs more readily than to challenge plans. The output looks complete. Rejecting it feels disruptive. Pre-execution gates sidestep that psychology, at least in theory.
The part nobody mentions about pre-execution plan approval: the plan is only as reliable as the model’s ability to accurately describe what it’s going to do. An agent that plans correctly but executes differently, due to context window limitations, ambiguous intermediate states, or tool interaction side effects, defeats the gate entirely. This is an open research problem, not a solved one. None of the four tools have published data on plan-execution fidelity.
Unanswered Questions
- Does the approval gate apply to shell command execution, or only file writes?
- Do any of the four tools publish data on plan-execution fidelity (how often agents do what they planned)?
- What happens when an agent's approved plan encounters an unexpected intermediate state mid-execution?
Warning
Plan-execution fidelity is the unaddressed variable in every plan-mode approval claim. An agent that plans correctly but executes differently, due to context window limits or tool interaction side effects, defeats the approval gate. None of the four tools have published data on this. It's not a minor caveat. It's the architectural assumption the entire safety case rests on.
The Benchmark Landscape
This is where the comparison gets uncomfortable.
Grok Build’s eval status is pending, no independent benchmark evaluation has been published. xAI’s capability claims are vendor-stated. Cursor Composer 2.5 has the most real-world usage data behind it, accumulated over months of developer adoption, though that data isn’t systematically published in a form that supports direct comparison. Claude Code and Codex Goal Mode both have some independent evaluation coverage, but benchmark methodology varies enough across evaluators that direct score comparisons are unreliable without controlling for task type, context length, and evaluation harness.
What Epoch AI’s model evaluation work establishes, and this matters for anyone trying to make a procurement decision, is that vendor-reported benchmarks and independent evaluations frequently diverge on coding-specific tasks. Epoch AI’s evaluation methodology distinguishes between held-out test sets and benchmarks the models have seen during training. Most vendor benchmark claims don’t specify. Most independent evaluations do. Until Grok Build, and to a lesser extent the other three tools, are run against a held-out coding task evaluation with published methodology, comparative capability claims should be treated as positioning, not evidence.
Pricing and Access Economics
The access models tell a secondary story about each vendor’s intended market.
Grok Build is bundled with SuperGrok and X Premium+ subscriptions, no additional cost for existing subscribers. That’s a consumer and prosumer play. xAI is betting that developer adoption happens at the individual level first, then scales to organizational use. The enterprise channel option in the CLI (stable/alpha/enterprise) suggests organizational deployment is planned, but pricing hasn’t been disclosed.
Codex Goal Mode reached general availability earlier in this cycle, with API pricing for enterprise customers. That’s the opposite approach, GA with commercial terms, targeting teams that need a contract, not a subscription. Claude Code sits in a similar position: API-first, with usage costs that enterprise procurement teams can model.
Cursor Composer 2.5 operates on a seat-licensing model, a structure familiar to development teams already paying for IDE tooling. It doesn’t require a separate API relationship with an AI vendor. That procurement simplicity is a real advantage in organizations where AI API contracts require legal review.
The pricing structures aren’t just commercial decisions. They signal who each tool is actually built for. Grok Build is for individual developers experimenting now. Codex and Claude Code are for engineering organizations buying production capacity. Cursor is for teams that want to stay inside their existing toolchain budget.
Evidence
What to Watch
What Enterprise Adoption Decisions Should Actually Weigh
Four tools, four approval models, four pricing structures, and one genuinely unanswered question that determines enterprise risk posture: what operations does the approval gate actually cover?
File writes are the obvious one. Most developers assume the approval gate applies to file writes. But shell command execution, network requests, and environment variable access are where the real attack surface lives in agentic systems. A tool that gates file writes but not shell commands gives teams a false sense of control. None of the four tools have published explicit documentation specifying every operation type covered by their approval mechanism.
That’s the procurement question. Not “which tool scores highest on SWE-Bench?” Ask each vendor: does your plan-mode or approval gate apply to shell execution? To outbound network requests? To credential access? Get the answer in writing before the tool touches a production environment.
TJS Synthesis
Plan-mode convergence is real. The underlying problem it solves, agents that act without asking, is real. But four tools claiming similar features in six weeks doesn’t mean the features work the same way or carry the same risk profile. The architectural differences (pre-execution plan vs. post-execution diff, tool-use granularity, plan-execution fidelity) are meaningful and currently underdocumented across all four tools.
Don’t wait for a definitive benchmark comparison before adopting one of these tools. You’ll be waiting a long time. Run each tool against a specific subset of your team’s actual tasks, not synthetic benchmarks, in a sandboxed environment, with explicit documentation of which operations each tool executes without approval. The team that publishes that internal evaluation will have more actionable data than any external benchmark published this year.