There’s been a shift in how the best AI coding tools work, and it’s worth being clear about what’s actually different. Autocomplete-style tools — GitHub Copilot in its original form, most IDE extensions — generate completions in the file you’re looking at. They’re context-limited to what’s nearby and react to where your cursor is. That’s genuinely useful. It’s also a fundamentally different paradigm from what Windsurf, OpenCode, and a handful of similar tools are now doing.

The agentic approach is closer to giving a task to a junior developer and walking away. You describe what you want to happen. The tool reads the relevant parts of your codebase, forms a plan, makes changes across multiple files, runs tests or linters, observes the results, and iterates until either the task is done or it’s stuck and needs your input. It’s not autocomplete with a longer context window. It’s a different interaction model.

Windsurf’s Cascade

Codeium’s Windsurf editor launched Cascade as its differentiating feature. Cascade is a “flow” — not a chat assistant but a persistent agent that maintains awareness of your codebase and executes tasks end-to-end. The design choice that matters is that Cascade reads the parts of your codebase it needs, not just what you explicitly paste into the chat.

When you tell Cascade “add rate limiting to all API routes,” it searches your routes directory, identifies the pattern, finds the right middleware location, makes the changes, and if your project has tests for the affected routes, runs them to check the result. If a test fails, it reads the error and tries again. You review the diff.

This works well for refactoring tasks with consistent patterns across a codebase, for adding cross-cutting concerns (logging, error handling, validation), and for implementing features that follow an established pattern in the project. It struggles with tasks that require understanding business context, making architectural decisions without guidance, or working in codebases with unusual or inconsistent patterns that make “find the pattern and apply it” difficult.

The pricing model is subscription-based, competing with Cursor and the GitHub Copilot/VS Code combination.

OpenCode

OpenCode hit 160,000 GitHub stars in its first few weeks — a number that reflects how much developer interest there is in terminal-native AI coding. It’s a TUI (terminal user interface) tool: you open it in your project directory, describe a task, and it works on your codebase using the same agentic loop as the GUI tools but entirely in the terminal.

For developers who live in the terminal and find switching to a GUI editor for AI assistance disruptive, OpenCode is the answer. It uses your existing editor (or a built-in one) for file viewing and integrates with your existing shell workflow. If you’re doing remote development over SSH or working in environments where a full GUI isn’t practical, terminal-native agentic tools make a lot more sense than editor plugins.

OpenCode is model-agnostic — you configure it to use Claude, GPT-4o, or local models. That flexibility matters if you have cost constraints or want to use a locally-running model for privacy reasons.

How agentic editors differ from copilot-style tools

The honest comparison requires acknowledging that “agentic” tools aren’t uniformly better — they’re better at different things.

Copilot-style autocomplete is better when:

  • You’re in active writing mode and want suggestions as you type
  • The task is well-defined and contained to the current function
  • You want to stay in the driver’s seat and incrementally accept suggestions
  • Speed of iteration matters more than autonomy

Agentic tools are better when:

  • The task spans multiple files and following the chain manually is tedious
  • You have a clear specification but implementing it requires reading a lot of existing code to understand patterns
  • You want to delegate a task and review the output rather than generate it incrementally
  • The task involves repetitive changes across many files (renaming, adding a parameter everywhere, updating an interface implementation)

The failure modes are different too. Autocomplete suggests something wrong and you notice immediately because you’re actively reviewing each line. Agentic tools make changes across many files, and if you approve without careful review, you might not notice a subtle mistake until later. The blast radius of a missed error is larger because the edits are larger.

What actually works in practice

A few things that experienced users of these tools report consistently:

Specification quality determines output quality. “Make the auth system better” gets poor results. “Add JWT refresh token support to the auth middleware with a 7-day refresh window, following the existing pattern in auth/tokens.ts” gets good results. The more precisely you specify the task and point to existing patterns, the better the output.

Checkpoints help. For long tasks, breaking them into steps and reviewing the output of each step before continuing catches mistakes before they compound. Most agentic tools support this — you can pause, review, and continue.

Don’t use them for tasks that require architectural judgement. If you’re genuinely unsure how to structure something and you want input, a conversational tool (Claude, ChatGPT) will give you better architectural thinking than an agentic editor that will just make a decision and implement it.

Test coverage limits the blast radius. An agentic editor that runs tests after each change can self-correct. Without tests, you’re trusting the model’s judgement on correctness. If you’re using these tools seriously, investing in test coverage first pays off in the quality of agentic tool outputs.

The tooling is maturing fast. Where we are in mid-2026 is noticeably further along than 12 months ago. Whether the current generation of agentic editors is good enough to change your workflow depends on your project type and how much delegation you’re comfortable with — but the direction of travel is clear.