Introduction

AI coding agents moved from autocomplete to hands-on collaborators. They plan tasks, change many files, run commands, and open pull requests. They now live in IDEs, terminals, CI, and cloud sandboxes. This post maps the 2025 landscape, shows the agents that matter, explains common uses, tracks the trends, and closes with a pragmatic adoption path.

The agents that matter now

GitHub Copilot coding agent

Copilot’s coding agent turns a plain-English task into a background run that proposes a pull request. You can start it from GitHub.com, the GitHub CLI, supported IDEs, or an agents panel. It gathers context, edits code, runs tools, and pushes incremental commits to a draft PR for review. Teams keep control with branch policies and CI gates. Copilot agent mode in VS Code also exposes tool calls and terminal steps so you can approve or undo each action quickly. The key value is tight integration with GitHub repos so you always know what changed and why.

Cursor Agent

Cursor is an IDE that centers the agent experience. The agent can run commands, modify many files, and generate reviewable diffs. You stay in flow because the edit loop, terminal actions, and explanations live in one place. Teams like Cursor for repo-scale rewrites and for small, fast inner-loop edits where the context is already in the editor.

JetBrains Junie

Junie is JetBrains’ in-IDE coding agent. It navigates project structure, uses inspections, runs code and tests, and applies changes while surfacing plans and logs. It is built to feel native in JetBrains IDEs, which makes it strong for JVM shops and for projects that rely on JetBrains’ static analysis. MCP support and remote-dev compatibility broaden where and how it runs.

Windsurf with Cascade

Windsurf markets an “agentic IDE” experience with Cascade at the center. Cascade plans, executes, and keeps you in the loop with a flow-aware UI. It aims to reduce context switching by keeping work inside the editor while still exposing each action for review.

Where agents deliver value

Describe the goal, link an issue, and let the agent plan, edit, test, and open a pull request. Keep the scope narrow. Use branch protections and CI to gate the merge. This is the best first use because success is clear and risk is low.

Refactor and migrate

Batch renames, dependency upgrades, and API migrations fit agents well. Ask for a plan, approve the steps, and let the agent loop on compile errors and tests. IDE-native agents shine here because they show diffs in context and can coordinate edits across many files.

Write and fix tests

Have the agent generate missing unit tests and smoke tests, run them, and iterate until green. For apps with a UI, agents that drive a real browser add value because they can see what the app does and correct obvious regressions.

Repo Q&A and docs

Point the agent at the codebase and ask how a subsystem works, where to add a feature, or how to reproduce a bug. Turn the answers into READMEs or onboarding notes. This reduces ramp-up time for new contributors.

Terminal automation

CLI agents execute repetitive chores: scaffolding projects, running linters, formatting, applying codemods, or preparing release notes. Approval modes and logs keep the loop auditable. This helps developers who live in the shell.

PR triage and review assist

Agents summarize diffs, flag risky areas, and suggest small fixes. They do not replace review. They reduce toil and focus humans on important decisions.

Security and code health

Run lint, SAST, and style checks via the agent, then auto-apply safe fixes. Pair with pre-commit hooks and CI to enforce standards. The agent reduces backlog without hiding what changed.

From suggestions to execution

The shift is from token-level completion to task-level work. Agents plan, call tools, execute commands, and keep changing code until tests pass. GitHub’s coding agent, OpenAI’s Codex, Gemini CLI, Claude Code, and Replit Agent 3 all point in this direction.

MCP as the plumbing

Model Context Protocol gives agents a standard way to reach tools and fresh data. That lowers integration cost and improves security posture because permission scopes sit at the protocol layer. IDEs and agents that speak MCP gain a larger tool market overnight.

Terminal-first agents

Command-line agents matured. Claude Code, Gemini CLI, Amazon Q CLI, and Codex CLI deliver fast loops with visible plans and audited commands. This matches developer muscle memory and cuts context switching.

IDEs becoming agent platforms

Cursor, Windsurf, JetBrains, and VS Code agent mode push plan-and-apply flows with transparent logs and undo. Expect tighter hooks into test runners, debuggers, and build tools so agents can fix their own mistakes faster.

Longer autonomous runs with guardrails

Cloud agents spin up sandboxes or VMs, work for hours, and post PRs as they go. Approval gates, audit logs, and per-task budgets keep autonomy safe. Teams standardize on small tasks and merge often to reduce risk.

Build a secure operating model

Prefer local or sandboxed runs. Log every action. Vet MCP servers and extensions. Rotate keys if you add or remove tools. Train reviewers to spot risky changes.

Decide your control plane

Pick where agents live for each task: IDE, CLI, CI, or cloud. Many teams mix modes: IDE for small edits, CLI for terminal chores, and cloud agents for background feature work. Document when to use which.

What to use when

  • GitHub-centric shops: Copilot coding agent for task-to-PR and VS Code agent mode for plan-and-apply loops. Strong GitHub integration and review flow.
  • Terminal power users: Claude Code, Gemini CLI, Amazon Q CLI, or Codex CLI. Approval modes and clear logs fit shell workflows.
  • IDE-first teams: Cursor, Windsurf, or JetBrains Junie for diffs, action logs, and native inspections.
  • Cloud dev platforms: Replit Agent for autonomous scaffolds, browser tests, and progress tracking.
  • Open and extensible: Cline and Continue for model choice, MCP tooling, and client-side architecture.

Adoption path for 2025

Start with visible, low-risk wins. Pilot agents on documentation updates or small feature branches. Measure outcomes. Expand gradually. Keep humans in the loop.

Tags

AI, Agents