The hype cycle is over; the utility phase is here. A new practitioner’s guide published on DEV.to cuts through the noise of the current AI agent landscape by categorizing tools into three distinct roles: terminal-first coding agents, shared coordination systems, and agent-building infrastructure. Authored by Jakub Galczyk, the analysis argues that conflating these roles leads to inefficient workflows. The core thesis is simple: an agent that can inspect a repository still needs the right task context, a way to execute work, and a review path. Choosing the wrong layer for the job is the primary failure mode for teams integrating AI into their dev cycle.

The Terminal-First Contenders

For developers living in the shell, the guide highlights Claude Code and tlbx as the primary options. Claude Code is positioned as the go-to for reading and editing files, executing commands, and managing Git directly from the terminal. It’s a native agent that keeps the coding task in a shell-based workflow, ideal for those who prefer command-line control over IDE integration. tlbx is assessed similarly as a platform for working with agents and terminals, though the guide notes that its practical selection depends on verifying how its terminal workflow connects to specific repository tasks. Both tools emphasize immediate code interaction but lack the built-in project management context of larger suites.

IDE Integration and Command Review

Cursor and VS Code with GitHub Copilot Agent represent the IDE-centric approach. Cursor combines codebase search, multi-file edits, and shell command execution within an editor, allowing developers to request changes in project context and inspect edits immediately. The VS Code + GitHub Copilot Agent workflow brings agent-assisted coding into the existing workspace, pairing project files with an integrated terminal. A critical feature highlighted here is the ability to review commands before execution. The guide stresses that command review is a human checkpoint, not a substitute for understanding the action, making these tools suitable for teams that want to maintain strict oversight while leveraging agent speed.

Coordination and Infrastructure Layers

Beyond direct coding, the guide distinguishes tools like ONES.com, which serves as a shared system for coordinating work around agents. ONES.com allows an MCP-compatible client to read issue details and reproduction steps, connect to a Git repository MCP server for actions, and return completion information to the issue. This is coordination, not replacement. On the infrastructure side, OpenAI Agents SDK, Cloudflare Agents, and AWS Bedrock AgentCore are categorized as building blocks. The OpenAI Agents SDK supports tool-using agent development where developers assemble the orchestration, while Cloudflare and AWS provide platforms for running agents in application workflows rather than as interactive coding assistants.

Key Takeaways

  • Terminal-first agents like Claude Code are best for developers who need direct shell control and Git integration without IDE overhead.
  • IDE agents like Cursor and VS Code Copilot excel at multi-file edits and command review, keeping context close to the editor.
  • Coordination platforms like ONES.com bridge the gap between issue tracking and code execution via MCP, ensuring agents work from shared context.
  • Infrastructure tools like OpenAI Agents SDK and AWS Bedrock AgentCore are for building custom agent workflows, not for daily coding tasks.
  • The 'lower TCO than Jira' claim for ONES.com is qualitative; value depends on how well the coordination fits the specific R&D process.

The Bottom Line

Stop treating all AI agents as interchangeable. If you don't need to build the agent, don't buy the SDK. If you need shared context, don't rely on a terminal tool that forgets the ticket after you close the shell. Pick the layer that solves your bottleneck, not the one with the best marketing.