AI coding agents work inside isolated or persistent cloud sandboxes and can also be invoked from CI/CD triggers, scheduled jobs, scripts, and chatops workflows to investigate issues, implement changes, generate artifacts, and open draft pull requests or PR-ready branches for asynchronous human review.
Engineers and platform teams still decide what work to delegate, define the architecture and guardrails, choose which automation entry points are safe, validate code quality and generated artifacts, review pull requests and logs, and make the final merge or deployment decisions.
The agent picks up a Jira ticket, implements the requested change in an isolated sandbox, and opens a draft pull request linked to the issue for review.
Devin / GitHub Copilot coding agentThe agent uses bug-report context such as stack traces and reproduction notes to prepare a fix branch and draft pull request for an engineer to validate.
DevinAn agent can attach benchmark results, screenshots, logs, or migration notes to its pull request so reviewers get full context without rerunning the work locally.
OpenHands / CodeRabbitI help companies navigate AI adoption -- from strategy to production. Whether you are building your first LLM-powered feature or scaling an agentic system, I can help you get it right.
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Now in early beta for SuperGrok Heavy subscribers — Grok Build is a new coding agent that runs right from your terminal.
Today we're launching an early beta of Grok Build, a powerful new coding agent and CLI for professional software engineering and complex coding work.
I'm paying for three dedicated AI subscriptions and somehow that's the minimum.
- Claude for thinking and writing.
- GitHub Copilot for cloud agents that run while I review.
- OpenCode for cheaper open models when I want to offload.
Then Perplexity (from a Pro account) for research that actually cites its sources. Gemini (from Google One) for images and calendar integrations.
Each one has one job. None of them do all the others.
Here's what actually changed my workflow: I stopped writing code and started steering it.
My day looks like: describe a feature to Copilot's agent, switch to Claude to plan the next one while it works, come back to review what it built, ask for adjustments, repeat.
The dynamic changed from "I write, AI assists" to "AI writes, I review and direct."
GitKraken 12.0 helped with this too. Agent Mode lets me run multiple coding agents in parallel without terminal gymnastics. One panel shows status for all active sessions. One click spins up a new worktree. One click cleans it up when you're done.
Non-agentic tools haven't changed much: Capacities for notes, TickTick for tasks, Helium Browser for browsing without Google's telemetry, Warp for terminal multiplexing.
The stack works. But I'm aware half of it might not exist in six months.
What's your workflow look like? Still writing most of the code yourself?
#ai #developer #workflow #typescript #freelance
For most of its life, Cursor has been an IDE. A very good one. But with the public beta of the Cursor SDK, the company is making a different kind of move — one that should get the attention of DevOps teams.
The Cursor SDK is a TypeScript library that gives engineers programmatic access to the same runtime, models, and agent harness that power Cursor’s desktop app, CLI, and web interface. In short, the agents that used to live inside an editor can now be invoked from anywhere in your stack.
That’s a meaningful shift in how AI coding tools fit into software delivery pipelines.
To solve this new problem, we built a system called Symphony. Symphony is an agent orchestrator that turns a project-management board like Linear into a control plane for coding agents. Every open task gets an agent, agents run continuously, and humans review the results.
This post explains how we created Symphony—resulting in a 500% increase in landed pull requests on some teams—and how to use it to turn your own issue tracker into an always-on agent orchestrator.