One of the hottest topics right now in the GenAI space is the concept of software engineering agents. These coding assistance tools do more than just help the engineer with code snippets here and there; they broaden the size of the problem they can solve, ideally autonomously and with minimum interference from a human. The idea is that these tools can take a GitHub issue or a Jira ticket and propose a plan and code changes to implement it, or even create a pull request for a human to review. While this is the next logical step to increase the impact of AI coding assistance, the often advertised goal of generic agents that can cover a broad range of coding tasks is very ambitious, and the current state of tooling is not showing that convincingly yet. However, we can see this working sooner rather than later for a more limited scope of straightforward tasks, freeing up developer time to work on more complex problems. Tools that have been released with beta versions of agents include GitHub Copilot Workspace, qodo flow, Tabnine's agents for JIRA, or Amazon Q Developer. The SWE Bench benchmark lists more tools in that space, but we caution you to take benchmarks in the AI space with a grain of salt.
