OpenHands: Capable but Requiring InterventionI connected my repository to OpenHands through the All Hands cloud platform. I pointed the agent at a specific issue, instructing it to follow the detailed requirements and create a pull request when complete. The conversational interface displayed the agent's reasoning as it worked through the problem, and the approach appeared logical.
Also used openhands for a test. says it needs intervention (not fully delegated iow)
we tend to underspecify because we're exploring, experimenting, and can provide immediate course corrections. We might type a quick prompt, see what the AI produces, and refine from there. This exploratory approach works when you're actively engaged
indeed. as mentioned above too. n:: My sense that this is a learning mode akin to the haptic feedback of working on things by hand.
The fundamental rule for working with asynchronous agents contradicts much of modern agile thinking: create complete and precise task definitions upfront. This isn't about returning to waterfall methodologies, but rather recognizing that when you delegate to an AI agent, you need to provide all the context and guidance that you would naturally provide through conversation and iteration with a human developer.
What I mentioned above: to delegate you need to be able to fully describe and provide context for a discrete task.
The ecosystem of asynchronous coding agents is rapidly evolving, with each offering different integration points and capabilities:GitHub Copilot Agent: Accessible through GitHub by assigning issues to the Copilot user, with additional VS Code integrationCodex: OpenAI's hosted coding agent, available through their platform and accessible from ChatGPTOpenHands: Open-source agent available through the All Hands web app or self-hosted deploymentsJules: Google Labs product with GitHub integration capabilitiesDevin: The pioneering coding agent from Cognition that first demonstrated this paradigmCursor background agents: Embedded directly in the Cursor IDECI/CD integrations: Many command-line tools can function as asynchronous agents when integrated into GitHub Actions or continuous integration scripts
A list of async coding agents in #2025/08 github, openai, google mentioned. OpenHands is the one open source mentioned. mentions that command line tools can be used (if integrated w e.g. github actions to tie into the coding environment) - [ ] check out openhands agent by All Hands
isn't just about saving time — it's about restructuring how software gets built.
not just time saving, but a restructuring. So, any description of how the structure changes (before / after style) further down?
several of these tasks running in parallel, each agent working independently on different parts of your codebas
do multiple things in parallel. note: The assumption here that the context is coding
You prepare a work item in the form of a ticket, issue, or task definition, hand it off to the agent, and then move on to other work.
compares delegation to formulating a 'ticket'. Assumes well defined tasks up front I think, rather than exploratory things.
While interactive AI keeps you tethered to the development process, requiring constant attention and decision-making, asynchronous agents transform you from a driver into a delegator.
async means no handholding, but delegation instead. That is enticing obviously, but assumes unattended execution can be trusted. Seems a big if.
Asynchronous coding agents — also called background agents or cloud agent
cloud agent = background agent = async agent
why asynchronous agents deserve more attention than they currently receive, provides practical guidelines for working with them effectively, and shares real-world experience using multiple agents to refactor a production codebase.
3 things in this article: - why async agents deserve more attention - practical guidelines for effective deployment - real world examples
asynchronous coding agents represent a fundamentally different — and potentially more powerful — approach to AI-augmented software development. These background agents accept complete work items, execute them independently, and return finished solutions while you focus on other tasks.
Async coding agents is a diff kind of vibe coding: you give it a defined more complex tasks and it will work in the background and come back with an outcome.
… a fundamental characteristic of complex human systems … [is that] cause and effect are not close in time and space. By effects, I mean the obvious symptoms that indicate that there are problems drug abuse, unemployment, starving children, falling orders, and sagging profits. By cause I mean the interaction of the underlying system that is most responsible for generating the symptoms, and which, if recognized, could lead to changes producing lasting improvement. Why is this a problem? Because most of us assume they are most of us assume, most of the time, that cause and effect are close in time and space.