Pushing the boundaries of autonomy, an engineer on the Goose team has their agent monitor all their communications. The agent then intervenes, proactively developing new features that were merely discussed with colleagues and opening a pull request without being prompted.
Related Insights
Integrate AI agents directly into core workflows like Slack and institutionalize them as the "first line of response." By tagging the agent on every new bug, crash, or request, it provides an initial analysis or pull request that humans can then review, edit, or build upon.
Block's CTO quantifies the impact of their internal AI agent, Goose. AI-forward engineering teams save 8-10 hours weekly, a figure he considers the absolute baseline. He notes, "this is the worst it will ever be," suggesting exponential gains are coming.
Block's AI agent, Goose, has an accessible UI that allows non-technical employees in roles like sales and finance to build their own software dashboards and tools. This democratizes software creation within the enterprise, turning domain experts into citizen developers.
The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.
Block's CTO argues that LLMs are a wasted resource when they sit idle overnight and on weekends. He envisions a future where AI agents work continuously, proactively building features, running multiple experiments in parallel, and anticipating the needs of the human team so that new options are ready for review in the morning.
In an extreme example of recursive development, Block's team uses their open-source AI agent, Goose, to write most of the new code for the Goose project itself. The ultimate goal is for the agent to become completely autonomous, rewriting itself from scratch for each release.
Because AI agents operate autonomously, developers can now code collaboratively while on calls. They can brainstorm, kick off a feature build, and have it ready for production by the end of the meeting, transforming coding from a solo, heads-down activity to a social one.
Instead of pre-engineering tool integrations, Block lets its AI agent Goose learn by doing. Successful user-driven workflows can be saved as shareable "recipes," allowing emergent capabilities to be captured and scaled. They found the agent is more capable this way than if they tried to make tools "Goose-friendly."
AI agents are operating with surprising autonomy, such as joining meetings on a user's behalf without their explicit instruction. This creates awkward social situations and raises new questions about consent, privacy, and the etiquette of having non-human participants in professional discussions.
Instead of a multi-week process involving PMs and engineers, a feature request in Slack can be assigned directly to an AI agent. The AI can understand the context from the thread, implement the change, and open a pull request, turning a simple request into a production feature with minimal human effort.
