Block is re-architecting its entire business by treating all functions—from payments to HR—as a collection of capabilities. These are unified and accessed through a central AI agent middleware layer (Goose), orchestrating workflows across previously siloed product and corporate functions.

Tim McLear used AI coding assistants to build custom apps for niche workflows, like partial document transcription and field research photo logging. He emphasizes that "no one was going to make me this app." The ability for non-specialists to quickly create such hyper-specific internal tools is a key, empowering benefit of AI-assisted development.

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.

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."

Prototyping and even shipping complex AI applications is now possible without writing code. By combining a no-code front-end (Lovable), a workflow automation back-end (N8N), and LLM APIs, non-technical builders can create functional AI products quickly.

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.

The key to driving AI adoption at Block was leadership by example. CEO Jack Dorsey and CTO Danji Prasana use their internal AI tool, Goose, daily. They argue this hands-on approach provides more insight into organizational workflow changes than any top-down mandate or analysis of industry reports.

At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.

Visual AI tools like Agent Builder empower non-technical teams (e.g., support, sales) to build, modify, and instantly publish agent workflows. This removes the dependency on engineering for deployment, allowing business teams to iterate on AI logic and customer-facing interactions much faster.