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."
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.
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.
Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.
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 codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.
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.
Using a composable, 'plug and play' architecture allows teams to build specialized AI agents faster and with less overhead than integrating a monolithic third-party tool. This approach enables the creation of lightweight, tailored solutions for niche use cases without the complexity of external API integrations, containing the entire workflow within one platform.
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.
Historically, developer tools adapted to a company's codebase. The productivity gains from AI agents are so significant that the dynamic has flipped: for the first time, companies are proactively changing their code, logging, and tooling to be more 'agent-friendly,' rather than the other way around.