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 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.

AI's impact on coding is unfolding in stages. Phase 1 was autocomplete (Copilot). We're now in Phase 2, defined by interactive agents where developers orchestrate tasks with prompts. Phase 3 will be true automation, where agents independently handle complete, albeit simpler, development workflows without direct human guidance.

AI code editors can be tasked with high-level goals like "fix lint errors." The agent will then independently run necessary commands, interpret the output, apply code changes, and re-run the commands to verify the fix, all without direct human intervention or step-by-step instructions.

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.

Using AI agents in shared Slack channels transforms coding from a solo activity into a collaborative one. Multiple team members can observe the agent's work, provide corrective feedback in the same thread, and collectively guide the task to completion, fostering shared knowledge.

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.

AI acts as a massive force multiplier for software development. By using AI agents for coding and code review, with humans providing high-level direction and final approval, a two-person team can achieve the output of a much larger engineering organization.

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.