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.

Unlike tools like Zapier where users manually construct logic, advanced AI agent platforms allow users to simply state their goal in natural language. The agent then autonomously determines the steps, writes necessary code, and executes the task, abstracting away the workflow.

Atlassian's CEO argues that AI tools should not just focus on novel capabilities. They must also improve users' current processes (e.g., AI-assisted writing). This dual approach brings the existing user base along while simultaneously showing them new, transformative ways to work, ensuring broader and faster adoption.

A key criterion for selecting tools is now their ability to be controlled by AI agents. Gabor chose Atlassian (Jira/Confluence) specifically because its Model-Component-Package (MCP) allows Claude Code agents to connect and operate the software directly, a critical factor for automating the development lifecycle.

Project management tools like Jira are not obsolete; they are positioned to become the coordination layer for AI agents. As autonomous agents work together on complex tasks, they will require standardized, headless systems for project management and knowledge sharing, creating a new market for agent coordination.

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.

Software development platforms like Linear are evolving to empower non-technical team members. By integrating with AI agents like GitHub Copilot, designers can now directly instruct an agent to make small code fixes, preview the results, and resolve issues without needing to assign the task to an engineer, thus blurring the lines between roles.

To maximize an AI agent's effectiveness, treat it like a team member, not just a tool. Integrate it directly into your company's communication and project management systems (like Slack). This ensures the agent has the full context necessary to perform its tasks.

To drive adoption of AI agents, don't force users into a new application. Instead, integrate the agent directly into their existing collaboration tools like Slack. This approach reduces friction and makes the agent feel like a natural part of the team, leading to higher engagement and user satisfaction.