Integrating Autonomous AI Agents into Scrum Teams with Paca

Practical Summary of Paca

Paca is a self-hosted, open-source project management platform designed to treat AI agents as first-class teammates within a Scrum framework. Unlike traditional tools that treat AI as peripheral chatbots, Paca allows agents to participate directly in sprint planning, task execution, and documentation. By utilizing a lightweight core and a plugin-based architecture, Paca enables teams to maintain a unified Scrumban board where human and AI contributions are visible in real time. The platform is built to support the Cynefin framework's approach to complex domains, ensuring that AI agents can probe, sense, and respond to project requirements alongside human developers.

Prerequisites and System Requirements

To deploy Paca, you require a Linux server with Docker installed. The platform is designed to be self-hosted, ensuring that your project data remains within your own infrastructure. For production environments, the recommended approach is to use the interactive install script provided by the project. If you are operating within a Kubernetes environment, Paca provides a Helm chart published as an OCI artifact. Regardless of the deployment method, you must manage your own secrets, including jwtSecret, adminPassword, and encryptionKey, as the system does not provide default values for these critical security components.

Installation and Setup

The most efficient way to get started is via the interactive install script, which handles configuration and service startup. For automated environments such as CI/CD pipelines or when directed by an AI agent, you can perform a non-interactive installation by setting the PACA_YES environment variable. This prevents the script from blocking on user prompts. The following command demonstrates the non-interactive installation method:

PACA_YES=1 bash <(curl -fsSL https://github.com/Paca-AI/paca/releases/latest/download/install.sh)

Once installed, you can access the Paca interface via your server's IP address. For Kubernetes users, the installation is performed using helm install, referencing the OCI registry at oci://ghcr.io/paca-ai/charts/paca. Ensure that your my-values.yaml file is correctly configured with your publicUrl and required secrets before deployment.

The P-A-C-A Workflow

Paca structures team collaboration around a four-phase cycle: Plan, Act, Check, and Adapt. During the Plan phase, Product Owners, Business Analysts, and AI agents refine the backlog and co-author BDD scenarios and System Design Documents. In the Act phase, the sprint is live, and both humans and AI agents pull tasks from the board to execute work. The Check phase involves QA agents running automated verification while humans review the output. Finally, the Adapt phase uses data from the completed sprint to inform the next cycle, allowing the entire team to conduct retrospectives based on real-time performance metrics.

Connecting AI Agents via MCP

Paca includes a Model Context Protocol (MCP) server, which allows any compatible AI agent to interact directly with your workspace data. This integration provides structured access to projects, tasks, sprints, and documents without requiring custom API development. To connect an agent like Claude Desktop, you must update the configuration file with the server details. The following configuration snippet illustrates how to define the Paca MCP server:

{ "mcpServers": { "paca": { "command": "npx", "args": ["-y", "@paca-ai/paca-mcp"], "env": { "PACA_API_KEY": "your-api-key-here", "PACA_API_URL": "http://localhost:8080" } } } }

After updating the configuration and restarting the client, the AI agent can perform tasks such as listing active sprints, creating new tasks, or adding comments to existing work items.

Extensibility and Customization

Paca is highly configurable, allowing teams to adapt the platform to their specific processes without writing custom code. Workflows, statuses, and board layouts are managed through project-level configuration files. For more advanced requirements, Paca features a plugin system where plugins are compiled to WebAssembly (WASM). These plugins run in a sandboxed environment with a capability-based permission model, ensuring that they only access the host functions they explicitly require. Users can browse and install community plugins directly through the Paca UI by navigating to Settings → Plugins → Marketplace.

Limitations and Considerations

While Paca offers significant advantages for AI-integrated Scrum teams, it is important to note that it is a self-hosted platform, meaning the responsibility for maintenance, security, and backups lies with the user. The platform is designed to be lightweight, which means it avoids the feature bloat found in some enterprise tools, but this also requires teams to be intentional about which plugins they install to meet their specific needs. Furthermore, while the platform supports various AI agents, the effectiveness of the integration depends on the capabilities of the specific agent being connected via the MCP server or the ACP bridge.

When to Use Paca

Paca is best suited for development teams that are already comfortable with self-hosting infrastructure and are looking to move beyond simple AI chatbots toward true autonomous agent collaboration. It is an ideal choice for teams practicing Scrum or Scrumban who want to integrate AI into their daily workflow, from backlog refinement to sprint retrospectives. By providing a unified board and a transparent, event-driven automation engine, Paca helps teams manage complexity in a way that mirrors the scientific method. For more information on the project's vision and technical details, visit the official repository at https://github.com/Paca-AI/paca.