Agentic work management
Agentic work management is Port's platform layer for running AI agents safely across engineering. We do not provide the agents. We provide the registries, governance, and orchestration that let your teams adopt agents from Cursor, Claude Code, or any MCP-compatible tool, without losing visibility or control.
What is agentic work management?
Engineering organizations are adopting AI agents at every layer of the stack. Squads spin up their own agents, build their own skills, and connect their own MCP servers. The result is agentic chaos: duplicated work, inconsistent quality, no governance, and no visibility into what agents are doing or how well they perform.
Agentic work management gives platform teams a single place to:
- Define which agents and skills are sanctioned for company-wide use.
- Connect company systems to agents through a managed MCP layer.
- Govern who can run what, with approvals where it matters.
- Measure adoption, reuse, and impact.
Solution components
| Component | What it does | Who uses it |
|---|---|---|
| Skills registry | A central library of reusable skills (prompts, tool bundles, instructions) that platform teams publish and developers pull into their IDE with one CLI command. | Platform teams publish, developers consume |
| Agent registry | A catalog of approved agents with their owners, capabilities, and usage policies. Includes Port's built-in AI agents and any third-party or in-house agents you onboard. | Platform teams, AI/ML teams |
| MCP registry & connectors | Managed MCP servers that expose Port's catalog, actions, and integrations to any MCP-compatible client. Bring company context to agents without custom plumbing. | Platform teams, developers |
| Governance & approvals | Role-based access, approval workflows, and audit trails that apply to every agent action. Humans stay in the loop on the steps that matter. | Platform teams, security, engineering leadership |
How Port makes it work
- Context lake. The Port software catalog is the source of truth agents reach into. Every skill, every MCP tool, every workflow pulls from the same model of services, owners, environments, and standards.
- Bring your own agent. Port does not lock you into a single AI vendor. Use Claude Code, Cursor, your own LangGraph stack, or anything MCP-compatible. Port governs the platform; you choose the agents.
- Humans in the loop. Approvals, RBAC, and dynamic permissions are first-class. The same governance you trust for self-service actions applies to agents.
Next steps
- Build an AI agent: configure a Port-managed agent against your catalog.
- Interact with AI agents: trigger and track agent runs from Slack, the UI, or workflows.
- Skills: publish reusable skills to the registry and pull them into your IDE.
- Port MCP server: connect any MCP-compatible client to Port.
Port integrates with the tools your agents already use: GitHub, GitLab, Jira, Slack, PagerDuty, AWS, Kubernetes, and 50+ other platforms.