Agent management
Agent management is about governing every AI agent operating in your organization - what data they can access, what actions they can take, and who can use them. That includes code-first agents you build with SDKs in your own repos or CI (for example OpenAI Agents SDK, Anthropic Claude Agent SDK, or Claude Code), cloud-managed agents hosted on vendor control planes (for example AWS Bedrock / AgentCore, Azure AI Foundry, or Cursor Cloud Agents), and Port-native agents configured inside Port.
Govern agents, skills, and MCP at scale
As engineering teams adopt AI agents, the number of agents, skills, prompts, and MCP servers grows quickly. Without a shared layer to discover and govern them, each team operates in isolation — and platform teams lose the ability to enforce standards or answer basic operational questions.
Without shared governance, agents, skills, prompts, and MCP servers end up scattered across teams, making it hard to see what exists, control access, or avoid duplication. Each team maintains separate configurations and there is no single place to track ownership, security, or approved tools.
How Port helps
Port gives you one place to discover and govern agents, skills, prompts, and MCP across your organization:
| You need to… | Port capability |
|---|---|
| Discover agents on external platforms — code-first (OpenAI Agents SDK, Claude Code, GitHub Actions) or cloud-managed (Bedrock / AgentCore, Azure AI Foundry, Cursor Cloud Agents) | External agents |
| Maintain a registry of external agents with ownership and status in Context Lake | Agent registry |
| Govern which skills, prompts, and MCP servers teams may use | AI registry — skills, prompts, MCP registry |
| Build and run agents natively in Port | Port custom agents |
| Scope what agents can read from Context Lake | Context Lake data access |
| Connect IDEs and external tools to your Context Lake | Port MCP server |
Port brings agents, skills, prompts, and MCP under one governance layer so teams can move fast without losing control.
What's in this section
External agents
Connect and govern agents hosted outside Port — whether you build them with SDKs in code or run them on cloud-managed platforms. Discover agents in Context Lake, trigger sessions from workflows, and track deployments through an agent registry.
Port MCP server
Connect IDEs, AI tools, and agents to Port using natural language. The MCP server exposes your Context Lake and workflows as callable tools, so agents like Claude, Cursor, and GitHub Copilot can query the catalog and take governed actions from wherever developers already work.
Learn more about the Port MCP server →
Port custom agents
Customize and orchestrate complicated workflows inside Port. Build intelligent agents that can be used as part of automations and engineering workflows.
Use Port custom agents to:
- Automate incident response workflows
- Create intelligent PR review processes
- Build custom task management assistants
- Generate automated deployment reports
- Orchestrate multi-step engineering processes
AI registry
Connect IDEs and agents to Port, publish skills and prompts, and govern which MCP servers teams may use. This part of Agent management groups the Port MCP server, MCP registry, Skills, and Prompts documentation.
Use the AI registry to:
- Install and secure the Port MCP server for builders and developers.
- Track approved MCP servers and installation guidance in Context Lake.
- Define skills and prompt blueprints that Port AI and MCP clients can load at runtime.