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Context Engineering with Port AI

Closed Beta

Port's AI offerings are currently in closed beta and will be gradually rolled out to users by the end of 2025.

Context engineering is the practice of constructing an optimal information environment that provides the right amount of directions, tools, and knowledge for AI systems to successfully solve problems. Unlike prompt engineering, which focuses on crafting individual requests, context engineering ensures AI has access to relevant, structured data and capabilities needed for consistent, accurate results.

What is Context Engineering?​

Context engineering addresses a fundamental challenge: AI systems need curated, relevant context to perform effectively. If given too broad a range of information, AI experiences context distraction and task failure. If given insufficient context, it cannot understand how to complete tasks successfully.

Port AI solves this through a domain-integrated context approach, providing AI with structured access to your engineering ecosystem's knowledge graph while maintaining security boundaries.

How Port AI Supports Context Engineering​

Port AI implements context engineering through three core capabilities:

1. Effective Prompt Design​

Port AI accepts custom prompts that you design to guide AI through your context systematically. Your job is to build effective prompts that leverage your organization's specific data and terminology.

Guidelines for effective prompts:

  1. Define Clear Goals: Explicitly state what the AI should accomplish
  2. Provide Domain Context: Include organization-specific terminology and processes
  3. Set Response Style: Define communication tone and level of detail
Your goal is to help developers understand service ownership and responsibilities in our microservices architecture.

When answering questions about services:
- Always include the owning team and primary contacts
- Reference recent activity and deployment status
- Link to relevant documentation and runbooks
- Consider service dependencies and relationships

Use our organization's terminology:
- "Platform Team" owns infrastructure services
- "Product Teams" own feature services  
- "SRE Team" handles shared monitoring tools

2. Secure Engineering Data Access​

Port AI provides structured access to your engineering ecosystem through MCP (Model Context Protocol) tools. This enables AI to explore and understand your engineering context while respecting security boundaries.

Available Context Includes:

  • Blueprints & Entities: Complete software catalog with services, applications, and infrastructure
  • Relationships: Dependencies, ownership, and architectural connections
  • Historical Data: Deployment history, incident patterns, and change tracking
  • Task Context: Current workloads, tickets, and priorities across systems
  • Organizational Standards: Naming conventions, approval processes, and compliance requirements
  • Scorecards & Metrics: Quality gates, SLA compliance, and performance indicators

Flexible Data Model as Context: Port's flexible data model serves as engineering context for AI agents. Everything you've already built becomes available context:

  • Blueprint descriptions explain what each entity type represents
  • Property configurations provide meaning for each field, limits, and usage
  • Action configurations include regex patterns, limits, and meaningful descriptions
  • Relationships define how entities connect and depend on each other

To learn more about data controls and security, see AI Security and Data Controls.

MCP Server Benefits

Port's native MCP server integration means AI agents can intelligently navigate your entire engineering ecosystem without requiring custom training or data preparation.

3. Safe Action Execution​

Port AI enables controlled automation through configurable action permissions and autonomy levels.

Action Control Options:

  • Manual Approval: AI creates draft actions that require human review before execution
  • Automatic Execution: AI executes pre-approved actions within defined boundaries

AI agents can run actions in the same way developers can. They are restricted by the same mechanisms:

  • User permissions and RBAC policies
  • Action rules and validation requirements
  • Approval workflows defined for each action
  • Execution boundaries set in action configurations

Context Engineering Examples​

Here are real-world scenarios where Port AI's context engineering capabilities deliver value:

Service Ownership Discovery​

Query: "Who owns the checkout service?"

Context Provided:

  • Software catalog with service definitions
  • Team structures and membership
  • Recent commit and deployment history
  • Associated documentation and runbooks
  • Current incident and ticket assignments

Critical Incident Assessment​

Query: "What are our current critical incidents?"

Context Provided:

  • Incident priority classifications
  • Service dependency mapping
  • Customer impact assessment
  • Historical incident patterns
  • On-call schedules and escalation paths

Developer Task Prioritization​

Query: "What should I work on today?"

Context Provided:

  • Personal task assignments across systems
  • Incident tickets requiring attention
  • Code review requests and PR status
  • Security issues approaching SLA deadlines
  • Team priorities and strategic projects
Integration with AI Features

Context engineering principles apply across all Port AI capabilities, from the Port AI assistant interface to custom AI Agents.