AI agents
Why it matters
Engineering leaders need to understand delivery trends, identify bottlenecks, and act on metrics but extracting insights from dashboards and data requires time and expertise. AI agents connected to your engineering context lake can analyze metrics, explain trends, and recommend improvements in natural language turning raw data into actionable intelligence without manual investigation.
How Port helps
Port provides AI agents and custom AI skills that operate directly on your software catalog & context lake. Agents analyze delivery performance, DORA metrics, scorecard compliance, and reliability data surfacing insights and recommendations grounded in your organization's actual data. You can also build custom AI skills tailored to your specific engineering intelligence workflows. Agents reach beyond the catalog using MCP connectors, pulling real-time data from GitHub, Jira, PagerDuty, and other tools for complete context.
Example scenarios
Delivery performance analysis
An engineering manager asks the Delivery Performance agent: "Which teams have seen the biggest increase in PR cycle time this quarter and what's driving it?" The agent analyzes PR data across all teams, identifies three teams with rising cycle times, and pinpoints the root causes, e.g one team has a review bottleneck, another has flaky CI blocking merges, and the third has an unusually high rate of stale PRs.
Scorecard remediation
A team lead sees their service scored Bronze on the DORA scorecard but isn't sure where to focus. They ask the agent "What do we need to fix to reach Silver?" The agent compares the team's metrics against the scorecard thresholds and returns a prioritized list, e.g reduce change failure rate from 18% to under 15% by improving test coverage, and increase deployment frequency from weekly to twice-weekly by splitting large PRs.
Reliability insights
During a reliability review, an SRE asks "What are our top 3 services by incident frequency this quarter, and are they improving?" The agent pulls incident data, correlates it with deployment history, and responds with a breakdown showing one service has improved after a recent fix, while two others are trending worse with links to the specific deployments that preceded the incidents.