CircleCI Connector

Overview

The CircleCI connector enables AI Team members to monitor and analyze CircleCI pipelines, workflows, and jobs to support continuous integration and deployment operations. By connecting CircleCI to AI Team, AI teammates can track pipeline execution, analyze build failures, review test results, and correlate deployments with system behavior.

The connector provides access to build status, execution metrics, test results, and deployment history, making CI/CD data queryable through natural language interactions with AI teammates. This integration helps teams maintain reliable delivery pipelines, quickly identify and resolve build issues, and understand how deployments impact system reliability.

Add the CircleCI Connector

To add the CircleCI connector, you obtain an API token from CircleCI and configure authentication in Edge Delta.

Prerequisites

Before configuring the CircleCI connector, ensure you have:

• CircleCI account with API access
• Personal API token from CircleCI
• Network connectivity from Edge Delta to the CircleCI API

Configuration Steps

1. Navigate to AI Team > Connectors in the Edge Delta application
2. Find the CircleCI connector
3. Click the connector card to open the configuration panel
4. Configure the General tab options (see below)
5. Click Save to complete the configuration

The connector is now available for use by AI Team members who have been assigned this connector.

Event Listening

To receive real-time events from CircleCI (build completions, failures, etc.), configure a webhook in CircleCI to send events to Edge Delta. Without webhook configuration, AI teammates can query CircleCI data when prompted but won’t respond to events automatically.

See CircleCI Webhooks for setup instructions.

General Options

Display Name

Name to identify this CircleCI connector instance. Choose a descriptive name like “CircleCI Production” to differentiate it from other connectors.

CircleCI Personal API Token

API token for authenticating with CircleCI. To create a personal API token, log into CircleCI, navigate to User Settings > Personal API Tokens, and create a new token. The token must have read access to projects, workflows, and pipelines. See Managing API Tokens for detailed instructions.

Tools

config_helper

Validates CircleCI configuration files and identifies issues before they break pipelines.

create_prompt_template

Creates prompt templates for consistent AI behavior when interacting with CircleCI data.

find_flaky_tests

Identifies tests that fail inconsistently across pipeline runs to help improve test reliability.

get_build_failure_logs

Retrieves logs from failed builds to help debug and resolve failures.

get_job_test_results

Fetches test results from specific jobs to understand test failures and performance.

get_latest_pipeline_status

Retrieves current pipeline status to monitor pipeline health.

list_followed_projects

Lists CircleCI projects you follow and their project slugs.

recommend_prompt_template_tests

Suggests tests for prompt templates to improve reliability.

rerun_workflow

Reruns workflows from the beginning or from a specific failed job.

run_pipeline

Triggers new pipeline builds.

run_rollback_pipeline

Triggers rollback pipelines to revert faulty deployments.

How to Use the CircleCI Connector

The CircleCI connector integrates with AI Team, enabling AI teammates to monitor and analyze CI/CD pipelines based on natural language queries. Once configured, AI teammates can investigate build failures, track test results, and correlate deployments with system behavior.

Use Case: Build Failure Investigation

When builds fail, AI teammates retrieve failure logs, identify the failing job or test, and analyze error patterns. For dependency installation failures, teammates correlate CircleCI build logs with outbound network error logs and historical performance data to determine whether failures stem from transient external issues (such as package registry outages), environmental problems, or configuration regressions. Based on this analysis, teammates recommend either re-running the build (for transient issues) or implementing durable remediation (for persistent problems).

Use Case: Test Reliability Analysis

AI teammates aggregate test results across pipelines over time to identify tests that fail sporadically but consistently pass on reruns. When you notice intermittent test failures, AI teammates analyze historical test data to quantify flakiness and recommend prioritized actions: temporarily quarantining flaky tests to unblock pipelines, moving problematic tests to scheduled jobs for isolation, or flagging tests that require immediate attention based on their impact on CI/CD velocity.

Use Case: Pre-Merge CI Triage

When pull requests trigger CI pipelines, Code Analyzer pulls structured job metadata and test results from CircleCI (including JSON summaries and JUnit artifacts) and enriches them with GitHub context such as pull request diffs, recent commits, and code ownership. By querying historical CI telemetry, the teammate determines whether failures represent new regressions introduced by the current changes or recurring flakiness unrelated to the PR. Recommendations appear directly within the pull request, with supporting evidence including historical failure rates and affected code paths.

Use Case: Canary Analysis

AI teammates continuously compare canary deployments against known baselines by examining error rates, p95 latency, and service logs. When you deploy a canary, teammates analyze whether the changes meet your rollout criteria and recommend either promoting to the next deployment stage or rolling back. This analysis runs automatically as canary metrics accumulate, providing early warning if a deployment degrades service quality.

Use Case: Deployment Correlation and Rollback Orchestration

AI teammates correlate CircleCI deployment events with Edge Delta telemetry data to identify whether a deployment introduced production issues. When error rates spike after a deployment, teammates analyze the timeline and code changes to determine causation. For confirmed deployment-related incidents, teammates reconstruct failure sequences by correlating deployment timelines with live service metrics. They then generate rollback proposals that include the specific pipeline to trigger and the expected impact. Rollback actions require explicit stakeholder approval before execution, ensuring human oversight for production changes.

Troubleshooting

Connection errors: Verify your API token is valid and has not expired. Check that firewall rules allow outbound HTTPS traffic to CircleCI.

No pipelines found: Verify your API token has access to the expected projects. Check that projects have recent pipeline activity.

Webhook issues: If configured, verify webhook URLs and secrets match between CircleCI and the connector configuration.

Next Steps

• Learn about creating custom teammates that can use this connector
• Explore other connectors to integrate additional tools

For additional help, visit AI Team Support.