Agent Builders
Build AI systems that predict, prevent, and optimize across your entire stack.
Real-time stack health pulse
Synthesize signals across all tools into a single 'how healthy is the system right now' assessment
Shadow dependency discovery
Detect runtime dependencies that aren't declared in package manifests (HTTP calls between services, shared databases)
Knowledge freshness scoring
Identify documentation, runbooks, and design docs that are stale relative to the code they describe
Merge conflict probability
Given active branches and their file overlap, predict which PRs are likely to conflict
API breaking change impact prediction
When an API contract changes, predict which consumers will break based on actual usage patterns
Performance regression attribution
When latency increases, identify the causal chain: deploy → code change → specific function → database query
Flaky test root cause
Correlate test flakiness with infrastructure state, concurrent builds, and resource contention
Tech debt prioritization
Rank technical debt items by impact on velocity, incident risk, and cost, weighted against effort
Monolith extraction candidates
Identify service boundaries within a monolith based on code coupling, team ownership, and deployment frequency
Deployment risk scoring
Given what's in a PR, what infrastructure it touches, and historical incident data, score the risk of shipping it
Data flow mapping
Trace how user data moves through services, databases, and third-party tools for compliance awareness
Sentry error prediction from deploy diff
Given the files changed in a deployment, predict which error classes are likely to spike based on historical patterns
Test flakiness prediction
Identify tests likely to become flaky based on their coupling to frequently-changed code
Performance regression prediction
Based on code complexity metrics and historical correlation with latency, flag PRs likely to cause regressions
Error clustering by root cause
Group Sentry errors not by stack trace similarity but by shared causal origin (same PR, same dependency, same config change)
Alert fatigue analysis
Identify which alerts are symptoms of the same underlying issue and should be deduplicated
Test coverage optimization
Identify which untested code paths have the highest incident correlation and prioritize coverage there
Undocumented tribal knowledge detection
Find critical systems that only one person has ever committed to or reviewed
Technical debt inventory
Classify and quantify debt: outdated dependencies, TODO comments, skipped tests, workaround patterns
Outage probability forecasting
Based on current error rate trajectories, dependency health, and deploy frequency, estimate probability of an outage in the next N hours
Build time regression forecasting
Predict when CI/CD pipeline duration will cross acceptable thresholds based on codebase growth trends
Automated incident root cause tracing
When a Sentry error spikes, trace backward through the graph: which deploy, which PR, which commit, which issue prompted the change
Cross-service incident correlation
Identify when errors in Service A were caused by changes in Service B through shared dependencies
PR reviewer recommendation
Suggest reviewers based on file expertise, current workload, and knowledge distribution goals
Feature flag cleanup prioritization
Rank stale feature flags by risk (how much code they gate, how long since last toggle)