01 / DISCOVER
Actively hunt for leads.
Monitor code, PRs, issues, build failures, deployments, and performance signals for patterns worth following.
OutputA structured lead with signals and affected components.Your repos are leaving clues.
Find the engineering problems nobody’s looking for.
LeadYard deploys autonomous engineering agents to discover, investigate, and resolve the technical problems hiding across your development stack. Connect your repositories, CI pipelines, issue trackers, and observability tools. LeadYard follows the signals, connects the evidence, and prepares actionable fixes — with your engineering team in control.
Read-only discovery by default
Human-approved code changes
Connected signals / one investigationYARD — 001
Illustrative product concept · No live data
Repositorycheckout-service
CI signalRecurring warnings
ObservabilityLatency change
Engineering lead · illustrativeInvestigation
A query pattern worth a closer look.Three signals connect to one code path. Trace is gathering evidence and checking alternatives.
Your engineering stack, connected
GitHubGitLabGitHub ActionsJenkinsSentryDatadogJiraLinear
Live from the Yard
A lead isn’t another ticket.
It’s an investigation already in motion: connected signals, a probable cause, a proposed change, and a human review point. Explore the sample panel to see the shape of a LeadYard lead.
Illustrative investigation · Sample dataLead-2048 · Performance regression
HighCheckout API latency increased after deployment
- Sample telemetry shows p95 latency moving from 210ms to 640ms after deployment
v2.18.4. - A query in
checkout/repository.ts:184appears repeatedly in the order-processing path.
Trace · probable causeSuspected repeated query pattern linked to a recent change.
Proof · next checkRun the proposed benchmark against high-item-count checkouts.
Recommended action · Review, validate, then decideHUMAN APPROVAL REQUIRED
Engineering blind spots
Your tools report problems.
They don’t investigate them together.
Signals are already in your stack. The connections between them still live in someone’s head.
GitHub knows what changed. CI knows what failed. Sentry knows what crashed. Datadog knows what slowed down. Jira knows what was reported.
Source / GitHubCode changed
Source / CIBuild failed
Source / SentryError returned
Source / DatadogLatency climbed
Source / JiraTicket opened
A flaky test gets retried. A slow query slips through another release. A workaround spreads across services. An incident forms before anyone sees the pattern.
LeadYard follows the signal until the problem becomes actionable.The lead lifecycle
From scattered signals
to review-ready fixes.
Specialized agents share context from discovery through verification, while your team keeps control of consequential decisions.
02 / INVESTIGATE
Build an evidence trail.
Trace dependencies, inspect history, compare failures, test hypotheses, and surface what remains unknown.
OutputLikely cause, impact, evidence, and open questions.03 / PROPOSE
Prepare a scoped fix.
Draft code changes, regression tests, dependency updates, or a refactoring plan tied to the evidence.
OutputProposed patch and reproducible validation steps.04 / COLLABORATE
Resolve it together.
Bring developers and agents into one Lead Room to question findings, review changes, and record decisions.
OutputOne shared investigation with clear ownership.05 / VERIFY & LEARN
Check that it holds.
Compare authorized test and monitoring signals with success criteria; reopen with context if the issue returns.
OutputResolution history and verification status.Your autonomous engineering crew
Specialized agents.
Shared context. One mission.
Purpose-built roles coordinate around each lead so investigation can move forward without losing its evidence or its human owners.
SScout
Signal discoveryFinds what deserves attention.
Examines CI failures, commits, issue trends, dependencies, and performance changes to uncover engineering leads.
TTrace
Root causeFollows the evidence.
Maps execution paths, checks Git history and traces, identifies affected services, and tests competing explanations.
AAtlas
ArchitectureSees across repositories.
Investigates duplicated functionality, architectural drift, circular dependencies, and risky service coupling.
PPatch
RemediationTurns findings into a proposal.
Prepares code changes, refactoring plans, dependency modifications, and regression tests linked to the evidence.
PProof
VerificationChecks whether the fix holds.
Runs authorized tests and benchmarks, reviews CI outcomes, and checks agreed success criteria.
OOrchestrator
Coordination engineKeeps the investigation moving.
Breaks leads into tasks, coordinates agents, tracks dependencies, and escalates decisions that need a person.
Built for human + AI collaboration
One lead.
One room.
Investigations get scattered across chat, code review, terminals, dashboards, and undocumented assumptions. Lead Rooms bring the evidence, owners, agents, and proposed changes into one shared workspace.
Investigate togetherDiscuss the evidence beside relevant files, traces, and CI runs.
Delegate with contextAsk an agent to reproduce, inspect, compare, or investigate more.
Keep the historyRetain hypotheses, decisions, fixes, and verification for next time.
Illustrative board
The Yard
Sample workspaceThe Yard
Incoming 2
CI reliabilityFlaky integration suiteScout found pattern
DependencyRuntime upgrade blockedNeeds triage
Investigating 1
PerformanceCheckout API latencyTrace + Proof
Review 1
Technical debtDuplicated validationPatch proposed
Illustrative product concept · No live workspace
What LeadYard hunts
Work that rarely gets
enough attention.
Follow recurring patterns across repositories and tools to make hidden engineering work easier to see and act on.
Stop fixing the same failure every sprint.
Connect error signatures, CI runs, change history, and prior fixes to investigate why a bug returns.
Example: A serialization error resurfaces across releases after earlier patches.Find debt that slows the team down.
Surface coupled, frequently changed modules with recurring defects, duplicated code, and workarounds.
Example: Diverging validation logic causes inconsistent behavior across services.Trace regressions to the change.
Connect performance telemetry with deployments, code changes, queries, and benchmarks.
Example: Worker memory rises after an update under a specific load pattern.Spot structural issues before rewrites.
Analyze module boundaries, service dependencies, circular references, and architectural drift.
Example: A shared utility becomes a tightly coupled dependency across services.Turn red pipelines into investigations.
Group flaky tests, recurring build failures, dependency conflicts, and environment issues.
Example: Parallel test runs expose shared-state mutation.Make maintenance visible sooner.
Investigate outdated packages, blocked upgrades, incompatible interfaces, and dependency chains.
Example: One deprecated package blocks a runtime upgrade across services.Not just AI-assisted. Agent-operated.
Give your agents a goal, not a sequence of prompts.
LeadYard plans an investigation, selects tools, gathers evidence, delegates parallel work, and surfaces what it still needs to know.
A goal for the Orchestrator
Find the causes of recurring CI failures in the payments repository.
A goal for the Orchestrator
Investigate why our API latency has increased over the last two releases.
A goal for the Orchestrator
Prepare a fix for LEAD-2048 and request a review.
Autonomous investigation. Controlled execution. Human-owned decisions.
One platform. Your engineering context.
Connect the tools
your team already uses.
Link signals across your connected systems so a commit, build, issue, or trace can become part of a larger investigation.
GitHub
GitLab
GitHub Actions
Jenkins
CircleCI
Sentry
Datadog
Prometheus
Jira
Linear
Integration availability and permissions depend on the configured workspace and plan. Documentation sources may include Markdown, repository docs, and internal knowledge sources.
Autonomy with boundaries
Your team stays in control.
Give agents room to investigate. Define what they can execute, what needs approval, and which evidence supports each recommendation.
Read-only discovery
Begin with inspection of repositories, logs, and connected engineering systems.
Explicit permissions
Configure which agents can run tests, create branches, or prepare pull requests.
Approval checkpoints
Require human approval before consequential actions, merges, or production changes.
Evidence-first reporting
Review source references, assumptions, confidence, and validation status.
Complete activity history
Track agent actions, delegated tasks, tool use, and human decisions.
Scoped access
Limit agents to repositories, services, and tools required for assigned work.
Built for engineering teams
An extra layer
of engineering intelligence.
Give each role a clearer view of emerging technical risk and the work that can improve the system.
Engineering managers
See recurring problems and high-impact maintenance beyond the manually created backlog.
Tech leads
Prioritize architecture work, delegate investigations, and review evidence-backed proposals.
Platform & SRE
Investigate systemic reliability, performance, and CI issues across connected services.
Developers
Spend less time gathering context and more time evaluating and validating fixes.
Pricing
Scoped to your
engineering context.
Early access is shaped around the repositories, integrations, and investigation goals your team wants to start with.
Early access · Start with a conversation
Let’s map your first lead.
We’ll learn how your engineering team works, where the signals live, and what an agent should help investigate. Scope and commercial details can follow that conversation.
A few good questions
Before you connect.
Control, evidence, and human review are part of the product from the start.
Does LeadYard change code on its own?
Discovery is read-only by default. Teams can configure permitted actions, and consequential changes can require explicit human approval. Merges and production changes remain human decisions.
What does “a lead” mean for engineering?
A lead is a structured problem worth investigating: it connects signals to affected systems, evidence, a hypothesis, and a possible next action. It is more than an alert or an untriaged ticket.
How do agents explain their findings?
Investigations should link back to relevant source evidence, show assumptions and confidence, and include unresolved questions or alternative explanations when the evidence is incomplete.
Are the examples on this page live data?
No. The Lead-2048 panel and Yard board are illustrative product concepts with sample data. This page does not connect to repositories, CI, or observability services.
There’s more hiding in your codebase.
Your next high-impact fix is already leaving clues.
Let your agents find it, investigate it, and build a proposed fix. Bring your team into the room.