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Uber Validator + AutoCover

Self-ReportedCurated

Coordinator + reused sub-agents across two developer-platform tools.

Uber (Developer Platform team)· Operating since Nov 20, 2024· active
Curated from ZenML LLMOps Database — Uber Validator + AutoCover — not claimed by or endorsed by the organization. Metrics cited only as the source states. Absent metrics render as [unknown].

Recent activity

Version cuts and proof, newest first — the living track record.

  1. Artifact · Uber presents Validator + AutoCover at GitHub Universe 20241y ago

Spec sheet

The benchmark fields — designed for comparison across teams.

Topology
Orchestrator–Worker
Agent count
6
Platform
LangGraph
Runs on
LangGraph ×6
Industries
software-delivery
Task kinds
code-reviewtest-generation
Trust tier
Self-Reported
Proof entries
1

Topology & roster

Orchestrator–Worker

Orchestrator-worker with reused sub-agents. Validator: coordinator + LLM sub-agent + deterministic linter sub-agent. AutoCover: Scaffolder → Generator → Executor, reusing the Validator for quality checks (a bounded, reusable sub-agent pattern across both tools).

System wiring

Typical Orchestrator–Worker layout — schematic, not verified wiring
Node details

Typical Orchestrator–Worker layout — schematic, not verified wiring

HumanHuman operatorHuman gate
Tool
Human operator
Autonomy
Human-gated
Sends
  • directs → Orchestrator
OrchestratorOrchestrator
Tool
Orchestrator
Autonomy
Runs autonomously
Sends
  • dispatches → Worker agent
  • requests gate → QA reviewer
Receives
  • directs ← Human operator
BuilderWorker agent
Tool
Worker agent
Autonomy
Runs autonomously
Sends
  • commits to → Shared workspace
Receives
  • dispatches ← Orchestrator
QAQA reviewer
Tool
QA reviewer
Autonomy
Runs autonomously
Sends
  • gate verdict → Shared workspace
Receives
  • requests gate ← Orchestrator
ResourceShared workspace
Tool
Shared workspace
Autonomy
Runs autonomously
Receives
  • commits to ← Worker agent
  • gate verdict ← QA reviewer

How a typical Orchestrator–Worker team handles a task

Typical Orchestrator–Worker layout — schematic, not verified wiring

  1. Task arrives

    Human operator directs Orchestrator.

  2. The orchestrator routes the work

    Orchestrator dispatches build work to Worker agent.

  3. Worker agent builds the work

    Worker agent builds the work.

  4. Independent review gates the work

    QA reviewer reviews the work. This reviewer is autonomous and separate from the agent that built the work, so the check is independent of its author.

  5. The artifact lands

    The artifact lands in Shared workspace: Worker agent contributes via "commits to" and QA reviewer contributes via "gate verdict".

  6. Human holds the last word

    Human operator holds final approval.

Replicate a typical Orchestrator–Worker setup

Typical Orchestrator–Worker layout — schematic, not verified wiring

Ingredients

  • HumanHuman operator
  • OrchestratorOrchestrator
  • BuilderWorker agent
  • QAQA reviewer
  • ResourceShared workspace

Setup order

  1. 1.Provision the substrate: Shared workspace.
  2. 2.Stand up the orchestrator: Orchestrator.
  3. 3.Wire Worker agent: it receives "dispatches" from Orchestrator. Wire QA reviewer: it receives "requests gate" from Orchestrator.
  4. 4.Give QA reviewer an independent workspace/verdict channel: "gate verdict" to Shared workspace.
  5. 5.Declare the human gate: Human operator holds final approval.

Performance metrics

Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.

Developer-platform coverage increase
10%
self-reported

~10% increase in developer-platform test coverage; vendor/conference claim of '2-3x more coverage in about half the time' vs benchmarked industry tools. Same sourcing caveat as the developer-hours metric on this team — conference-presented, aggregator-transcribed, not company-published. Source: ZenML LLMOps database. [self_reported]

as of Nov 20, 2024
Developer hours saved (test generation)
21,000
self-reported

~21,000 developer hours saved via automated unit-test generation (AutoCover). All figures here trace only to ZenML's third-party LLMOps database summary of conference talks (GitHub Universe 2024 talk by Matas Rastenis & Sourabh Shirhatti, confirmed via YouTube video metadata; LangChain Interrupt 2025 talk) — no first-party Uber engineering blog post was located as of 2026-07-05. A 'shared build-system agent' detail in some secondary summaries could not be corroborated anywhere and is excluded here. Source: ZenML LLMOps database, citing Uber conference talks 2024-11 / 2025-05. [self_reported]

as of Nov 20, 2024

Token economics

Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.

No cost metrics on record. Cost tracking is hard across runtimes; honest absence beats invented figures.

Blueprint

Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.

Why it works

Reusing the same Validator sub-agent inside both the IDE-review tool and the test-generation tool (AutoCover) avoids maintaining two separate quality-check implementations — a bounded, composable agent reused across products rather than rebuilt per product.

How it was built

Composable LangGraph agents under an internal opinionated wrapper. AutoCover runs up to 100 parallel generation iterations and up to 100 parallel test executions.

Oversight model

IDE-embedded suggestions surfaced to engineers; humans accept or reject precomputed fixes and generated tests. Org context: ~5,000 engineers, hundreds of millions of lines of code.

Proof (1)

The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.

  1. ArtifactNov 20, 2024self-reported

    Uber presents Validator + AutoCover at GitHub Universe 2024

    ~21,000 developer hours saved, ~10% coverage increase, per a third-party LLMOps aggregator (ZenML) summary of conference talks; no first-party Uber engineering blog post was located as of 2026-07-05.

    https://www.zenml.io/llmops-database/ai-powered-developer-tools-for-code-quality-and-test-generation

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