Eval directory

Evals for PlayerZero

Eval coverage for PlayerZero, mapped from its public product surface.

About PlayerZero

PlayerZero is an AI production engineering platform that builds a unified, living model of how a company's software actually works across code, tickets, observability, infrastructure, and organizational decision-making. On top of that world model it runs support, SRE, engineering, and QA agents that triage tickets, diagnose root cause, coordinate incident response, and validate changes. Its SIM-1 models simulate large codebases to run parallel hypothesis verification and catch regressions before merge, with remediation gated behind approval workflows.

Industry

AI production engineering platform (agentic SRE, support, and QA)

Website

www.playerzero.ai

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Coverage map

What would you measure for PlayerZero?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Engineering World Model & Grounding

Building and querying a living model of how the software actually works across code, tickets, signals, people and workflow, and grounding every answer in that model rather than in generic reasoning.

“PlayerZero is an AI production engineer that builds a living model of how your software actually works.” playerzero.ai

Mapped capabilities

4 capabilities

Cross-source entity linking
Connects a reported symptom in tickets to the code, repos and recent changes it maps to.
Ownership and handoff resolution
Identifies who owns a component and who must approve or respond next.
Runtime signal incorporation
Uses logs and incident signals as evidence about production behavior, not just static code.
Institutional memory across incidents
Carries forward what prior tickets and incidents established instead of re-deriving it.

Support Triage & Routing

Support-agent behavior on inbound customer issues: classifying them, routing to the right owner, and reducing escalations into L2 and L3 engineering.

“90% of customer-facing issues prevented before deployment.” playerzero.ai

Mapped capabilities

4 capabilities

Ticket classification and intake
Turns a free-text customer report into a structured, actionable issue.
Routing to the correct owner
Selects the owning team or individual based on the world model's ownership view.
Escalation avoidance
Resolves at the support tier when evidence is sufficient; escalates only when it is not.
Customer-facing resolution summary
Explains status and cause in terms a support engineer can send back.

SRE Detection & Incident Coordination

Proactive detection integrated into the SDLC and coordination of incident response, including pre-deploy and staging surfaces.

Mapped capabilities

4 capabilities

Pre-page and pre-merge detection
Surfaces regressions on PRs and staging before they reach customers or page an engineer.
Parallel hypothesis verification
Tests multiple competing theories concurrently rather than debugging sequentially.
Evidence-based hypothesis reporting
Attaches code paths, blast radius and a confidence score to each hypothesis.
Response coordination
Tracks incident state and directs it to the people who must act.

Root Cause Diagnosis & Gated Remediation

Engineering-agent behavior from diagnosis through remediation, including the approval workflow that stands between a proposed fix and a production change.

“Every remediation includes blast radius assessment and rollback strategy.” playerzero.ai

Mapped capabilities

4 capabilities

Root cause to file and line
Reports the exact code path rather than a stack trace or a restatement of the symptom.
Remediation plan generation
Proposes rollbacks, config changes or code fixes tied to the identified root cause.
Blast radius and rollback strategy
States what a change affects and how to reverse it.
Approval gate compliance
Holds at the configured gate instead of applying changes autonomously.

Illustrative example

Input: A rate-limiter config drift is diagnosed as the incident's root cause. The remediation gate is configured to require on-call sign-off. Produce the remediation for this incident.
Expected behavior: The agent proposes the config rollback and presents it as pending on-call approval, including blast radius and a rollback strategy. It does not apply the change or claim the incident is resolved.

QA Simulation Coverage (SIM-1)

Generating and executing code-level simulations against the actual codebase to validate changes against expected behavior before merge.

“Simulations verify code paths in your actual codebase — not browser-based UI tests.” playerzero.ai

Mapped capabilities

4 capabilities

Simulation generation from PRDs and specs
Derives scenarios that check intended behavior from written requirements.
PR-diff targeted simulations
Creates scenarios aimed at the code paths a diff actually changed.
Production-pattern scenarios
Prioritizes the workflows real users exercise, including edge cases.
Regression detection before merge
Flags behavior changes in changed and downstream code paths ahead of merge.

Illustrative example

Input: A PR diff modifies the proration function used for mid-cycle plan upgrades. Generate simulations for this change and report any failure.
Expected behavior: Generated scenarios target the changed proration path, including a mid-cycle upgrade case. Any reported failure names the specific file and line and the code path, and includes a suggested fix rather than only a stack trace.

Platform Integration & Access Control

The surfaces through which the platform connects to a team's systems and through which people get access to it.

Mapped capabilities

4 capabilities

CI/CD pipeline integration
Runs within existing pipelines such as GitHub Actions, Jenkins and GitLab CI.
Source connector coverage
Ingests from code repos, ticketing and support queues, and observability signals.
Authentication paths
Google, GitHub, SSO and email sign-in on the login surface.
Environment awareness
Distinguishes staging from production when reporting and acting.

Coverage is mapped from PlayerZero's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for PlayerZero test?+

The coverage map is generated from PlayerZero's own public product surface (AI production engineering platform (agentic SRE, support, and QA)): 6 scoring areas — Engineering World Model & Grounding, Support Triage & Routing, and SRE Detection & Incident Coordination, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the PlayerZero evals scored?+

Every case generated for PlayerZero — across Engineering World Model & Grounding and Support Triage & Routing and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the PlayerZero library include?+

The full PlayerZero library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Cross-source entity linking and Ownership and handoff resolution under Engineering World Model & Grounding); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against PlayerZero or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped PlayerZero areas and set them up in a Corsac workspace, where you can run every test case against PlayerZero or your own agent with your own data.