Operating Teammately end to end
Use this workflow to coordinate the full correctness system while keeping project foundations, benchmark work, expert authority, evaluation evidence, and candidate improvement separate.
Decision checkpoint
| Phase | Owning scope | Exit condition |
|---|---|---|
| Establish project understanding | Project | Project Agent Brief and Indexed Reference are usable |
| Define content and reusable assets | Project | Project Input Schema, Cases, Harnesses, and Coverage Facets are explicit |
| Establish benchmark evidence | Benchmark | Dataset snapshot and coverage state are reviewable |
| Resolve specialist correctness | Benchmark Contribution and project governance | Attributable artifacts have explicit lifecycle state |
| Evaluate candidates | Benchmark version | Exact Runs and case/rubric evidence are available |
| Improve behavior | Benchmark version | Goal Contract, candidates, receipts, and frontier are durable |
Prerequisites
- A workspace and project.
- An accountable operator, domain expert, and AI engineer or candidate owner.
- Source knowledge, examples, and a candidate system appropriate to the intended benchmark.
Before and after
| Before | Operation | After |
|---|---|---|
| Agents lack a shared project model | Configure Agent Setup | Project understanding is reusable and inspectable |
| Examples have inconsistent shape | Save Project Input Schema and prepare Cases | Inputs and materials share a canonical contract |
| Coverage and correctness are implicit | Define Coverage Facets and request Contributions | Benchmark intent and specialist standards are explicit |
| Candidate claims depend on anecdotes | Run Benchmark Evaluations | Evidence is bound to versions, cases, and rubrics |
| Engineering iterations lack chronology | Use Improve | Goals, candidates, receipts, and current frontier stay connected |
Steps
- Configure Project Context and Reference Materials in Agent Setup. Create or select Comparison Directions and Review Screens under Assets when a Contribution needs them.
- Save Project Input Schema and establish reusable Coverage Facets.
- Create or import Cases and save candidate Harness versions under Assets.
- Create or select a benchmark, configure Coverage Management, select Cases in Benchmark Datasets, inspect Representation, and preserve a snapshot.
- Request focused Expert Contributions for unresolved standards, cases, or coverage. Reconcile contributed artifacts in their owning project or benchmark surfaces.
- Confirm governed policies and rubrics and the benchmark version that will use them.
- Run exact saved Harness versions through Benchmark Evaluations. Inspect Dashboard, List, Compare, Arena, and Run detail according to the question.
- Start an Improvement Session only from evidence that identifies measurable candidate work.
- Return newly discovered correctness or coverage gaps to Expert Contributions, Correctness Governance, Coverage Management, or Cases.
Object and state changes
This workflow touches project context, reference indexes, input schema, facets, assets, benchmark datasets and snapshots, coverage state, Contributions and contributed artifacts, policies, rubrics, Harness versions, Runs, evaluation results, and Improvement Sessions. Each object remains in its owning scope and retains historical evidence.
Success criteria
- Project and benchmark scope is explicit at every operation.
- Agent preparation, expert judgment, and governed artifacts remain distinguishable.
- Dataset and candidate versions make evaluation reproducible.
- Improvement begins with a measurable goal and pinned evidence.
- New learning returns to one responsible upstream artifact.
Common failure modes
- Putting benchmark-specific instructions into permanent Project Context.
- Treating connected sources as approved standards.
- Selecting generated Cases without case review or schema conformance.
- Comparing Runs after multiple evidence boundaries changed.
- Treating external-worker activity as observable before an artifact returns.
Worked example
Example: full grounding loop
A team indexes source repositories, defines source-authority coverage, imports canonical cases, and requests a Contribution to resolve conflicting guidance. The governed rubric enters a benchmark version, two saved Harness versions are compared, and an Improvement Session tests retrieval changes. A missing-source pattern discovered during improvement returns to Coverage Management.
Related reference pages
Related troubleshooting pages
Source confidence
Doctrine-backed: the sequence follows the current public capability model and active product topology. Linked pages provide code-backed operation details.