# Agent instructions
Generated: 2026-09-14T09:34:16.336Z
Source build: local
Canonical docs: https://teammately.ai/docs
---
id: agent-instructions
title: Agent instructions
summary: Rules coding agents should follow when using Teammately public docs as source context.
kind: reference
product_area: reference
status: stable
updated: 2026-08-23
canonical: /docs/agent-instructions
---
# Agent instructions
Use this page as the behavioral policy for agents operating with Teammately docs. For route selection and context packaging, read [Agent context index](/docs/agent-context).
## Task intent
Answer Teammately product and docs questions safely without inventing unsupported product claims. The agent should preserve source confidence, cite stable pages, and ask for confirmation when a claim depends on draft, inferred, or missing evidence.
## Required context
- Read [/docs/llms.txt](/docs/llms.txt) first for the recommended context loading strategy.
- Use [Agent context index](/docs/agent-context) to choose the smallest relevant context pack.
- Use page-local markdown or llms routes for exact wording.
- Use the manifest or page API when source confidence, source refs, related IDs, or metadata matter.
- In an MCP client, search first and fetch the cited page or block before relying on exact wording.
## Allowed assumptions
- Code-backed pages can support object, state, and workflow claims when source_refs are present.
- Doctrine-backed pages can support positioning, lifecycle, category language, and public narrative.
- Docs-backed pages can support claims about routes, llms files, manifests, search, context APIs, and validation behavior.
- Stable public pages supersede older draft or removed pages.
## Forbidden assumptions
- Do not present draft schema pages as public schemas.
- Do not infer public APIs, SDKs, API keys, auth behavior, rate limits, billing, compliance, tenant isolation, deployment modes, support guarantees, customer names, model-provider integrations, or production monitoring behavior.
- Do not treat AI-assisted suggestions as approved policies, rubrics, or review context unless a stable page says a human approved them.
- Do not collapse Teammately into eval dashboards, observability, annotation, prompt testing, generic LLMOps, or a replacement for adjacent tools.
- Do not use internal source file names as public UI promises unless the public docs already state the behavior.
## How to answer safely
1. Identify whether the user is asking for positioning, operating steps, object behavior, recovery, or implementation context.
2. Load the smallest matching context pack or query-scoped context.
3. Check source confidence before making claims.
4. Prefer stable page citations over broad corpus summaries.
5. If search returns an AI Overview, verify its block references with `fetch`; treat the overview as routing assistance rather than independent authority.
6. State uncertainty when a claim is inferred, draft-only, or outside the docs.
7. Ask for human confirmation before advising changes that depend on permissions, compliance, deployment, billing, customer-facing APIs, or unsupported integrations.
## When to ask for human confirmation
- The user asks whether an inferred or draft schema is a public contract.
- The answer would require exact UI labels not present in stable docs or source refs.
- The user asks about admin, auth, security, compliance, deployment, billing, or support commitments.
- Related evidence conflicts across docs, product code, and doctrine.
- The requested action could change review context, approval state, or benchmark interpretation without accountable human review.
## Related human docs
{% related-card-grid title="Related human docs" %}
- [Product boundaries](/docs/introduction/product-boundaries)
- [Human approval boundaries](/docs/governance/human-approval-boundaries)
- [What AI Features Can and Cannot Do](/docs/governance/what-ai-features-can-and-cannot-do)
{% /related-card-grid %}
## Related reference docs
{% related-card-grid title="Related reference docs" %}
- [Agent context index](/docs/agent-context)
- [Reference index](/docs/reference)
- [Object model](/docs/object-model)
{% /related-card-grid %}
## Safe operating boundaries
Agents may help readers find pages, summarize stable docs, compare source-confidence labels, and draft operational next steps. Agents should not approve policies, approve rubrics, assign reviewers, declare a candidate ready for rollout, or convert draft/inferred material into public product commitments.
Search mode is diagnostic metadata, not a confidence label. Lexical fallback remains usable when semantic embeddings are unavailable; source confidence still comes from the fetched page metadata and prose.
## Source confidence
Docs-backed: this page is a behavior policy for using the docs system safely. The docs loaders, context routes, and MCP tools establish the available retrieval surfaces, cited block references, and read-only tool boundaries.
---
id: intro.what-is-teammately
title: What is Teammately?
summary: Understand Teammately as correctness infrastructure for building trustworthy specialist AI with expert judgment and AI agents.
kind: concept
product_area: introduction
status: stable
updated: 2026-08-22
canonical: /docs/introduction/what-is-teammately
---
# What is Teammately?
Teammately is correctness infrastructure for teams building specialist AI. It turns in-house experts' judgment into an operating system for designing benchmark coverage, making correctness explicit, constructing challenging cases, evaluating candidate behavior, and deciding what to improve next. AI agents prepare and connect the work so scarce expert attention is spent on consequential judgment rather than manual organization.
> Category boundary
>
> Teammately centers the definition and development of trustworthy AI behavior. Logs, traces, model endpoints, coding environments, and external data can enter the workflow, but the product's durable value is the connected correctness system built from expert judgment, cases, standards, evaluation evidence, and improvement history.
## Definition
The system has five connected capabilities. [Coverage Engineering](/docs/coverage-engineering) defines the behavior space a benchmark must represent. [Correctness Elicitation](/docs/concepts/correctness-elicitation) turns tacit preferences, exceptions, and disagreements into policies, applicability conditions, and binary rubrics. [Weave](/docs/concepts/weave) constructs cases, response variants, case materials, and—where supported—worlds from that structure. [Trialground](/docs/concepts/trialground) evaluates Harnesses and weights against benchmark Cases and preserves responses and Rubric results. [Coevolve](/docs/concepts/coevolve) explores candidate improvements and keeps every retained direction tied to current benchmark evidence.
These capability names explain how the system works. Procedures use the labels visible in the product, such as Correctness Governance, Agent Setup, Benchmark Datasets, Coverage Management, Expert Contributions, Benchmark Evaluations, and Improve.
## Decision checkpoint
| If the team needs... | Capability | Product surfaces to open |
| --- | --- | --- |
| A deliberate map of important behavior | Coverage Engineering | Coverage Facets and Coverage Management |
| Reusable standards grounded in specialist judgment | Correctness Elicitation | Correctness Governance and Expert Contributions |
| Challenging cases and supporting materials | Weave | Assets, Benchmark Datasets, Case Construction Patterns, and Case Foundry |
| Repeatable evidence about candidate behavior | Trialground | Harnesses and Benchmark Evaluations |
| Evidence-backed candidate improvement | Coevolve | Improve and Improvement Sessions |
## Why teams use it
A benchmark score cannot define correctness on its own. Specialist systems depend on domain rules, exceptions, source authority, interaction patterns, and consequences that generic criteria do not capture. Teammately gives experts and AI engineers a shared artifact graph: an expert contribution can inform a policy, a policy can produce a rubric, a coverage gap can motivate a case, a case can expose a harness weakness, and an evaluation can become the starting evidence for an Improvement Session.
This reuse is the practical meaning of scaling expert judgment. Teammately prepares coverage structure, candidate cases, possible standards, and unresolved questions before asking an expert. The expert's response remains attributable and can be materialized into governed artifacts instead of disappearing into meeting notes.
## Product scope
Project-level foundations hold reusable knowledge and assets: Correctness Governance, Coverage Facets, Assets, Agent Setup, and Project Settings. Benchmark workspaces bind those foundations to a concrete evaluation program through Benchmark Datasets, Coverage Management, Expert Contributions, Benchmark Evaluations, and Improve.
Teammately preserves correctness evidence and makes the next engineering question inspectable. Customer teams remain responsible for downstream product, governance, deployment, and operational choices.
> Human ownership
>
> AI agents can prepare, draft, classify, generate, evaluate, and propose. A suggestion is not a governed policy, accepted expert contribution, benchmark membership decision, or retained candidate merely because an agent produced it. Use the state shown by the owning product surface.
{% example-demo title="Grounded enterprise search" %}
Coverage Engineering identifies conflicting-current-source questions as an important behavior slice. Correctness Elicitation records the expert rule that material claims must cite the controlling source or state uncertainty. Weave creates cases with current and superseded documents. Trialground evaluates a retrieval harness and exposes unsupported blends of the two sources. Coevolve starts from those failures, tests a source-selection change, and retains only candidates supported by evaluation evidence.
{% /example-demo %}
## Related workflows
{% related-card-grid title="Related workflows" %}
- [Product quickstart](/docs/quickstart)
- [The correctness loop](/docs/product-loop)
- [First correctness loop](/docs/operating-manual/first-correctness-loop)
{% /related-card-grid %}
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Product map](/docs/getting-oriented/product-map)
- [Key objects and relationships](/docs/getting-oriented/key-objects-and-relationships)
- [Product boundaries](/docs/introduction/product-boundaries)
{% /related-card-grid %}
## Source confidence
Doctrine-backed: this page follows the current public top-page story and the approved product-to-UI mapping. Linked code-backed pages define exact routes, states, and controls.
---
id: product-loop
title: The Teammately correctness loop
summary: See how coverage, elicitation, case construction, evaluation, and improvement reinforce one another.
kind: concept
product_area: introduction
status: stable
updated: 2026-08-22
canonical: /docs/product-loop
---
# The Teammately correctness loop
The correctness loop is how a team repeatedly turns domain knowledge into stronger AI behavior. It follows the five public capabilities while preserving a trace from every result back to the project context, expert contribution, case, policy, rubric, benchmark version, Harness version, and evaluation setting that made the result meaningful.
## Definition
1. **Design coverage.** Establish Dimensions, Project Topics, and Case Construction Patterns, then decide which combinations the benchmark must represent.
2. **Elicit correctness.** Use focused expert contributions to resolve policies, exceptions, applicability, disagreements, and binary rubric language.
3. **Construct the challenge set.** Create or import canonical cases, attach required materials, generate difficult variants, and curate benchmark dataset membership.
4. **Evaluate behavior.** Run an exact saved Harness Version against an exact Benchmark Version and inspect responses, Case-level Rubric evidence, comparisons, and rankings.
5. **Improve from evidence.** Start an Improvement Session with a bounded Goal Contract, explore candidates, evaluate them through the canonical path, and retain a current frontier.
6. **Return new learning.** Update coverage, correctness, cases, or the candidate according to what the evidence actually showed.
## Decision checkpoint
| Evidence says... | Responsible part of the loop | Change first |
| --- | --- | --- |
| Important behavior has no cases | Coverage Engineering or Weave | Coverage facet, construction pattern, or case set |
| Experts cannot apply the standard consistently | Correctness Elicitation | Policy scope, applicability, or rubric wording |
| A case cannot be interpreted or executed reliably | Weave and Project Input Schema | Input shape, case material, or world boundary |
| One saved candidate fails applicable rubrics | Trialground | Harness candidate or its runtime configuration |
| Several candidate branches improve different slices | Coevolve | Goal constraints, next experiment, or retained frontier |
| Result movement cannot be explained | Benchmark version and evaluation boundary | Versions, settings, mapping, or run metadata before any product change |
## How expert effort compounds
The loop should ask an expert only after agents have prepared the relevant structure and evidence. A Contribution can include selected Cases, source attachments, scoped statements, draft Policies, Rubric questions, or coverage uncertainty. Completed expert work can materialize as an attributable contributed Policy, Rubric, Case, or coverage observation through the owning workflow.
That same judgment can guide future case construction, determine which rubrics apply during evaluation, and identify missing correctness during improvement. Reuse across the loop is more valuable than maximizing the number of disconnected review actions.
## How product scope changes through the loop
Project foundations are reusable. Project Context, Reference Materials, policies, rubrics, Coverage Facets, Cases, and Harnesses do not belong to only one benchmark. A benchmark workspace selects and versions the relevant subset, manages coverage, coordinates contributions, evaluates candidates, and records improvement.
This scope distinction prevents accidental drift. Editing a project-level policy may affect several benchmarks. Changing dataset membership should create a new benchmark evidence boundary. Saving a Harness draft is different from selecting an exact saved Harness version for a Run.
## Before and after
| Before | Loop work | After |
| --- | --- | --- |
| Domain knowledge is distributed across people and files | Agent Setup and Correctness Elicitation organize it | Project context and governed correctness artifacts are inspectable |
| Examples are convenient rather than deliberate | Coverage Engineering and Weave shape the challenge set | Dataset representation and missing coverage are explicit |
| Candidate behavior is discussed from anecdotes | Trialground runs a versioned evaluation | Case-level rubric evidence and comparisons are available |
| Improvement is a sequence of untracked edits | Coevolve starts from pinned evidence | Candidate branches, receipts, chronology, and current frontier remain connected |
{% example-demo title="Changing a retrieval harness" %}
An evaluation shows failures only when current and superseded documents appear together. The team first confirms that the coverage slice and grounding rubric are valid. An Improvement Session pins those cases and the failing Harness version, then tests source-date filtering and citation-selection candidates. A stronger candidate becomes part of the current frontier only after a canonical evaluation produces the expected rubric evidence. If the work uncovers an unseen source-conflict pattern, that observation returns to Coverage Management.
{% /example-demo %}
## Related workflows
{% related-card-grid title="Related workflows" %}
- [Product quickstart](/docs/quickstart)
- [Run a benchmark evaluation](/docs/benchmark-evaluations/run-evaluation)
- [Start an Improvement Session](/docs/improve/start-improvement-session)
{% /related-card-grid %}
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Product map](/docs/getting-oriented/product-map)
- [Project Input Schema](/docs/project-settings/input-schema)
- [Expert Contributions](/docs/expert-contributions)
{% /related-card-grid %}
## Source confidence
Doctrine-backed: this page explains the approved operating loop. Linked product pages are the authority for exact controls and lifecycle states.
---
id: quickstart.product
title: Product quickstart
summary: Configure one project foundation, one benchmark slice, one expert contribution, one evaluation, and one evidence-backed improvement.
kind: quickstart
product_area: introduction
status: stable
updated: 2026-09-07
canonical: /docs/quickstart
---
# Product quickstart
Run one narrow correctness loop. The goal is not a large benchmark; it is a traceable chain from project context and deliberate coverage to expert-grounded standards, a versioned evaluation, and one justified next change.
## When to use it
Use this path for a new project or for an existing AI system whose correctness work is scattered across documents, examples, and informal expert feedback. Choose one behavior slice with a clear specialist owner.
## Decision checkpoint
| Starting point | First action | Ready to continue when... |
| --- | --- | --- |
| Agents do not understand the product or domain | Complete Agent Setup | Project Context and controlling Reference Materials are inspectable |
| Cases arrive in inconsistent shapes | Configure Project Input Schema | One input architecture and any required case materials are declared |
| Important behavior is not represented deliberately | Define Coverage Facets | Dimensions, Project Topics, and Case Construction Patterns name the slice |
| Correctness depends on tacit judgment | Request an Expert Contribution | The expert's scope, selected evidence, and required decisions are explicit |
| Cases and standards are ready | Create a benchmark snapshot and evaluate a saved Harness version | Exact cases, rubrics, candidate, and settings are bound to the Run |
## Prerequisites
- A Teammately project for the specialist AI behavior.
- An accountable project operator and at least one domain expert.
- A small number of representative examples or enough Reference Materials to construct them.
- A candidate system that can be represented by a saved Harness version before evaluation.
## Before and after
| Before | Action | After | Stop if... |
| --- | --- | --- | --- |
| Domain context is implicit | Write the Project Agent Brief and connect Reference Materials | Agents have explicit project understanding | Controlling sources are missing or contradictory without an owner |
| Inputs and supporting artifacts vary | Save Project Input Schema | Cases share one canonical content contract | Existing cases cannot satisfy the proposed schema |
| Expert knowledge is tacit | Request and complete a focused contribution | Policies, rubrics, cases, or coverage observations can be materialized | The request asks for a label without the evidence needed to explain it |
| Candidate behavior is anecdotal | Evaluate a saved Harness version | Results are traceable to cases and applicable rubrics | Dataset snapshot or candidate version is ambiguous |
| A weakness is confirmed | Start an Improvement Session from evidence | Candidate work follows a bounded Goal Contract | The requested outcome has no pinned measurement binding |
## Steps
1. Open or create the project and write the Project Agent Brief in **Agent Setup → Project Context**.
2. Add controlling knowledge through **Agent Setup → Reference Materials → Materials**, then inspect the published blocks in **Indexed Reference**.
3. Configure **Project Settings → Input Schema**. Select plain text, chat, or structured input and declare required case materials and accepted artifact families.
4. Create the smallest useful set of Coverage Facets: a Dimension, relevant Project Topics, and a Case Construction Pattern for the chosen behavior slice.
5. Add or construct cases in Assets, then select the intended cases in **Benchmark Datasets**. Confirm Representation and create or choose the appropriate snapshot.
6. In **Expert Contributions**, request one focused contribution. Select the expert, state the objective, attach or select the relevant cases, and include only the contribution components needed to resolve the question.
7. Inspect the completed contribution and materialize accepted policies, rubrics, cases, or coverage observations through their owning surfaces.
8. Save an exact Harness version. In **Benchmark Evaluations**, configure and run it against the selected benchmark version.
9. Inspect Dashboard and List results before using Compare or Arena. Trace important movement to case-level rubric evidence and run metadata.
10. If a candidate change is justified, open **Improve**, start from the relevant evidence, prepare and confirm the Goal Contract, and evaluate candidate work through the canonical Run path.
## Object and state changes
This path can create or update Project Context, Reference Materials items and indexed blocks, Project Input Schema, Coverage Facets, Cases, benchmark dataset membership and snapshots, Contributions, contributed artifacts, policies, rubrics, Harness drafts and saved versions, Runs, evaluation results, and Improvement Sessions. Each object keeps its own authority boundary; completing one step does not automatically approve or materialize every downstream artifact.
## Success criteria
- Another operator can identify the project context and source material used by agents.
- The case set conforms to the Project Input Schema and represents a named coverage slice.
- Expert judgment is attributable to a completed Contribution and its accepted artifacts.
- The evaluation binds an exact benchmark version to an exact saved Harness version.
- Any improvement work starts from pinned evidence and records its Goal Contract, candidate results, and current frontier.
## Common failure modes
- Treating Reference Materials as approved policies.
- Asking experts broad questions without selected cases or a concrete contribution objective.
- Evaluating an unsaved Harness draft or an unclear benchmark snapshot.
- Reading only an aggregate score and skipping failed case/rubric pairs.
- Starting improvement before the target and measurement evidence are resolved.
## Related reference pages
{% related-card-grid title="Related reference pages" %}
- [Agent Setup](/docs/agent-setup)
- [Project Input Schema](/docs/project-settings/input-schema)
- [Expert Contributions](/docs/expert-contributions)
- [Benchmark Evaluations](/docs/benchmark-evaluations)
- [Improve](/docs/improve)
{% /related-card-grid %}
## Related troubleshooting pages
{% related-card-grid title="Related troubleshooting pages" %}
- [Dataset upload](/docs/troubleshooting/dataset-upload)
- [Unclear cases](/docs/troubleshooting/unclear-cases)
- [Benchmark results changed unexpectedly](/docs/troubleshooting/benchmark-results-changed-unexpectedly)
{% /related-card-grid %}
## Source confidence
Doctrine-backed: this quickstart connects the current public story to code-backed product surfaces. Follow the linked pages for exact states and controls.