# Coevolve Generated: 2026-09-13T04:34:28.323Z Source build: local Canonical docs: https://teammately.ai/docs --- id: concepts.coevolve title: Coevolve summary: Explore multiple evidence-backed candidate directions while keeping goals, benchmark receipts, trajectories, and the current frontier connected. kind: concept product_area: coevolve status: stable updated: 2026-08-22 canonical: /docs/concepts/coevolve --- # Coevolve Coevolve is the improvement capability inside Teammately correctness infrastructure. It lets agents and engineers pursue multiple candidate directions from benchmark evidence, evaluate those candidates through the same canonical path, and continue from stronger branches without losing the goal, chronology, or evidence behind the current frontier. ## Definition Coevolve is realized through **Improve** and durable **Improvement Sessions**. A session starts from selected benchmark evidence and a target Harness. A Goal Contract turns a free-form objective into pinned target identities, measurement bindings, constraints, unresolved items, and permitted intervention boundaries. Once confirmed, that contract governs future work without rewriting earlier evidence. Sessions can use Work or Evolve behavior. Work supports bounded implementation and review. Evolve supports parallel candidate exploration across epochs and retains a frontier based on current evaluation evidence. Candidate Harness versions become meaningful only when canonical Runs return observable results. ## Decision checkpoint | Situation | Improvement action | Evidence requirement | | --- | --- | --- | | One known candidate change needs implementation | Start a Work session | Confirmed Goal Contract and pinned target evidence | | Several hypotheses should compete | Start an Evolve session | Explicit authorization, measurement bindings, and comparable evaluation path | | An external coding worker will implement | Prepare a scoped worker package | Returned Harness version or evaluation request before claiming observable progress | | A candidate looks stronger | Inspect the current frontier | Canonical evaluation receipts support the retained position | | Exploration exposes missing correctness or coverage | Return the observation upstream | Identify the policy, rubric, case, or coverage artifact that must change | ## Evidence-backed branching Candidate exploration is not a sequence of undocumented edits. Each proposal should state the hypothesis and its relationship to the Goal Contract. Evaluation receipts bind candidate identity to benchmark identity and result. Narrated trajectories can explain the work performed, while chronology records durable transitions, agent activity, pauses, resumptions, and terminal state. The current frontier is not simply the newest candidate. It represents the candidates retained by the session's evidence and goal constraints. A candidate can improve one slice and regress another; the frontier and comparison views keep that tradeoff visible. ## Bridge between experts and engineers Improvement can reveal that the candidate is not the only incomplete part of the system. A missing coverage tuple, unclear rubric, contradictory policy, or insufficient case material should become an upstream contribution opportunity. This is how Coevolve connects coding agents to domain experts: engineering work is guided by benchmark evidence, and newly discovered correctness questions return to focused expert work. External workers remain bounded. Teammately may prepare a package for Codex, Claude Code, or another worker, but it records only the work returned through the defined contract. It does not infer private activity or fabricate a working state. {% example-demo title="Parallel grounding hypotheses" %} An Evolve session starts from failures involving conflicting policy documents. One candidate changes retrieval filtering, another changes source ranking, and a third changes answer construction. Each saved Harness version is evaluated against the pinned benchmark. The frontier retains the candidates supported by grounding and uncertainty rubrics, while a newly observed source-authority ambiguity becomes an Expert Contribution opportunity. {% /example-demo %} ## Related workflows {% related-card-grid title="Related workflows" %} - [Start an Improvement Session](/docs/improve/start-improvement-session) - [Inspect evaluation results](/docs/benchmark-evaluations/inspect-results) - [Request an Expert Contribution](/docs/expert-contributions/request-contribution) {% /related-card-grid %} ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Improve](/docs/improve) - [Harnesses](/docs/assets/harnesses) - [Product boundaries](/docs/introduction/product-boundaries) {% /related-card-grid %} ## Source confidence Doctrine-backed: this page defines Coevolve as the public capability. The Improve pages provide code-backed session, contract, candidate, and frontier behavior. --- id: improve.overview title: Improve summary: Coordinate durable Improvement Sessions from pinned benchmark evidence through Goal Contracts, candidates, evaluations, trajectories, and frontiers. kind: concept product_area: improve status: stable updated: 2026-09-07 canonical: /docs/improve --- # Improve Improve is the benchmark-scoped workspace for coordinating candidate work from benchmark evidence. Each Improvement Session pins the exact starting evidence it uses, including any Benchmark Version, Harness Version, and Run identities. Sessions preserve the goal, authorization, agent and worker activity, candidate Harness Versions, canonical evaluation receipts, and Current frontier. ## Definition A new session begins with **Start from evidence**. The operator selects the target and relevant benchmark evidence, then prepares a **Goal Contract**. The contract binds the free-form objective to canonical target identities, measurement bindings, constraints, unresolved items, and intervention boundaries. The current Improve workspace offers **Coevolve**, **External Agents**, and **External fine-tuning** experiences where enabled. Coevolve supports bounded Work and Evolve behavior; external experiences use explicit handoff or provider boundaries. All retained candidates still rely on observable Harness Versions and canonical Benchmark Evaluations rather than unrecorded claims of progress. ## Decision checkpoint | Need | Session choice | Required evidence | | --- | --- | --- | | Investigate or implement one bounded direction | Work | Confirmed Goal Contract and pinned failures or target evidence | | Explore several competing hypotheses | Evolve | Comparable measurement plan and explicit exploration authorization | | Delegate implementation externally | Worker package | Pinned target, return contract, and observable returned candidate | | Decide which candidates remain viable | Current frontier | Canonical evaluation receipts and goal constraints | | Stop scheduling new work temporarily | Pause | Retained evidence and chronology remain available | | Correct missing benchmark meaning | Contribution or coverage follow-up | Upstream policy, rubric, case, or coverage observation is named | ## Goal Contract and chronology The Goal Contract is the session's authority boundary. Confirm it only after the target and measurement plan are resolvable. Later revisions should remain explicit rather than silently changing what earlier candidates attempted. Chronology records durable session transitions, agent activity, proposed approaches, candidates, evaluations, pauses, resumptions, cancellation, and terminal state. Narrated trajectories expose bounded work segments and can identify unannounced durable activity without pretending to know private external-worker behavior. ## Candidates and current frontier A candidate should identify its Harness version, hypothesis, parent or branch context, and evaluation evidence. The current frontier represents retained candidates supported by the latest valid evidence and Goal Contract. It is not synonymous with the most recent candidate or the highest aggregate score. Evaluation receipts link candidates back to Benchmark Evaluations. If a candidate improves one objective but violates a constraint or regresses a must-level rubric, the frontier should keep that tradeoff visible. ## Human and external-worker boundary Operators can pause, resume, cancel, review, or redirect work through the available session controls. External worker packages can carry the confirmed goal and evidence to Codex, Claude Code, or another worker. Teammately records only returned artifacts and requests that satisfy the return contract; it does not infer private work state. {% example-demo title="Evolve a grounding harness" %} The Goal Contract targets unsupported blends of current and superseded sources without regressing missing-source uncertainty. Evolve explores retrieval filtering, source ranking, and response-construction branches. Each candidate Harness version is evaluated against the pinned benchmark. The current frontier retains the candidates supported by both grounding and uncertainty evidence, while a newly discovered policy conflict becomes an Expert Contribution opportunity. {% /example-demo %} ## Related workflows {% related-card-grid title="Related workflows" %} - [Start an Improvement Session](/docs/improve/start-improvement-session) - [Write and confirm a Goal Contract](/docs/improve/goal-contracts) - [Choose Work or Evolve](/docs/improve/work-and-evolve) - [Interpret candidates and the current frontier](/docs/improve/candidates-and-frontier) - [Read chronology and trajectories](/docs/improve/chronology-and-trajectories) - [Inspect evaluation results](/docs/benchmark-evaluations/inspect-results) - [Request an Expert Contribution](/docs/expert-contributions/request-contribution) {% /related-card-grid %} ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Coevolve](/docs/concepts/coevolve) - [Harnesses](/docs/assets/harnesses) - [Product boundaries](/docs/introduction/product-boundaries) {% /related-card-grid %} ## Source confidence Code-backed: the active versioned Improve workspace and session contract define starting evidence, Goal Contracts, Work and Evolve behavior, candidates, receipts, chronology, frontiers, and lifecycle controls. Internal coordinator implementation is supporting evidence, not a public API. --- id: concepts.trialground title: Trialground summary: Evaluate exact Harness and Benchmark Versions in a managed proving ground with inspectable responses and Rubric evidence. kind: concept product_area: trialground status: stable updated: 2026-09-07 canonical: /docs/concepts/trialground --- # Trialground Trialground is the evaluation capability inside Teammately correctness infrastructure. It gives AI engineers a managed proving ground for running exact candidate implementations against exact Benchmark Versions while preserving the response, Rubric outcomes, settings, and metadata needed to explain the result. ## Definition A candidate is represented by a saved Harness version rather than an informal description of a prompt or code state. A benchmark version provides the cases and governed correctness boundary. Benchmark Evaluations binds those inputs into Runs, computes rubric-level evidence, and exposes results through Dashboard, List, Compare, Arena, and run-detail views. Trialground names the capability; **Harnesses** and **Benchmark Evaluations** are the current UI surfaces. The distinction matters when giving instructions: users edit and save a Harness under Assets, then select its saved version for a benchmark evaluation. ## Decision checkpoint | Question | Inspect | Required boundary | | --- | --- | --- | | What code or prompt was evaluated? | Harness and saved version | Draft editor state is not the candidate identity | | What behavior space was tested? | Benchmark version and dataset snapshot | Dataset membership is fixed for interpretation | | Which standards judged each case? | Applicable policies and rubrics | Suggested or unrelated standards are excluded | | Why did a candidate pass or fail? | Case response and applicable Rubric outcomes | Aggregate movement is traced to Case-level evidence | | Can two candidates be compared? | Compare or Arena | Both candidates expose compatible benchmark and settings boundaries | ## Managed evaluation Harness Assets provide a multi-file editing surface, Draft and Saved states, coding-agent assistance, secret references, and benchmark activation. Evaluation setup selects the relevant Harness version and preserves run metadata. Managed execution can scale case work while keeping every result attached to the benchmark and candidate identities. The current Benchmark Evaluations API does not expose execution trajectories, spans, private reasoning, or tool traces. The visible **Traces / Spans** segment is therefore a capability fence, not inspectable Run evidence. Improve's narrated trajectories are separate session chronology and must not be described as evaluation execution traces. Rubric outcomes remain the governed evidence within the evaluation contract. ## Reading evidence Start with Dashboard to understand overall progress and distribution, then use List for Run and Case-level results. Compare is a symmetric matrix whose columns are saved Harness Versions and whose rows can group Cases, all results, Policies, Rubrics, Dimensions, Topics, Groups, or Patterns. Arena computes pair evidence from admitted results; it is not a subjective preference interview. Run detail is the place to resolve execution state, output mapping, response evidence, and failures. When a result is surprising, check versions and configuration before changing the candidate. A different benchmark snapshot, Harness version, output mapping, evaluation setting, or run-metadata value can explain movement that otherwise looks like a behavior change. {% example-demo title="Retrieval candidate comparison" %} Two saved Harness versions use different source-selection strategies. Both run against the same benchmark version containing current-source, stale-source, and missing-source cases. Compare shows that one candidate improves current-source grounding but regresses when no controlling source exists. The team opens the failed cases and rubric results before deciding whether the candidate belongs in the next Improvement Session. {% /example-demo %} ## Related workflows {% related-card-grid title="Related workflows" %} - [Run a benchmark evaluation](/docs/benchmark-evaluations/run-evaluation) - [Inspect evaluation results](/docs/benchmark-evaluations/inspect-results) - [Start an Improvement Session](/docs/improve/start-improvement-session) {% /related-card-grid %} ## Related reference pages {% related-card-grid title="Related reference pages" %} - [Harnesses](/docs/assets/harnesses) - [Benchmarks](/docs/object-model/benchmarks) - [Run metadata](/docs/benchmark-evaluations/run-metadata) {% /related-card-grid %} ## Source confidence Doctrine-backed: this page defines Trialground as the public capability. Harness and Benchmark Evaluation pages provide code-backed behavior and state details. --- 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.