Application intake

A loan application at Aster Financial arrives with a mismatched income figure and an unreadable identity document, and the agent must hold it rather than route it onward.

A checklist of structural traits, such as multiple channels, written completeness rules and real routing alternatives, that indicate an intake process can support measurable agent training or evaluation.

An intake agent typically touches a submission store, case system, party records, document store and policy rules, and a realistic environment models the keys that link them.

Intake turns a new application, request or case into a checked, routed record, and its exceptions and handovers make it a realistic test of agents acting in connected systems.

Intake forces an agent to change state in connected systems and handle duplicates, gaps and ambiguous routing, with outcomes that can be written down and graded.

A loan application at Aster Financial arrives with an incomplete payslip and an employer mismatch, and the intake officer's choices show exactly which state changes an agent must get right.

Intake can be modelled as six linked entity types with a declared status transition table and tool actions that mirror those transitions, exportable as JSONL or Parquet.

A checklist of the process traits that give an interactive environment something meaningful to test, from multi-channel arrival to timers that change the correct next action.

Intake is where a submission is captured, checked and routed, and every step changes state, which makes it a natural basis for interactive environments where agents act and are graded.

Intake is bounded but not trivial: it has clear start and end states and many places to go wrong, so it suits first-stage environment design and grading.

Extraction benchmarks test whether a field was read. Intake tests whether the right decision was made with the fields, using policies, existing records and changing case state.

An intake officer at Aster Financial receives a broker-submitted loan request with one payslip instead of two. The case shows how a completeness verdict changes status, routing and later workload.

Intake is where a new application, claim or request is captured, checked and routed. Its explicit rules and visible downstream costs make it a practical target for evaluating agents.

Each intake stage can be exposed as a tool action over a shared case store, with reference data, a state log and an answer key that the harness reads after the run.

A checklist of characteristics that make an intake process measurable: written rules, finite routing, deliberate ambiguity, inspectable state and visible downstream cost.

A home loan application at Aster Financial arrives incomplete, is routed on a judgement call, and later returns to intake. The case shows why event histories matter more than final records.

Describes a case-as-graph model for intake: entity tables, stable keys, a sequence number per event, and a rule identifier that ties outcomes back to policy.

A short fit test for resellers deciding whether a generated intake line suits their catalogue, covering customer needs, licence permissions, schema documentation and risk review.

Direct answers on what intake data adds beyond decision outcomes, which domains it spans, and why generated material still needs checks for grounding and disclosure risk.

Intake is the first stage of most business processes: capture, completeness checking and routing. It carries many small decisions that AI agents must learn, which makes it a useful catalogue line.

Intake turns a messy incoming submission into a structured, checked and routed case, which makes it a compact test of reading, rule application, tool use and handover.

Intake becomes learnable when submissions, cases, checks, actions and policy rules are stored as typed records linked by identifiers, in formats such as JSONL and Parquet.

A checklist of eight observable traits that indicate an intake process contains enough rules, variation and handover detail to be useful for training or evaluation material.

A home insurance claim arrives with a malformed policy number and no photos, and the intake officer repairs the link, requests documents and routes only when the checklist is met.

A short question-and-answer on why intake is a useful target for training and evaluation, what makes it hard to learn from public text, and what a buyer should check first.

A utility connection task pairs two cases for the same lot, where the second corrects the first, and defines the exact links, field changes and queue move expected.

A checklist of elements to confirm before accepting a release, from case types and exception classes through to expected outcomes and starting environment state.

A release can use JSONL for cases and tasks, Parquet for large tables, structured policy files and foreign keys that let a loader rebuild the full case graph.

Task packs can carry structured policies, deliberate rule conflicts and starting state, across several domains, either as static files or as a simulated environment with tool actions.

A release can contain cases, parties, documents, notes, policies and expected outcomes, generated to be consistent with one another so agents can be both trained and graded.

A release could contain generated records, relationships, policies and tasks with starting state, so an environment has data, rules and goals that fit together.

Yes, a release can carry structured and readable policy rules, several snapshots of one case, and tagged messy submissions with exception types a buyer specifies.

A checklist of the record sets that let a seeded environment run an episode end to end, from cases and parties to scripted events and a transition table.

A task pack can be delivered as a JSONL file of task specifications that reference Parquet or JSONL snapshots, with scripted events, assertions and protected fields.

A task pack for a utility connection request carries starting state, a policy rule, a scripted late document and an expected end state, which together define pass or fail.

A release can be organised as linked tables in JSONL or Parquet, with stable identifiers connecting cases, parties, documents, policies, events and answer keys.

A checklist of the choices that shape a generated intake release: domain, channels, policies, defect mix, relationships, answer-key depth and handover fields.

Yes. Policies, routing tables, exception rules and effective dates can be generated alongside cases, so that every task has a rule base and a defensible answer.

A release can include case records, parties, documents, linked prior cases, versioned policies, routing tables, tasks with answer keys and initial environment state.

A storm-damage claim, CLM-0412, starts in a simulated insurance environment with policy, prior claim and routing data. The agent's actions change state and expose a routing trap.

Covers JSONL event streams, Parquet entity tables, JSONL task packs, a manifest and versioned policy files, plus schema and change-note requests resellers should make.

Lays out the layers a scoped release can include: records, relationships, policies, tasks and simulated environment state, and why you should state which layers each product holds.

Answers what can be scoped beyond plain records: policy files, exception types, generated free text, multiple domains, and deliberately unpopulated fields.

A specification list for resellers covering records, items, checks, routing decisions, handovers, policies, tasks and packaging, so scope is explicit before generation begins.

A storm damage claim notification task shows starting state, tools, a reference path, a scripted customer reply and a scored consequence, the parts a task pack holds.

A utilities connection request has two applicable rules and a missing licence number, and the task tests whether an agent applies them in the right order before routing.

A simulated environment can ship as seed snapshots in JSONL, a JSON Schema tool contract, task files with reference states, and a reset call that accepts seeds and perturbations.

A release can combine records, relationships, policies, tasks and environment state, and a buyer can choose a single layer or the full set depending on the training or evaluation goal.

A question-and-answer on whether generated intake material can carry numbered policies, queue workloads, exception paths and time-dependent rules alongside case records.

A checklist of eight record types a buyer can name in a request so a generated sample is built around the fields, links and outcomes the evaluation needs.

A checklist of consistency, policy, exception-mix, realism, disclosure and reset checks a buyer can run on a first generated sample before committing to anything larger.

Reset, action logs, state export, a field-level grader and a separate violation check make runs comparable across agent versions and reusable as training trajectories.

Separate false completeness, false incompleteness, wrong routing and record damage, then test escalation, grade state rather than prose, and use held-out splits to detect overfitting.

Quality comes from internal consistency, policy fidelity, exception coverage and a review of grounding and disclosure risk, all of which a buyer can check on a sample independently.

An evaluation engineer runs an agent on a clinic referral task, and structured expected outcomes show it routed an unsigned referral, pinpointing a document-checking weakness.

Quality shows up in data consistency, rule fidelity, grader behaviour and grounding, and each can be checked on a small sample before any agent is trained or scored.

An evaluation lead replays a patient referral run, finds the agent never searched for a near-duplicate party, and rescores a superficially correct run as a failure.

Beyond tool calls, an environment benefits from deterministic reset, a JSONL step log, a state diff, a per-criterion grader interface and a clear error contract.

A checklist of file-level and run-level checks, covering keys, transitions, timestamps, rule coverage, reference solutions, determinism, protected fields and disclosure review.

Score several criteria separately, add trace checks beside final-state checks, and watch for known failure modes such as early routing, overwriting fields and duplicate creation.

An evaluation lead finds an agent scoring well on utility connection requests by reading the submission channel, not the case, and fixes it with a rebalanced pack.

Score false completeness, over-chasing, missed duplicates, wrong merges, unsupported handover notes, skipped escalations and use of a superseded policy, alongside final routing.

A checklist for reviewing a sample: key correctness, link integrity, label balance, trap coverage, policy versions, leakage and resemblance to real people.

Inspect three layers: internal consistency of records, agreement between policies and answer keys, and realism and disclosure risk of the generated content.

Log each run as an event trace, then grade final state, required checks and handover content with separate hooks against the answer key, with reset between tasks.

Eight acceptance checks covering state transitions, rule conformance, exception coverage, leakage, template echo, split hygiene, disclosure scanning and record keeping.

Lists seven failure modes, including label leakage, split leakage and template echo, with practical tests for the two that resellers most often miss.

Four inspection lenses: internal consistency, rule conformance, coverage of awkward cases, and grounding and disclosure review, with split hygiene added for training use.

A reseller analyst runs transition and policy-join checks on a utility connection sample, finds a misrouted priority case, holds the batch and sends the defect back.

Describes environment reset by seed, a fixed tool set, JSONL trace output with state hashes and a component scorer, so agent runs can be repeated and compared.

An agent correctly requests a missing dangerous goods declaration, then books and routes the shipment anyway, and the grader separates the outcome, transition and rule-citation failures.

A question-and-answer listing common intake failure modes, how a grader can detect each, how to tell model failures from data failures, and why escalation tasks matter.

Graders can read the final environment state and ordered action trace, apply final-state, transition and note checks, and write per-criterion results to JSONL and Parquet.

Quality shows in four places: realistic case untidiness, coherent rules, checkable reference outcomes, and an inspection of grounding and disclosure risk in free text.

A checklist of eight inspection steps covering rule tracing, status validity, free-text review, case mix, reviewer agreement, date consistency, baseline failures and reset repeatability.

An engineering lead specifies one claim path, the tool actions, four exception classes and the delivery form, then checks the first sample before any larger release is requested.

A checklist for assembling a brief that is specific enough to scope and judge, covering path, systems, policies, exceptions, delivery form, intended use, success criteria and review.

Ask for a small spread of clean and exception tasks with policies, start state, expected outcomes and a schema, then agree licence limits and success criteria before starting.

Scope one intake path, one policy set and a deliberate exception mix, choose the lightest delivery form that answers your question, and settle licensed use early.

A pilot folder layout, release and task identifiers, dev and test splits, a tool mapping table and reset-and-export calls let delivery match an existing harness.

A pilot delivery is easier to review with a manifest, schemas, snapshots, task specifications, policies, reference traces, graders and a licence notice, with stable keys and versioning.

An environment lead specifies one request type and four exception families, then tests a sample with consistency checks and reference solutions, and returns findings by task id.

A generated sample still needs your own review for grounding and disclosure risk, and permitted uses are set out in a licence agreed directly with MirrorFirm.

Scope starts from the decisions the environment must exercise: request types, exception families, interface format and licence needs, then a small sample before any larger commitment.

A checklist of items to specify in a sample request, from request types and exception families to formats, reference solutions, licence terms and defect reporting.

Decide the case types, depth, delivery form and intended use first, then agree licence terms in writing that cover training, evaluation, publication and redistribution.

Validate schema and keys, wire tool actions to your harness, confirm deterministic reset, run a baseline and record versions so later releases can be compared.

An evaluation lead narrows a referral-intake pilot from many referral types to two, defines the questions it must answer and agrees what the first sample must show.

Permitted uses depend on the licence agreed for your engagement. Ask for training, evaluation, publication and redistribution rights to be stated explicitly in writing.

A marketplace product manager scopes a freight booking pilot with a written acceptance test, passes one check, fails another, and defers listing pending a revised sample.

Covers what to request in a sample, how to settle permitted uses such as training and derivatives, what a release does not cover, and how to describe it honestly to customers.

Four scoping decisions before listing: domain and depth, components, licence terms to settle in writing, and the limits that come with generated material.

Practical setup steps: manifest, schema files, catalogue metadata fields, a staging area, update versioning, evaluation wiring and a provenance record for each release.

Eight points for a written pilot request: domain, exceptions, schema, free text, acceptance test, permitted uses, stated limits and a defect reporting process.

A question-and-answer on licensable forms, the limits of generated output, why privacy or compliance should not be assumed, how changes are raised, and what to prepare.

A model lab's applied team scopes a narrow personal loan pilot on Aster Financial, reviews a small sample, lists ambiguities, and uses them to decide the next scope.

A pilot can ship as a self-describing bundle with a manifest, schemas, JSONL cases, Parquet tables, policies, tasks, environment seeds and grader files, plus split and feedback conventions.

A checklist of eight requests to include in a scoping note so a pilot sample is narrow, documented, balanced, checkable and clear about licence scope and limits.

Scoping begins with the intended use, then narrows the domain, channels, rules, formats and tools, and treats licence terms as a direct conversation with MirrorFirm.