Practical guides to process mapping, automation, batch images and files, local LLMs, frontier AI, and business process API design.

Process mapping turns an informal sequence of tasks into a reviewable description of work. Start with a trigger, define the accepted outcome, and identify the person or system responsible for each transition. The most useful map explains waiting, rejection, correction, and cancellation as clearly as the successful path.

Keep the first map small enough to review with the people doing the work. A supplier approval, document intake, or image publishing flow is a better starting point than an entire department. Separate the current process from the intended future process so assumptions do not quietly become requirements.

What to capture for each step

Record the required input, accountable role, decision rule, output evidence, and exception owner. A box labeled “approve” is incomplete until the team agrees who can approve which version of the input. A box labeled “send” needs a definition of what happens when the destination accepts the request but its response is lost.

Use ordinary flowcharts for simple discussions and a formal notation when the audience or tooling requires it. Notation should help the team explain its decisions, not hide uncertainty behind more symbols. Version the map alongside the requirements that it informs.

Turn a map into an implementation brief

Describe the states that matter to the business separately from worker activity. A document can be awaiting approval while a notification worker retries. Those are different kinds of waiting and should not be represented as the same failure.

Attach representative examples to the map. Include a normal submission, missing information, a duplicate event, a late approval, and a changed input. Write the expected outcome for each before choosing endpoints or queue technology. These examples become useful acceptance tests.

A practical first deliverable

Produce one reviewed diagram, a short data dictionary, a transition table, and an unresolved-questions register. Identify the process owner and the evidence needed to declare a pilot successful. Then automate one narrow transition while preserving a documented manual fallback.

The process automation guide picks up where the map ends. For an interface-oriented view, explore the business process API guide. The goal is a system whose behavior follows an agreed process, rather than a process that has to bend around an unexplained script.

Start with one recurring task that has recognizable inputs and an outcome a person can verify. Separate preparing evidence from making a business decision. A workflow can automatically validate a package while leaving approval with an authorized reviewer.

The trigger might be an event, a schedule, or a manual command. It identifies when the application should consider work; it does not prove that the intended operation is new. Give the business task a stable identity and retain event identities separately.

Store what the workflow knows

An accepted run needs durable state, an input reference, a process version, and timestamps. Define transitions such as queued, running, awaiting review, succeeded, and failed according to the actual workflow. Keep each transition tied to an owner and a condition.

A restart should not erase the system's understanding of completed work. Record useful checkpoints and item-level results. The operator should be able to distinguish a legitimate business wait from a worker failure without reconstructing the whole run from logs.

Plan retries around the action

An interrupted request may have succeeded at its destination. Use stable operation identities and the destination's documented idempotency behavior where available. When the outcome is uncertain, reconcile before repeating a side effect that could create another record or notification.

Classify recoverable and permanent failures separately. Bound attempts and elapsed time. Route unresolved work to an owner with enough context to decide whether to repair, resume, or cancel. A generic retry button is not an adequate recovery procedure for every failure.

Make the pilot observable

Choose a representative input set and test duplicate delivery, missing data, delayed responses, and worker restarts. Define expected business outcomes before running those tests. Count accepted results and exceptions rather than presenting attempts as completed work.

Begin with a limited rollout and a clear stop condition. Keep the manual path available until the team understands the automated one. The process mapping guide helps define the underlying agreement, while the job lifecycle reference provides a compact example of the states used to observe it.

Batch image processing starts with an output contract, not a folder of files. Specify dimensions, composition, format, transparency, metadata handling, and naming for each destination. A square social card and a small website preview have different requirements even when they share a source.

Decide whether to crop, pad, or request a new composition when the original aspect ratio differs. Keep important typography inside a safe margin. Test the actual thumbnail size as well as the full-resolution image before accepting a template for the entire library.

Keep input and output traceable

Create a manifest containing asset identity, source version, checksum, expected variants, and transformation version. Preserve original files and write derivatives to a separate location. A revised transformation should produce an identifiable result rather than silently obscure the previous one.

Inspect actual file type, dimensions, and frame or page count. Set limits appropriate to the worker environment. Distinguish an unreadable file from a temporarily unavailable source so retry decisions address the actual problem.

Treat delivery as its own step

Validate dimensions, format, decodability, and expected output count before publishing. Write through a staging location or another completion mechanism supported by the destination. Avoid exposing a partial file under a URL that implies a finished asset.

Automated checks do not settle editorial quality. Review representative crops, light and dark artwork, transparency, and headline readability. A contact sheet can reveal inconsistent composition that individual success messages miss.

Scale only after the contract works

Use bounded concurrency and observe memory, elapsed time, and accepted outputs under realistic input sizes. Retain item-level results and retry only the failures that justify another attempt. Completion should reconcile to the manifest, including rejected or review-required assets.

Our image pipeline playbook walks through these decisions in detail. Use the batch manifest reference when connecting images to a broader workflow that also handles files or model jobs. A dependable batch delivers both usable pixels and an explanation of every asset's outcome.

Define the expected input set and the meaning of a completed batch. Decide whether partial delivery is permitted, what happens to late files, and whether a correction replaces a source or creates a new version. These choices belong to the business contract, not an incidental implementation detail.

Identify each input with a stable file identity and a versioned manifest. Preserve the received bytes in a controlled location when policy permits. Avoid trusting a filename or extension as proof of content, age, or correctness.

Validate format and business meaning separately

Parsing determines whether the file can be read. Business validation determines whether the extracted records are acceptable. A row can parse correctly while carrying an unknown identifier, a missing required value, or an ambiguous unit.

Document supported schemas, encodings, empty-value meanings, and normalization rules. Preserve source evidence needed to explain a transformed value. Route ambiguity to an explicit exception instead of silently converting it into a plausible-looking result.

Keep errors at the right level

Separate row-level rejection from file-level failure. A missing column may invalidate an entire export, while one unknown item code might affect only a record. Report stable reason codes and source locations without exposing unnecessary sensitive content in logs.

Quarantined files need an owner and a reprocessing policy. Treat corrected input as a new source version so the original failed run remains explainable. Apply type, size, and extraction limits to protect the processing environment from untrusted files.

Publish a verifiable result

Stage outputs, inspect the exported representation, and reconcile accepted, rejected, and unresolved counts to the manifest. Publish a versioned dataset with the accepted inputs and transform version. The consumer should know exactly which result it received.

The file processing playbook develops a supplier-export example. The business process API guide explains how to expose a long-running file batch through a clear status and result contract. Keep the handoff as explicit as the transformation itself.

Choose a bounded job such as classifying approved internal notes or extracting a small set of fields. Define the allowed outputs and when the system should return insufficient information. A structured answer still needs evidence that it matches the source.

Create a representative evaluation sample before selecting a configuration. Include long inputs, missing context, ambiguous cases, and material that should not produce an answer. Keep a held-out set separate from prompt development.

Make the local boundary concrete

Inventory the model server, worker, storage, logs, monitoring, model downloads, and update process. Running inference locally does not by itself establish the behavior of every supporting component. Define approved network paths and access to source content.

Record the model artifact, prompt, schema, and worker version for each run. Review licenses for the intended use. Avoid putting sensitive prompts or responses into operational logs that have broader access than the source data.

Build a queue of identifiable jobs

Use a manifest with stable item identifiers and bounded request sizes. Application-level batch tracking remains necessary even when the serving runtime groups requests internally. Checkpoint accepted results so a restarted worker can resume without guessing which items finished.

Apply independent structural and semantic validation. Confirm that required fields and types are present, then test whether evidence actually supports the content. Bound retries, output length, and overall deadlines; repeatedly asking the same question is not a universal repair strategy.

Size the system through a pilot

Measure memory, elapsed time, accepted item count, and review effort with realistic inputs. Increase concurrency only when it improves useful throughput within the operating limits. Plan maintenance, rollback, and a named owner for unresolved work.

Read the local inference playbook for a complete example. Compare the frontier AI evaluation approach when considering a second route, and use the self-hosted architecture guide to document the wider operational boundary.

Frontier model processing should solve an observable task need rather than serve as an automatic destination for every uncertain output. Separate the difficulty of understanding an input from the consequence of acting on the answer. Some decisions still belong with an authorized human reviewer.

Define the task and its required evidence. Locating text, normalizing a field, and interpreting an agreement are different activities. Keep the output contract clear enough that reviewers can distinguish a missing answer from an unsupported one.

Establish quality before choosing a route

Compare candidate routes against the same representative evaluation set and acceptance rubric. Include incomplete, unusual, and difficult cases. A broad model benchmark does not establish performance on your specific process.

Use inspectable routing signals such as failed validation, missing required evidence, or a document class with demonstrated baseline limitations. Do not assume a model's self-reported confidence is a calibrated probability. Evaluate any threshold against labeled examples before relying on it.

Enforce the data boundary first

A restricted document must not leave its approved environment merely because a local route failed. Keep permission and destination rules separate from quality tuning. Check the complete payload, including context, attachments, and identifying metadata.

Normalize accepted outputs into a consistent application schema and retain route metadata. Record the model configuration, prompt version, reason for escalation, and validation outcome. A schema check establishes structure, not factual correctness.

Define fallback and cost limits

Fallback should preserve the acceptance standard. A temporary service failure, an unsupported answer, and missing source information call for different responses. Put a ceiling on attempts and keep an explicit review path when another model call cannot resolve the issue.

Use the frontier routing playbook to plan a controlled rollout. The AI credits guide connects routing choices to total cost per accepted result, while the local LLM guide helps define an internal alternative when the data policy requires one.

A business process API describes an agreement between a client and a workflow. Distinguish the business object from a processing run. The same package can have several runs as inputs or rules change, while each accepted run refers to one specific input set and process version.

Define the outcome the client can rely on. Accepted means that work has entered the system under its contract; it does not mean that every step has completed. Keep extraction, review, and publication separate when they represent different business permissions.

Make the lifecycle observable

Give a run a stable identity and a status representation with documented states. Keep item-level progress separate from the overall lifecycle. Define how partial results, cancellation, and unresolved errors affect the run's final disposition.

In the reference design used here, a submission returns an accepted response with a run identifier, and a status operation exposes its state. The job lifecycle reference explains the transitions. These are implementation patterns to adapt, not endpoints for a hosted ProcessAPI.com service.

Protect intent and access

Use an explicit idempotency contract to associate repeated submissions with the same intended operation. State the key scope, retention behavior, and response to conflicting request content. Protect downstream side effects independently; submission deduplication alone does not solve every repeat-execution problem.

Authorize access to each run and artifact, not only to the route name. A caller allowed to submit one account's work should not automatically read another account's outputs. Keep approval permissions distinct from worker execution rights when the process requires it.

Describe the contract before implementing it

Write schemas, example responses, error codes, and compatibility rules in a reviewable interface description. Keep the API version distinct from the version of the business process. A changed extraction rule can produce different results even when endpoint names remain the same.

The API design playbook develops the complete pattern. Start with the reference quickstart and sample files, then adapt authorization, persistence, and recovery to your own system. Clear documentation is part of the design, not evidence that the service has already been implemented.

Self-hosting gives a team responsibility for where and how a system runs. Define the boundary for source files, extracted text, model requests, results, logs, and backups. Include software updates, artifact downloads, monitoring, and any optional external fallback.

A statement that processing is local is not a complete network policy. Identify permitted destinations and who can authorize changes. Keep operational metadata separate from sensitive payloads when their access or retention requirements differ.

Match complexity to operational capability

Identify the roles your architecture needs: request handling, durable run state, storage, dispatch, and workers. Add a model service only where the workflow needs inference. Several roles may share infrastructure initially, but their responsibilities should remain clear.

Choose the simplest deployment that meets the accepted requirements and that the team can maintain. Record the tradeoffs and the conditions that would justify moving to a more complex architecture. A product name is not a replacement for an ownership plan.

Limit access and resource use

Scope each worker to assigned inputs and output destinations. Keep credentials outside source code and broad logs, and test rotation. Bound accepted file sizes, concurrent work, and resource use according to representative measurements.

Track queue age and incomplete items as well as service availability. A healthy process can still miss a business deadline if work waits indefinitely. Define the owner and the next action for unresolved tasks.

Restore the whole processing context

Back up the state and artifacts needed to explain accepted runs, and test restoration in isolation. Recovery may need to reconcile effects already completed in another system. A successful backup job does not prove that the workflow can resume safely.

Version application code, process rules, model artifacts, prompts, and schemas together where they affect results. Read the self-hosted deployment playbook for an operational review, then compare the processing cost framework using maintenance and review effort alongside infrastructure charges.

An AI credit is a provider-defined billing abstraction. Its meaning depends on the underlying rates, model, workload, and commercial terms. A balance cannot be translated into a reliable number of completed documents without a workload model and an acceptance rule.

Separate credits or currency from the usage that caused the charge. Preserve measured input, output, requests, media units, or compute time as relevant to the selected provider. Keep the applicable rate reference alongside the usage record.

Define the result you actually need

A submitted request, a generated response, and an accepted business result are different counts. Choose the outcome that matters to the receiving team and use the same acceptance standard across comparisons. Keep rejected and unresolved items visible.

Include baseline processing, escalation, retries, validation, storage, and review in the appropriate cost boundary. A low first-pass inference rate does not automatically produce a low total cost per useful result. Equally, a more capable route may be unnecessary for a task already handled reliably by simpler processing.

Control budgets before work is sent

Plan how the application reserves estimated usage, records actual consumption, and accounts for in-flight requests. A displayed balance may not include every request already dispatched. Define whether a limit pauses intake, postpones optional escalation, or moves work to a waiting state.

Do not silently discard an item because a budget is exhausted. Record why it is waiting and who can authorize the next step. Keep billing status separate from the business priority of the underlying work.

Use a pilot instead of a fictional package

The cost-per-successful-job playbook contains a fully labeled arithmetic example. Replace its assumptions with your own workload measurements and current provider terms. Inspect differences by input size, output size, retry rate, and acceptance rate.

For a comparison of rules-based, local-model, and hosted-model approaches, use the pricing and cost planning page. This resource explains cost design; ProcessAPI.com does not sell credit bundles or take payments. The next useful purchase decision begins with a measured workload, not an invented conversion promise.

ProcessAPI.com connects the decisions that often get discussed separately: how a business process works, how its inputs are prepared, where processing runs, and what an API should report when the work is incomplete. The site is a practical reading resource and implementation reference for developers, operations teams, technical founders, and process owners.

Start with the process rather than a product promise. A clear map, an accepted output contract, and a recoverable workflow are useful whether a team chooses a small script, a dedicated orchestration system, or a larger service architecture.

One connected set of topics

The core guides cover process mapping, process automation, batch images, batch files, local LLM batch processing, and frontier AI processing. Architecture pages connect those disciplines to business process API design, self-hosted operations, and cost planning.

The ten long-form articles in The Process Playbook develop concrete, illustrative examples. The reference docs provide a compact job lifecycle and sample manifests to adapt in your own implementation. Examples are labeled so a reader can distinguish a proposed design from the behavior of a third-party service.

What you can do here

Read guides, follow related topics, inspect example JSON, and download the reference files. There is no account requirement, form submission, or payment flow. This website publishes information; it does not operate a hosted process execution service or issue API credentials.

The pricing and credits material explains how to evaluate costs using explicit assumptions and real workload measurements. It does not present invented service plans or imply that a credit balance has a universal conversion into finished jobs.

How the content is organized

Each article focuses on a distinct question, includes practical tradeoffs, and links to a supporting primary source. Topic guides summarize the planning decisions and connect to deeper reading. Category and tag archives help readers follow a subject across process, architecture, and operations.

See the editorial standards for how examples, sources, and cost assumptions are handled. Send corrections or topic suggestions to info@processapi.com, with the relevant page and enough context to identify the issue.

Email info@processapi.com with the page title or address and the passage you are asking about. For a correction, describe what appears inaccurate and include a primary source when one is available. Precise context makes a technical issue easier to evaluate.

Suggestions are welcome across process mapping, automation, batch images and files, local model workloads, frontier AI evaluation, API design, and self-hosted operations. A concrete workflow or failure case is particularly useful when proposing a future guide.

What to include in a technical question

Describe the intended outcome, the stage where work becomes unclear, and any relevant constraints. Distinguish a proposed architecture from a system already in production. A small, synthetic example is usually more useful than a large collection of unstructured logs.

Do not send API keys, passwords, private customer records, confidential documents, or unrestricted production access. Remove identifying details from examples before sharing them. This mailbox is for editorial communication, not a secure intake channel for processing business data.

Looking for implementation material?

The reference documentation contains an example job contract, lifecycle, and batch manifest. The Process Playbook provides longer explanations of design choices and operational tradeoffs. For a subject overview, use the topic links in the navigation.

ProcessAPI.com publishes guides and examples rather than operating a hosted API service. Account, billing, or live-service issues for another provider should be directed to that provider's own support channel. No contact form or account sign-up is required to use this site.

Each playbook links to one supporting primary source in its body. Standards organizations, official documentation, and first-party engineering publications are preferred for descriptions of technologies and service behavior. The linked source supports the relevant discussion; it is not an endorsement of this website.

Implementation recommendations and fictional walkthroughs are identified as design examples. They are not descriptions of a named customer's deployment or claims that every system should make the same tradeoff. A team's requirements, data policy, and operational capability remain part of any implementation decision.

Examples are not performance promises

The site does not use fictional customer testimonials, processed-job totals, uptime measurements, compliance certifications, or vendor partnerships. Numbers in a worked example are labeled assumptions and are intended to explain a method, not to describe observed service performance.

The AI cost example separates input usage, output usage, retries, operational expenses, and accepted items. Its rates are illustrative rather than current provider prices. Readers should replace those assumptions with applicable terms and observed workload data before comparing purchasing options.

Reference code describes a proposed contract

Example JSON and interface patterns illustrate how a processing service could communicate accepted intent and outcomes. They do not establish a live endpoint, authentication service, or hosted runtime. Code samples should be reviewed and tested in an appropriate environment before adaptation.

The reference design deliberately distinguishes a run from a business object, delivery from side effects, and a valid schema from a correct model answer. Those boundaries help readers identify where additional requirements and tests belong.

Corrections and continued reading

Send a correction to info@processapi.com with the page, disputed statement, and a relevant source where available. Technical documentation and provider terms can change, so consult the linked primary material for the behavior of a particular version or service.

Use the topic guides to find an entry point and The Process Playbook to follow a complete walkthrough. Related article links are selected to connect decisions across process design, batch processing, architecture, and operations rather than to repeat the same article under multiple titles.

The pages, images, styles, scripts, and example files are served as static resources. The website code does not include analytics trackers, advertising scripts, account systems, contact forms, file-upload tools, or newsletter collection forms. It does not set application cookies or store browsing information in local storage.

The interactive features are limited to navigation, document-copy controls, and a decorative process graphic. Example processing requests shown in the documentation are text; browsing a page does not execute those requests or send your files to a model service. Images and styling assets are included with the site rather than loaded from a third-party image service.

Hosting and network requests

A browser must request website files from the configured hosting service. Server or infrastructure logging depends on the hosting configuration and may record request information such as an address, timestamp, and requested resource. This page does not claim that the hosting provider collects no information.

The included website code does not add an external font request, embedded social widget, or third-party analytics connection. Hosting-level additions and policies are separate from the static website files described here.

Email contact

Selecting an email link opens your email application. Sending a message shares the information you choose to include through your email service and the recipient's email infrastructure. Do not send passwords, API credentials, confidential business files, or sensitive personal records.

Contact info@processapi.com for a question about this website or an editorial correction. Include only the information needed to explain the issue. The mailbox is not a file-processing service or a secure support portal.

External reading and downloads

Articles link to official documentation and other primary sources. Following a link takes you to a separate website with its own practices. Those sources are provided for further reading and are not loaded automatically as part of the article.

Reference JSON files are static examples that you may inspect locally. They contain illustrative identifiers rather than active credentials. The About page explains the site's purpose, and the editorial standards explain how source material and examples are used.

Define what the receiving team considers complete: a validated image set, an accepted dataset, a classified document, or a published business record. Record the expected input volume, size distribution, delivery window, and exception policy. Keep the quality standard fixed across alternatives.

The three approaches above are planning categories, not ProcessAPI.com products or paid plans. There is no checkout on this site. Choose a path according to the work, the permitted data boundary, and the team's ability to operate it.

Include costs beyond the first request

For rules-based processing, include worker execution, storage, maintenance, validation, and repairs. For local inference, also include the model-serving environment, utilization assumptions, evaluation, updates, and operational ownership. For hosted AI, account for applicable usage units, escalations, retries, and provider-specific terms.

Human review belongs in the model when a result cannot be accepted automatically. Keep that effort separate initially so it is clear whether a change affects inference cost or downstream work. A cheaper request may not be a cheaper accepted outcome.

A transparent worked example

In a hypothetical batch of 10,000 documents, assume 800 input and 200 output tokens per first-pass request. At illustrative rates of $0.50 and $2.00 per million tokens respectively, first-pass inference totals $8.00. Adding ten percent for equivalent retry usage and $12.00 for other modeled operations gives $20.80.

If 9,500 items are accepted, that is approximately $2.19 per thousand accepted items. The example excludes human review and any other unmodeled charge. These are arithmetic assumptions, not actual provider rates or observed acceptance results. The full cost playbook shows how to replace each assumption with a pilot ledger.

Questions to settle before choosing a provider

Confirm the supported workload, data-handling terms, processing window, usage accounting, limits, and price that apply to your intended configuration. Determine how failed or canceled requests and in-flight work affect usage. Check what happens when a budget limit is reached.

Compare a representative pilot under the same acceptance rubric. Report accepted, rejected, and unresolved results alongside usage and review effort. Use a range when input complexity varies rather than presenting one precise number as a universal cost.

Next steps

Read Premium AI Credits to separate billing abstractions from application-level value. Use the self-hosted guide to identify ownership costs, or the frontier AI guide to examine escalation. A useful cost decision connects the invoice, the operating work, and the business outcome.

These pages describe a proposed processing API design. They are implementation references, not instructions for calling a hosted ProcessAPI.com service. There is no API-key signup, live request console, SDK package, or browser-based processing endpoint.

The example separates a process definition, an input manifest, a run, and a result manifest. That structure gives a team concrete objects to discuss before selecting persistence, dispatch, worker, or model-serving technologies.

Follow the contract from input to result

Begin with the quickstart to inspect a request and a status representation. Then review the job lifecycle to agree on state transitions and failure behavior. Finish with batch manifests to define what a complete delivery must account for.

All examples use the same process name and manifest identity so the relationship between files is easy to follow. The logical input and output paths stand for locations your own implementation would manage, not downloadable business documents on this website.

Reference files

Download the job request, input manifest, run status, or result manifest. The example notes summarize their relationship.

Adapt the patterns to your authorization boundary, process versions, data policy, and partial-success rules. Test both successful and interrupted cases before treating any design choice as an operational guarantee.

Go deeper into the architecture

The business process API playbook discusses identity, authorization, versioning, and errors. The webhook reliability playbook examines repeated delivery and external effects. The self-hosted guide connects the interface to the responsibility of operating it.

This reference example processes two documents under documents.intake.v1. The request selects a manifest rather than placing every input directly in the request body. The manifest fixes the expected input set so a run can reconcile its result to a stable collection.

There is no live endpoint behind these examples. In your own implementation, add authentication, authorization, durable acceptance, input validation, and an appropriate storage boundary before exposing a submission route.

{
  "process": "documents.intake.v1",
  "input_manifest": "manifest-001",
  "policy": {
    "validation": "required",
    "on_ambiguity": "human_review",
    "max_attempts": 3
  },
  "output": {
    "include_item_results": true,
    "publish_after_validation": true
  }
}

Download job-request.json. The max_attempts field is an example policy value, not a universal retry recommendation. Your workflow still needs to classify which failures are worth retrying and define its elapsed-time limit.

2. Accept the request durably

The proposed interface uses POST /v1/runs for submission and returns HTTP 202 Accepted only after the accepted intent is recorded. A response should include the stable run identity and the location where an authorized client can observe it. Acceptance is not completion.

Define an idempotency-key scope and retention policy. A repeated submission for the same accepted operation should recover the original run where the contract supports that behavior. Reusing the key with materially different content should produce an explicit conflict, not an unrelated run hidden behind the same identity.

3. Observe a run without changing it

The proposed status operation is GET /v1/runs/{run_id}. It returns the lifecycle state and a bounded summary of progress. Reading status must still enforce object-level authorization. An identifier is not permission to see another account's work.

{
  "run_id": "run-example-001",
  "process": "documents.intake.v1",
  "input_manifest": "manifest-001",
  "state": "succeeded",
  "progress": {
    "total": 2,
    "succeeded": 2,
    "failed": 0,
    "pending": 0
  },
  "result_manifest": "results-example-001"
}

This is an illustrative completed run, not live telemetry. Download run-status.json and compare its totals with the two items in batch-manifest.json.

4. Reconcile the result

A succeeded run has accepted outcomes for every required item under the manifest's delivery rule. The result_manifest connects the run to its per-item output references. Use item identifiers to join the result to the input set; do not assume completion order matches input order.

Download result-manifest.json. Its output paths are logical references that an implementation would resolve under its own access controls. A real design must define how artifacts are retrieved, how long they remain available, and who may publish them.

5. Test the interrupted journey

Repeat an accepted submission, stop a worker between processing and persistence, and delay a dependency's response after it accepts a request. For each test, define the expected business outcome and inspect both local state and external effects.

Continue with the job lifecycle and batch manifest references. The longer API design playbook explains the tradeoffs behind identity, authorization, errors, and compatibility.

The states below form the reference design used across the documentation. Your implementation may choose different names or additional states when its actual business requirements justify them. Do not copy a state without defining who can enter it and what evidence must exist.

Business state is not worker state

A package awaiting approval can be healthy even while no worker is active. A notification worker retry does not necessarily change the package's business state. Keep these concepts distinct so an operator can identify the actual owner of a delay.

The reference's awaiting_review state stores the exact input version and required decision rather than keeping a long-running worker asleep. If the input changes, the business policy must decide whether an earlier approval remains applicable. Never silently attach an old decision to a materially changed package.

Record transitions durably

Each transition should identify the run, previous state, new state, responsible actor or service, reason, and time. Use persistence and concurrency controls appropriate to the chosen architecture so competing workers cannot create contradictory accepted transitions.

A status endpoint should report accepted state, not whichever value a particular worker currently holds in memory. Keep detailed attempt history in related records while returning a bounded summary to ordinary status readers.

Make partial results explicit

A failed or canceled run may have produced useful artifacts. Preserve those item-level outcomes without claiming that the whole delivery succeeded. Whether a consumer may use them is a business acceptance decision, not a consequence of the files existing.

The example input manifest sets partial_delivery to false. Therefore, every required item must satisfy the acceptance rule before the run is declared succeeded. A different workflow can allow partial delivery, but it needs to state how incomplete items are communicated and repaired.

Cancellation cannot erase history

Define the point at which cancellation stops new work and what happens to requests already in flight. A downstream operation may have been accepted before the cancellation reached it. Reconcile that outcome rather than assuming the cancel request rolled back every effect.

Some business effects need a compensating process, such as a separate reversal request, rather than a technical retry or state edit. Keep that process visible and separately authorized. Do not make a cancel button promise behavior that the destination does not support.

Recovery preserves the accepted record

In this reference, terminal runs are not rewritten into a different historical outcome. A correction or operator-authorized repair creates a linked run with the corrected inputs or configuration. The original result remains available according to retention policy.

Temporary item retries can occur before the run becomes terminal, within its accepted attempt and deadline policy. See the webhook reliability playbook for repeated delivery and the batch manifest reference for item-level reconciliation.

A batch manifest fixes what a run expects to process. It gives each item a stable identity and identifies the process and policy used to interpret the input set. This makes missing, rejected, and completed work visible without relying on file ordering or directory contents that may change.

The example contains two logical input paths. They stand for files your own implementation would supply; they are not website downloads. A real deployment should resolve source references inside an authorized storage boundary rather than blindly fetching arbitrary user-supplied locations.

{
  "schema_version": "1.0",
  "manifest_id": "manifest-001",
  "process": "documents.intake.v1",
  "items": [
    {
      "item_id": "document-001",
      "source": "inputs/document-001.txt",
      "source_version": "1",
      "expected_output": "outputs/document-001.json"
    },
    {
      "item_id": "document-002",
      "source": "inputs/document-002.txt",
      "source_version": "1",
      "expected_output": "outputs/document-002.json"
    }
  ],
  "policy": {
    "validation": "required",
    "on_ambiguity": "human_review",
    "max_attempts": 3
  },
  "acceptance": {
    "partial_delivery": false
  }
}

Download batch-manifest.json. The schema and policy values are illustrative. Add the integrity, retention, and authorization fields required by your actual workload before implementation.

Distinguish identity from position

item_id identifies the business item inside the accepted input set. It should remain stable when workers finish in a different order. source_version identifies which revision was accepted; a corrected source should create a distinguishable input version.

Keep source checksums or equivalent integrity evidence when your storage and workflow requirements call for them. Do not insert a fabricated checksum merely to make an example look complete. Compute it from the actual bytes under a documented algorithm and verify it at the relevant boundary.

Record one disposition per expected item

Each required item needs a final disposition: accepted output, explicit rejection, or unresolved status under the chosen contract. Keep reason codes and source references with failures so an operator can repair them without guessing which input produced the issue.

A retry should preserve the item identity and record a new attempt. A revised input or changed process interpretation should create a new versioned operation. These distinctions let consumers compare results without mixing recovery attempts and genuinely new work.

Reconcile before declaring success

Join results to inputs by item_id. Check for missing results, duplicate accepted dispositions, and unexpected item identifiers. Count accepted, rejected, and pending items against the manifest total. Interpret those counts using the manifest's partial-delivery rule.

{
  "result_manifest_id": "results-example-001",
  "run_id": "run-example-001",
  "state": "succeeded",
  "items": [
    {
      "item_id": "document-001",
      "state": "succeeded",
      "output": "outputs/document-001.json"
    },
    {
      "item_id": "document-002",
      "state": "succeeded",
      "output": "outputs/document-002.json"
    }
  ]
}

Download result-manifest.json. In this example both required items succeed, so the related run status reports a succeeded run with a total of two accepted items.

Apply the pattern to different workloads

For images, each asset may require several named output variants. For files, one input can produce multiple accepted and rejected records. For model jobs, retain prompt, model, schema, and validation versions as appropriate to explain the result. Extend the manifest around the actual unit of acceptance.

Read the batch images guide, batch files guide, or local LLM guide for workload-specific decisions. The shared principle is simple: a completed batch accounts for the expected work, not merely the requests a worker happened to finish.