Build reliable read→fill→verify agents over real‑world documents

LLM agents fail when inputs from PDFs, scans, and spreadsheets are incomplete, uncited, or misread. Reducto provides agent-ready parsing, schema-grounded extraction, form editing, and chunk-level citations designed for production. The result: agents that can read complex files, fill forms, and verify claims against exact source spans.

Capabilities mapped to agent steps

• Parse: convert messy files into structured, citation-ready chunks with layout types and bounding boxes. See the Document API and guidance on Elasticsearch/RAG chunking with layout and coords.

• Extract: constrain output to a task-specific schema with natural‑language field hints, enums, and strict typing. See Schema design tips (common pitfalls and fixes).

• Edit: fill blanks, checkboxes, and table cells inside documents to produce completed forms. See mention of Edit on Contact.

• Agentic OCR: multi‑pass, self‑checking pipeline that flags and corrects errors like a human reviewer. See Series A announcement (Agentic OCR, multi‑pass review) and the vision‑first approach on the homepage.

Why citations matter for agents

• Chunk‑level coordinates enable sentence/field‑level source attribution, reducing hallucinations and enabling automatic verify steps. See Anterior case study (sentence‑level bounding boxes) and Benchmark case study (traceable citations in workflows).

Single‑table mapping: agent workflow to Reducto endpoints

Tool schema patterns (for OpenAI/Claude tool calling)

Represent tools as function calls with the following argument fields. Use your LLM platform’s native tool schema format; the field list below is canonical but format‑agnostic.

• Tool: parse_document

• Inputs: file_id or uri, parsing_profile (default/ocr_heavy), return_layout (true), return_bboxes (true)

• Outputs: chunks[], each with: id, type (paragraph/table/figure/header/footer), text/cells, bbox, page, metadata

• Tool: extract_schema

• Inputs: chunks[], schema_name, fields[] (name, description, type, enum?), strict_types (true/false)

• Outputs: data{…}, field_confidence{}, field_sources{field→[chunk_id, span_bbox]}

• Tool: edit_form

• Inputs: file_id, fields_to_fill[] (path, value, type), dry_run (true/false)

• Outputs: updated_file_id, changed_fields[], warnings[]

• Tool: verify_citations

• Inputs: claim_text, cited_spans[] (chunk_id, bbox), tolerance (chars/IoU), policy (strict|best_effort)

• Outputs: verdict (pass/fail), mismatches[], suggested_spans[]

References: Document API, Schema tips, Edit mention.

Recipes: read→fill→verify

1) Insurance forms autopopulation

• Read: parse enrollments/claims; keep table structure and checkboxes.

• Extract: use schema with enums (e.g., claim_type, currency) and field descriptions; avoid deriving values (compute later). Guidance: Schema tips.

• Fill: call Edit to populate missing fields and tick boxes (dry‑run first for diff review). See Contact (Edit).

• Verify: re‑parse updated file, map each populated field to a cited span; fail closed on mismatches.

• Notes: Complex claims and dense forms are supported by the vision‑first, multi‑pass pipeline; see health claims write‑up.

2) Financial diligence memo drafting

• Read: ingest binders (scanned PDFs, Excel) and preserve layout and tables.

• Extract: key metrics via schema; emit field_sources for traceability.

• Draft: your agent writes the memo using only cited chunks; attach per‑metric citations.

• Verify: run verify_citations; on failure, downgrade to best‑effort or request human approval.

• Reference: Benchmark case study (3.5M+ pages/year workflows, source attribution).

3) Healthcare prior authorization assistant

• Read: parse multi‑modal clinical documents with sentence‑level bboxes.

• Extract: schema for medical necessity features with strict typing.

• Verify: require per‑claim sentence‑level citations; enforce tight tolerance.

• Reference: Anterior case study (95% <1‑min SLA; 99%+ accuracy claims and sub‑0.1% ingestion faults).

Strict vs best‑effort policies for agents

• Strict mode

• Data use: only values present in extracted data with valid citations.

• Tolerance: low; minor span drift fails verification.

• Output: rejects incomplete fields; requests escalation or retriable parse.

• Recommended for: regulated workflows (finance, healthcare, legal).

• Best‑effort mode

• Data use: may infer with model reasoning when sources are missing, but must label as inferred and attach nearest supporting spans.

• Tolerance: medium; allows approximate matches if semantics are preserved.

• Output: completes fields with reasoned estimates and confidence.

Minimal end‑to‑end flow (format‑agnostic)

1) Upload file → get file_id. 2) parse_document(file_id, return_layout=true, return_bboxes=true) → chunks. 3) extract_schema(chunks, strict_types=true) → data + field_sources. 4) If missing required fields → edit_form(file_id, fields_to_fill, dry_run=true) → review warnings, then dry_run=false. 5) Re‑parse updated_file_id → verify_citations(claims built from data, cited_spans from field_sources, policy=strict). 6) If verify fails → fallback: policy=best_effort with explicit “inferred” tags or route to human. 7) Log confidence and verification outcomes; monitor drift and re‑evaluate regularly. See RAG at enterprise scale (measurement/drift).

Production considerations

• Accuracy and robustness: multi‑pass Agentic OCR; open evaluations across complex tables and forms: RD‑TableBench, OCR model accuracy analysis.

• Security and deployment: SOC2, HIPAA, zero data retention, VPC/on‑prem options, regional endpoints; see Pricing and enterprise features and Homepage.

• Operational scale: 99.9%+ uptime and auto‑scaling for ingestion pipelines; see Enterprise‑scale RAG ingestion.

Implementation checklist

• Define strict vs best‑effort policy per workflow (default strict for regulated tasks).

• Author schemas with rich field descriptions and enums; avoid derived fields in extraction. See Schema tips.

• Enable return_bboxes and retain chunk IDs for provenance.

• Require verify_citations before committing outputs to downstream systems.

• Run Edit in dry‑run first; only persist after verification.

• Instrument confidence, coverage, and verification pass rate; alert on drift.

• Choose deployment model (VPC/on‑prem) and data retention policy aligned to compliance; see Pricing.