One-line summary

A multi-step agent that ingests a document or ticket, extracts structured fields, cross-references authoritative data, drafts a resolution, and routes only the genuinely ambiguous cases (typically 8-20%) to a human reviewer with a full context package.

Agentic Document Processing and Triage

1. Intake and normalization

Accept documents (PDF, image, email, ticket), run OCR / layout parsing, strip PII for logging, and emit a canonical JSON envelope with raw_text, metadata, provenance.

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2. Classification and routing

Lightweight classifier (fine-tuned BERT or LLM with function calling) tags the item by type and complexity. Simple cases go to fast path, complex cases to deep path.

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3. Extraction agent

LLM with structured output (JSON schema) extracts entities - for KYC: passport fields, beneficial owners; for tickets: device ID, symptom, region; for real estate: parties, clauses, obligations.

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4. Retrieval and verification

Vector + keyword hybrid search over runbooks, past cases, sanctions lists, regulatory text. Deterministic API calls (OFAC, PEP, MLS, permit DB) cross-reference extracted entities.

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5. Reasoning and resolution

ReAct-style agent drafts a proposed action, a confidence score per claim, and an audit trail of retrieved evidence. Enforces a JSON decision schema per use case.

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6. Human-in-the-loop escalation

Cases below confidence threshold or flagged by policy go to a reviewer queue with full context: original doc, extracted fields, evidence, proposed action, and why it was escalated.

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7. Feedback and governance

Reviewer decisions feed back into evaluation sets; every run is logged with model version, prompt hash, retrieval IDs, and outcome for audit under NIST AI RMF / regulator review.

OCR and layout

Amazon Textract, Azure Document Intelligence, or open-source Docling + Tesseract

Layout-aware extraction beats raw OCR for forms and IDs.

Classification

Distilled BERT / DeBERTa or LLM function calling (GPT-4o mini, Claude Haiku, Llama 3.1 8B)

Small models are enough for the top 10-20 categories.

Extraction

Frontier LLM with JSON schema / tool use (GPT-4.1, Claude 3.7 Sonnet, Gemini 2.5)

Structured outputs eliminate brittle regex parsing.

Retrieval

Hybrid search: pgvector or OpenSearch + BM25; LlamaIndex or LangChain for orchestration

Dense-only retrieval under-recalls exact identifiers.

External APIs

OFAC/PEP feeds, Companies House, DMV, MLS, Snowflake internal data

Deterministic checks remain deterministic - do not delegate them to the LLM.

Agent framework

LangGraph, CrewAI, or a plain state machine with typed tools

Prefer explicit state machines over open-ended autonomy in regulated contexts.

Human-in-the-loop

Label Studio, Argilla, or a custom Streamlit / Retool queue

Reviewer UX is where most teams lose velocity - invest here.

Observability

Langfuse, Arize Phoenix, or OpenTelemetry + custom spans

Log prompt, retrieved chunks, tool calls, final output per run.

Governance

Evidently, Giskard, Fiddler; NIST AI RMF controls

Bias, drift, and adversarial robustness monitoring.

•     Modular - each stage (OCR, classify, extract, retrieve, decide) can be swapped without rewriting the pipeline.

•     Deterministic checks stay deterministic (sanctions, fraud rules), which preserves regulatory defensibility.

•     Confidence-driven escalation produces a clean economic story: humans only see the 8-20% that matter.

•     Audit trail is a first-class artifact, so it works in regulated verticals (banking, healthcare, public sector).

•     Feedback loop turns every reviewer decision into training / evaluation data at near-zero marginal cost.

•     Latency accumulates - 5-7 LLM calls per item can push p95 past 10 seconds without careful batching.

•     Cost grows linearly with volume; no free lunch at million-item scale without distillation.

•     Hard to debug when an agent "confidently produces a resolution that was correct for the category it had inferred, but wrong because the category inference was wrong" (telco article).

•     Context-starvation failures dominate - upstream intake quality matters more than model choice.

•     Regulatory posture varies by jurisdiction; the same agent may need different guardrails per region.

KYC onboarding time

28-35 days

3 days

Banking article

KYC unit cost

$10-$14 per file

$1.10-$1.80 per file

Banking article

Telecom Tier-1 simple ticket resolution

~90% (human first-pass)

91% agent-autonomous

Telecom article

Telecom Tier-1 complex ticket resolution

~63% (human first-pass)

63% agent-autonomous

Telecom article

NOC per-ticket cost

$6.40

$0.18

Telecom article / Gartner AIOps

NOC staffing reduction

0% (baseline)

30-40% within 24 months

Gartner AIOps

Real-estate DD review time

100% (baseline)

30-40% (60-70% faster)

Real-estate article

Fraud detection rate

1x (rules-based)

3-4x with fewer false positives

HMRC / government article