The AI reads.
The human approves.
A single agent owns one stage of the pipeline end-to-end: fetches signed documents, parses the authoritative source record, reconciles client intake against it, populates the correct forms, drafts the client email, and hands the deal to a human for final review. Never submits on its own.
The high-value stage nobody wants to staff.
In every pipeline-heavy services firm we've worked with, there is one stage where operator time collapses. It's always the same shape: a client has signed the contract, the case data has to be verified against a system of record, and forms have to be prepared accurately before anything is filed upstream. Senior operators do this badly because they're bored; juniors do it badly because they don't know the edge cases.
The framework puts a named agent on that stage. It runs daily, it uses the same source-of-truth documents a human would, and it surfaces every discrepancy it finds rather than papering over them. The output is a submission package sitting on the operator's desk — with a note explaining every choice.
The agent never hides uncertainty. If the intake record says "X" and the source document says "not X," the agent flags it in notes — it does not pick. A human decides.
W9 — AI Document Monitor, 11 steps.
Triggered on a daily schedule and on e-signing webhooks. Every step is idempotent — the agent can crash and re-run any day without double-processing. Steps 9–11 are the handoff: task + notification + auto-move.
Document Monitor + Form Population
Daily agent loop. Owns one pipeline stage end-to-end.
- 01AI
Query e-signing API for all documents in status COMPLETED for this deal (required set: contract + authorization).
- 02IF/THEN
Both documents complete? NO → skip, check again next cycle. YES → continue.
- 03AI
Download signed PDFs, upload to the client CONTACT record (not the deal — signed originals belong to the contact, not the case).
- 04SET
e_sign_status = Completed, authorization_received = Yes, contract_signed = Yes.
- 05AI
Retrieve the authoritative source document (code sheet / decision letter / system-of-record PDF) from the deal attachments.
- 06AI
Parse source document: extract conditions/items with statuses and dates using a structured-output prompt.
- 07AI
Compare extracted source data against the conditions_list on the Contact record. Find mismatches and undisclosed items.
- 08IF/THEN
Any mismatches? YES → write precise discrepancy notes to the CONTACT record ("Item X — NEW per client; Denied per source, date: 2024-03-15"). NO → note "clean reconciliation."
- 09AI
Populate the correct form per item status: NEW → form A, DENIED → form B, intent → form C. Only selected sections are filled. Forms placed on the DEAL record.
- 10SET
conditions_verified = Yes, forms_populated = Yes, write conditions + form-selection rationale to DEAL notes.
- 11TASK
Create Task assigned to the deal owner: "Review AI-generated forms for [client]" with priority = HIGH.
- 12WEBHOOK
Google Chat alert to the operator channel with a deep link to the deal.
- 13AI
Draft a client-facing submission-update email in the operator's voice, attached to the deal as a draft (never auto-sent).
- 14ROUTE
Move DEAL to Pending Submission stage — the human-review column.
- 15EXIT
Deal is on the operator's desk. 2-day rotting timer starts. Operator approves / revises / submits.
Six first-class capabilities.
Every block is a separate, testable agent capability. We build them one at a time so each ships value before the next one starts.
E-sign monitoring
Daily check for COMPLETED status on required documents. Auto-transfer of signed PDFs to the right record in the CRM. Eliminates the operator's morning ritual of checking the signing platform.
Source-document parsing
Structured extraction from authoritative source PDFs (code sheets, decision letters, benefit letters). Conditions, statuses, dates — everything downstream depends on this.
Reconciliation + mismatch flagging
Compares client-reported intake against source documents. Surfaces every disagreement ("client says new, source says previously decided") in Contact notes. Never silently picks.
Form population
Correct form per item status (new / denied / intent). PDF form-field mapping. Only relevant sections are filled. Forms land on the deal ready for review.
Email drafting
Drafts client-facing submission updates, exam prep emails with condition-specific video links, status updates. All drafts sit in the deal for operator approval — nothing sends on its own.
Operator handoff
Task created, Google Chat alert fired, deal moved to the human-review column. The agent's output is a three-line task: "Review forms, review email, upload." Nothing more.
What the operator used to do vs. what they do now.
Same throughput. Different bottleneck. The operator's judgment moves from "did the client sign?" to "is the AI right about the mismatch?" — a higher-leverage question.
Operator-owned stage
- Log into e-signing platform every morning, check every active client for COMPLETED status
- Download signed PDFs, manually upload to the CRM against the right record
- Open the code sheet PDF, read it line by line, note conditions and statuses
- Cross-reference against the client-reported intake in the spreadsheet
- Open the correct form template, re-key every field, save as the client name
- Draft the client email from scratch
- Move the card, notify team in chat
Agent-owned stage
- AI detects COMPLETED overnight, signed PDFs already on the right record in the morning
- AI has already read the source document and flagged mismatches on the contact
- Correct form is populated and attached to the deal
- Client email drafted in the operator's voice, sitting in the deal
- Card is in the "Review" column with a task and a Chat ping
- Operator reads the mismatch note, reviews the forms, approves, submits
Idempotent, queue-backed, observable.
The agent runs on AWS with a LangGraph orchestration layer. Every step is idempotent — if a daily run crashes at step 7, tomorrow's run picks up without duplicating work. Dead-letter queue catches every failure with the deal id, prompt, model response, and stack trace so engineering can triage without interrupting operations.
Observability is first-class: every agent decision writes a structured event that flows into the same dashboards the PM uses to track human work. When the agent gets a reconciliation wrong, we can replay the exact prompt and response three months later.
The agent does not submit upstream. Ever. The only stage it can move a deal to is "awaiting operator review." That is the framework's one non-negotiable.
Built on the stack we hold partner status in.
AWS Select Consulting Partner · Anthropic-aligned for Claude deployments · HubSpot Solutions Partner · Salesforce Consulting Partner · Google Cloud · Microsoft Azure.
The AI-owned stage, running in 8 weeks.
If your operators spend their days reading PDFs and re-keying forms, this is the framework. Scoped, built, and handed off in a single phase — with observability and reliability baked in.