How to build a Permit Approval/Rejection Agent

TL;DR

This agent automates the review, compliance checking, and routing of permit applications for local governments, using AI to analyze submissions, reference code/fee documents, and send applicant notifications—dramatically reducing manual review time and errors.

What It Does:

• Ingests permit applications (via file upload and applicant input)

• Analyzes applications for completeness, compliance, and fee calculation using AI and knowledge base references

• Routes applications for acceptance or rejection, with clear staff-facing and applicant-facing outputs

• Sends automated emails to applicants with acceptance or rejection decisions and next steps

Who It’s For:

• Local government permitting departments

• Zoning and planning staff

• Municipalities seeking to streamline permit intake and review

• Any organization handling structured application review and compliance workflows

Time to Value:

• Less than one day to set up (just upload your code/fee docs and connect your email)

Output:

• Applicant-facing email: Clear acceptance or rejection with next steps

Common Pain Points for Approving Permit Applications

• Manual review is slow and error-prone

• Staff must cross-reference multiple code/fee documents

• Applicants submit incomplete or non-compliant applications

• Communication with applicants is inconsistent or delayed

• Staff spend time drafting repetitive emails and logs

What This Agent Delivers

• Automated completeness and compliance checks

• Instant fee calculation from uploaded schedules

• AI-generated, code-cited approval/denial drafts

• Consistent, formatted applicant and staff communications

• Automated email notifications for both acceptance and rejection

• Reduced staff workload and faster applicant turnaround

Step-by-Step Build (StackAI Nodes)

1) Input Node (in-0 — Name)

What it does:

• Collects applicant name and project details to start the process

Goal:

• Capture the initial data needed for review

2) Files Node (doc-0 — Application)

What it does:

• Lets users upload application files (plans, supporting docs)

• Extracts and processes text for AI review

Goal:

• Make all application materials available for automated analysis

3) LLM Node (llm-0 — Permitting Analyst AI)

What it does:

• Reviews application details and uploaded docs

• Checks for completeness, compliance, and calculates fees

• Cites code sections and drafts approval/denial with conditions

• Uses knowledge base files (zoning, fee schedule, templates) as references

Goal:

• Automate the expert review and decision-drafting process

Instructions

You are a Permitting Analyst for a U.S. local government. You understand zoning (e.g., R-2), building/parking standards, fee schedules, and intake workflows. Your job is to:

- Validate application completeness and list missing items.

- Cross-check project details against zoning/code snippets (setbacks, height, lot coverage, ADU limits, parking).

- Calculate fees from the fee schedule based on valuation and scope.

- Identify compliance risks and cite the exact section(s) from the code snippet file.

- Draft an approval or denial with conditions/corrections using the templates.

- Produce a concise applicant email and a staff-facing log entry.

Use the uploaded files as

Prompt

Prepare the approval and rejection responses for routing based on the provided application details.



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4) AI Routing Node (airouting-0 — AI Routing)

What it does:

• Classifies the application as “accepted” or “rejected” based on AI review output

Goal:

• Route the application to the correct next step (acceptance or rejection)

5) Template Nodes (template-2 and template-3 — Rejection/Acceptance Decision Templates)

What they do:

• Format the AI’s decision for staff and applicant visibility

• Ensure consistent, professional communication

Goal:

• Standardize outputs for both internal logs and applicant emails

6) Action Nodes (action-0 & action-1 — Send Email)

What they do:

• Send formatted acceptance or rejection emails to the applicant using Gmail

Goal:

• Instantly notify applicants of the decision and next steps

7) Output Node (out-0)

What it does:

• Presents the final formatted decision to the user (staff or applicant)

Goal:

• Provide a clear, consolidated result for review or record-keeping