Agent in a Box

Autonomous R&D Tax Credit Compliance Agent

finance
1

Autonomous R&D Tax Credit Compliance Agent: Automate Section 174 Documentation

Problem Statement

Startups and mid-market engineering firms lose hundreds of thousands of dollars annually because they fail to capture "qualified research expenses" (QREs) in real-time. The traditional process for claiming R&D tax credits is reactive, manual, and prone to audit risk. Typically, a firm hires a consultant at the end of the fiscal year who conducts "recollection interviews" with engineers. This leads to two major issues: Under-claiming, where engineers forget smaller technical pivots or R&D spikes that occurred 10 months prior; and Compliance Risk, where documentation is reconstructed after the fact rather than being contemporaneous.

For a Series B startup with 40 engineers, the manual effort to track time against IRS Section 174 requirements is a significant productivity drain. Engineers hate manual time-tracking, leading to low-quality data. Simultaneously, IRS auditors have increased scrutiny on the "Nexus" between specific technical challenges and the wages claimed. Without a direct link between a GitHub PR, a Jira ticket, and a specific "uncertainty" being resolved, the credit is vulnerable. The industry needs an AI agent for tax compliance that lives within the development workflow, silently categorizing technical uncertainty and experimental activity as it happens, ensuring that every dollar of qualified labor is backed by a contemporaneous "technical narrative" without requiring engineers to fill out spreadsheets.

What the Agent Does

  • Does: Monitors GitHub/GitLab commits and Jira/Linear tickets to identify "technical uncertainty."
  • Does: Categorizes activities into the IRS Four-Part Test (Permissible Purpose, Elimination of Uncertainty, Process of Experimentation, Technological in Nature).
  • Does: Drafts technical justifications for each epic or sprint based on actual code changes.
  • Doesn't: File the actual tax return (it prepares the supporting "Contemporaneous Documentation Report").
  • Doesn't: Access sensitive codebase content (it only analyzes metadata, PR descriptions, and diff summaries).

Workflow

  1. Ingestion: Agent connects to Jira/Linear and GitHub. It pulls PR descriptions, commit messages, and ticket comments for the week.
  2. Classification: Using the "Four-Part Test" logic, the agent filters out "Routine Maintenance" and "Style Changes" from "Experimental Development."
  3. Narrative Generation: For identified R&D clusters, the agent generates a technical narrative explaining the "uncertainty" (e.g., "Optimizing latency in distributed KV store") and the "experimentation" (e.g., "Testing three different sharding logic approaches"). Similar to how an Automated API Documentation & SDK Generator Agent parses code for docs, this agent parses code for compliance.
  4. Wage Mapping: The agent cross-references the GitHub contributors with the payroll roster (via CSV or Rippling API) to allocate hours to the specific project.
  5. Human-in-the-loop (HITL) Review: A monthly summary is sent to the CTO/VPE to "Confirm" or "Reject" the R&D classification of specific epics.
  6. Audit Trail Archiving: Finalized narratives and linked PR IDs are exported to a secure, timestamped PDF/S3 bucket for audit defense.

Tool Stack

  • Orchestration: LangGraph or Make.com
  • LLM: Anthropic Claude 3.5 Sonnet (Superior for technical documentation and nuance)
  • Data Ingestion: GitHub API, Linear API
  • Payroll Integration: Finch (Universal API for payroll/HRIS)
  • Storage: Pinecone (for tracking technical context over time)
  • Pricing: ~$150-$300/mo (API usage dependent on engineering team size)

Prompt Skeletons

### Prompt 1: R&D Eligibility Classifier (The Four-Part Test)
System: You are a Senior Tax Tech Specialist familiar with IRS Section 41 and 174.
Input: {jira_ticket_description}, {github_pr_diff_summary}
Task: Evaluate if this work meets the Four-Part Test:
1. Specific Purpose: Is it for a new or improved function, performance, or reliability?
2. Elimination of Uncertainty: Did the team know the result or method at the outset?
3. Process of Experimentation: Did they evaluate alternatives/hypotheses?
4. Technological in Nature: Does it rely on CS, Engineering, or Physics?

Output: Return a JSON object with "is_eligible": boolean, "confidence_score": 0-1, and "reasoning_brief".

### Prompt 2: Technical Narrative Architect
System: You are a Technical Writer. Your goal is to convert developer jargon into audit-ready R&D narratives.
Input: {eligible_work_clusters}
Constraint: Avoid marketing language. Focus on technical hurdles. Use terms like "Iterative testing," "System architecture constraints," and "Technical uncertainty regarding [X]."
Task: Write a 200-word technical justification for this project cluster that links the specific PRs to the broader technical objective of the company.

Success Metrics

  • Found Credits: Increase in identified QREs by >20% compared to manual year-end reviews.
  • Audit Preparedness: 100% of claimed hours linked to a specific technical narrative and PR ID.
  • Developer Friction: Zero manual time-tracking hours required from the engineering team.

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