AI Intake and Justification Agent
Source: Gemini Agent Response (Internal)
To combat the “novelty effect” and ensure AI spend is a calculated investment, an Intake & Justification Agent (built on Google’s ADK - Agent Development Kit) acts as an automated “Lead Auditor.”
1. Agentic vs. Traditional Decision Matrix
Section titled “1. Agentic vs. Traditional Decision Matrix”| Dimension | Traditional (RPA/Code) | AI Agent (LLM) |
|---|---|---|
| Logic | Deterministic (If X, then Y) | Probabilistic (Interpretation) |
| Data | Structured (CSV, SQL, API) | Unstructured (PDF, Image, Slang) |
| Errors | Zero-tolerance (Financials) | Context-dependent (Creative) |
| Cost | Fixed, low infra cost | Variable, high token cost |
2. ADK “Skill” Architecture
Section titled “2. ADK “Skill” Architecture”The agent uses specific skills to conduct “anti-AI” due diligence:
- Skill A (Context Miner): Consultative interview to identify the specific human task replaced and check if simple scripts (regex/Python) suffice.
- Skill B (Efficiency Calculator): Uses the formula $S = (H \times R) - (T \times C)$ to estimate savings.
- Skill C (Legacy Re-addresser): Checks Jira/GitHub to see if a solution already exists, suggesting a refactor over a new build.
3. The “Truth-Telling” Questions
Section titled “3. The “Truth-Telling” Questions”- The Overkill Check: Would an API or better UI solve this without an agent?
- The Latency Tolerance: Can the process handle a 15–30 second “thinking” delay?
- The Longevity Test: Is this a permanent need or a 24-month critical process?
- The Human-in-the-Loop: Who is the “Pilot” responsible for verifying the output?
4. Business Justification One-Pager
Section titled “4. Business Justification One-Pager”The agent outputs a recommendation (Traditional | Agentic | Refactor) with a projected burn vs. savings analysis, providing an auditable paper trail for the CIO.