# Agentic AI Qualification Framework

> Four-parameter qualification model for selecting high-value Agentic AI use cases

- **Category**: ai-adoption-guiding-principles

- **Canonical URL**: https://designpattern.fyi/ai-adoption-guiding-principles/part-iii-agentic-qualification-framework/

---

## Description
Four-parameter qualification model for selecting high-value Agentic AI use cases








## Additional Notes

# PART III: THE AGENTIC AI QUALIFICATION FRAMEWORK

## The Agentic AI Use Case Qualification Framework

Moving from standard Generative AI to Agentic AI changes the selection process entirely. You are no longer asking *"What information can this model look up?"* — you are asking *"What workflow can this system manage autonomously?"*

### The Four-Parameter Qualification Model

An ideal Agentic AI use case should require high scores in **Orchestration** and **Reasoning**, while maintaining a manageable **Risk Profile**. Run every candidate use case through all four parameters before committing to development.

```
   [ Use Case Idea ]
          │
          ▼
┌────────────────────────────────────────────────────────┐
│ 1. Orchestration Depth — Crosses multiple systems/APIs? │
├────────────────────────────────────────────────────────┤
│ 2. Non-Linear Reasoning — Handles edge cases & retries? │
├────────────────────────────────────────────────────────┤
│ 3. Data Entropy — Deals with messy, unstructured inputs?│
├────────────────────────────────────────────────────────┤
│ 4. Governance & Risk — Clear Human-in-the-Loop gates?   │
└────────────────────────────────────────────────────────┘
          │
          ▼
   [ GO / NO-GO Decision ]
```

| Parameter | The Test Question | Low Score → Alternative | High Score → Agentic AI |
|---|---|---|---|
| **1. Orchestration Depth** | Does the task require reading from a database, invoking an external API, updating a core system, and sending a confirmation in sequence? | Single-interface text task → Standard GenAI or basic RAG | Multi-system execution (EHR + Payer API + EHR write + messaging) → Agentic |
| **2. Non-Linear Reasoning** | Does the process require semantic judgment when a tool fails, information is missing, or an exception is encountered? | Rigid If/Then script → RPA | Adaptive exception handling (missing lab → find alternative, flight full → partner airline) → Agentic |
| **3. Data Entropy** | Is the core data trapped in unstructured formats — clinical notes, complex schedules, handwritten forms, multi-format documents? | Clean, structured database input → ML model or standard ETL | High-variability unstructured input (clinical charts, insurance schedules, incident narratives) → Agentic |
| **4. Risk Profile & Governance** | What is the blast radius if the agent makes an error? Are clear HITL handoff criteria defined before build? | High-risk, no HITL design → Do not automate until governance is defined | Defined HITL gates, clear escalation triggers, bounded action scope → Agentic with governance layer |

### Parameter Deep Dive

#### Parameter 1: Orchestration Depth — System & Tool Integration

Agentic AI earns its keep when it must orchestrate actions across multiple legacy environments in a single coherent workflow. The integration complexity is where agents provide asymmetric value relative to human operators, who must context-switch between systems manually.

**The Rule:** The more systems a workflow requires, and the more state-dependent the sequence of calls, the stronger the case for an agent

**The Test:** Does the task require reading from a database, invoking an external API, updating a core system, and sending a confirmation? If it only requires reading and writing text within one interface, standard GenAI or a basic RAG setup is sufficient

#### Parameter 2: Non-Linear Reasoning & Exception Handling

Healthcare, airlines, and insurance are defined by exception cases. The 80% standard workflow is manageable by RPA. The 20% of exceptions — missing data, failed API calls, conflicting information, ambiguous edge cases — is where enormous human time is consumed and where Agentic AI provides transformative value.

**The Rule:** If a workflow follows a rigid "If X, then Y" script with no exceptions, use traditional RPA. Agents are over-engineered for deterministic processes

**The Test:** Does the process require semantic judgment when a tool fails or when information is missing? Agentic AI excels when it has to figure out an alternative path — like realising a passenger's connection is impossible and calculating three alternative routes based on real-time crew availability

#### Parameter 3: Data Entropy — Handling Chaos

The most expensive and time-consuming part of insurance underwriting, clinical prior auth, and airline irregular operations is not the decision itself — it is assembling the information needed to make the decision from disparate, unstructured sources.

**The Rule:** High variability in input formats defeats traditional automation but is where agents thrive

**The Test:** Is the core data trapped in unstructured clinical charts, complex commercial insurance schedules, or disparate flight status updates? Agents can interpret intent, normalise the data, and make context-aware decisions based on it

#### Parameter 4: Risk Profile & Governance Complexity

The single most common cause of failed Agentic AI deployments in regulated industries is insufficient governance design. Risk profile definition is not a post-build activity — it is a prerequisite for architectural decisions, tool access scoping, and HITL gate placement.

**The Rule:** High autonomy requires strict boundaries. You must design explicit Human-in-the-Loop handoff parameters before a single line of agent code is written

**The Test:** What is the blast radius if the agent makes an error? High-risk actions must be scoped as *"Agent proposes, Human disposes."* Low-risk tasks can run on autopilot

### Cross-Domain Application Matrix

| Domain | Low Fit for Agentic AI *(Better for GenAI or RPA)* | High Fit for Agentic AI *(Autonomous Multi-Step)* | Core Agentic Behaviour *(Tools & Reasoning Used)* |
|---|---|---|---|
| **Healthcare** | Summarising a medical chart; generating a patient discharge template. | Autonomous Care Handoff & Prior Authorisation | Parses clinical notes, cross-references payer rules via API, identifies missing lab values, queries the EHR to pull them, and compiles the submission package. |
| **Airlines** | Answering "What is my baggage allowance?"; drafting a flight delay email. | Dynamic Mass Disruption Re-accommodation | Tracks cancellations, queries PNR, checks real-time seat inventory, reserves alternative flights, triggers hotel voucher APIs, and updates loyalty profiles sequentially. |
| **Insurance** | Generating a standard quote text; extracting fields from a clean PDF claim form. | Commercial Underwriting Support & FNOL Triage | Ingests complex commercial property schedules, validates risk data against external weather/geospatial systems, flags exposure thresholds, and drafts alternative policy terms for underwriter review. |