The Problem: The most dangerous error in AI integration is the “Silent Failure.” The HTTP Connector receives a 200 OK status code, so Boomi’s standard Try/Catch assumes success. However, the JSON payload might contain hallucinated data, cut-off sentences, or an apology (“I’m sorry, I cannot generate that…”).

The Problem: AI APIs charge by the “token” (roughly 4 characters). A runaway process loop or a massive document could cost hundreds of dollars in minutes. Boomi does not track this natively.

The Solution: You must act as the meter. Extract the usage.total_tokens field from every AI response and aggregate it into a persisted counter.

The Problem: Every model has a hard memory limit (Context Window), e.g., 8,000 or 128,000 tokens. If you send a conversation history that exceeds this, the API throws a 400 Bad Request error, crashing your process.

The Solution: The “Rolling Window” pattern. Before calling the API, measure your payload. If it’s too large, trim the oldest user messages while strictly preserving the System Prompt (your instructions).

// Groovy Script for Data Process Shape
import groovy.json.JsonSlurper
import groovy.json.JsonOutput

// Mock token count (approx 4 chars = 1 token)
def countTokens(text) { return text.length() / 4 }
def MAX_TOKENS = 8000

for( int i = 0; i < dataContext.getDataCount(); i++ ) {
    InputStream is = dataContext.getStream(i);
    def json = new JsonSlurper().parse(is)
    
    // While total tokens > limit, remove index 1 
    // (Index 0 is usually System Prompt, so we remove the oldest user message)
    while (countTokens(JsonOutput.toJson(json.messages)) > MAX_TOKENS) {
        if(json.messages.size() > 1) {
            json.messages.remove(1) 
        } else {
            break // Safety break
        }
    }
    
    // ... store updated JSON back to stream
}

The Problem: AI providers frequently hit Rate Limits (HTTP 429). If you use a standard retry loop that retries immediately, you will simply be blocked faster and for longer.

The Solution: Implement “Exponential Backoff.” Wait 2 seconds, then 4 seconds, then 8 seconds. This gives the API provider time to recover.

flowchart LR
Start((Start)) --> Call[Call AI Connector]
Call --> Check{Status Check}
Check -->|200 OK| Success((Success))
Check -->|429 Rate Limit| RetryCheck{Retry Count OK}
RetryCheck -->|Yes| Wait[Wait Shape]
Wait --> Backoff{Calculate Delay}
Backoff -->|2s 4s 8s| Call
RetryCheck -->|No| Fail((Final Error))
        

The Problem: If OpenAI is down, retrying 10,000 incoming documents individually is a waste of resources. It floods your logs and delays other processes.

The Solution: Use a persistent property to track the “Health” of the integration. If it fails 5 times in a row, “Trip” the circuit.

The Problem: Your primary model (e.g., GPT-4) is the smartest, but also the slowest and most prone to timeouts during peak hours.

The Solution: Implement a “Waterfall” routing logic. If the Gold model fails, downgrade to Silver, then Bronze.

flowchart TD
Start --> Primary[Attempt GPT-4]
Primary -->|Success| End((End))
Primary -->|Timeout or Error| Secondary[Attempt GPT-3.5-Turbo]
Secondary -->|Success| End
Secondary -->|Error| Cache[Return Cached Response]
Cache --> End
        

The Problem: Hardcoding prompts (e.g., “Summarize this text”) inside the connector configuration makes you rigid. To change the prompt, you must redeploy the entire process.

The Solution: Use a Boomi Cross Reference Table (CRT) as a content management system.

By querying this table for Active=true, you can switch prompt strategies instantly without touching the canvas.

Combining all strategies allows you to build a self-healing AI pipeline. The flow validates inputs, manages costs, handles errors intelligently, and degrades gracefully.

flowchart TD
Start --> CircuitCheck{Circuit Open}
CircuitCheck -->|Yes| Default[Return Safe Default]
CircuitCheck -->|No| FetchPrompt[CRT Fetch Active Prompt]
FetchPrompt --> BudgetCheck{Budget Limit OK}
BudgetCheck -->|No| Alert[Stop and Alert]
BudgetCheck -->|Yes| ContextTrim[Script Trim Context]
ContextTrim --> TryCatch[Try Catch Block]

subgraph "The AI Core"
TryCatch --> GPT4[Connector Primary Model]
GPT4 --> ValidatePrimary{Biz Rule Valid JSON}
ValidatePrimary -->|No| Fallback[Connector Fallback Model]
Fallback --> ValidateSecondary{Biz Rule Valid JSON}
end

ValidatePrimary -->|Yes| Success[Log Metrics and Finish]
ValidateSecondary -->|Yes| Success
ValidateSecondary -->|No| Default

TryCatch -->|Error| RetryLogic{Retry Count OK}
RetryLogic -->|Yes| Wait[Wait Shape Backoff]
Wait --> GPT4
RetryLogic -->|No| TripCircuit[Set Circuit Open]
TripCircuit --> Default
        

Building an AI Gateway isn’t just about technical error handling; it is about transforming a probabilistic experiment into a deterministic business process.