Build a New AI Agent Project with Olakai

This skill guides you through creating a new AI agent that is fully integrated with Olakai for analytics, KPI tracking, and governance.

Prerequisites

Before starting, ensure:

1. Olakai CLI installed: npm install -g olakai-cli
2. CLI authenticated: olakai login
3. API key for SDK (generated per-agent via CLI, see Step 2.2)

Why Custom KPIs Are Essential

Olakai's core value is tracking business-specific KPIs for your AI agents. Without KPIs, you're tracking events without gaining actionable insights.

What you can measure with KPIs:

• Business outcomes (items processed, success rates, revenue impact)
• Operational data (step counts, retry rates, execution time)
• Quality indicators (error rates, user satisfaction signals)

Without KPIs configured:

• No dashboard KPIs beyond basic token counts
• No aggregated performance views
• No alerting thresholds
• No ROI calculations

Every agent should have 2-4 KPIs that answer: "How do I know this agent is performing well?"

KPIs created here belong to this specific agent only. If you later create additional agents, each one needs its own KPI definitions, KPIs cannot be shared or reused across agents.

Understanding the customData to KPI Pipeline

Before diving into implementation, understand how data flows through Olakai:

SDK customData → CustomDataConfig (Schema) → Context Variable → KPI Formula → kpiData

How It Works

1. customData (SDK): Raw JSON you send with each event
2. CustomDataConfig (Platform): Schema defining which fields are processed
3. Context Variables: CustomDataConfig fields become available for formulas
4. KPI Formula: Expression that computes a value (e.g., SuccessRate * 100)
5. kpiData (Response): Computed KPI values returned with each event

Critical Rules