Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 8 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Set up a new Google ADK project. Initialize your agent to use Gemini 2.5 Pro. Define a basic `welcome` intent to confirm the agent is running. Ensure you have the necessary Google Cloud credentials configured. ```python from google.generativeai.vertexai import agents_v1beta as agents import vertexai vertexai.init(project='your-gcp-project', location='us-central1') # Initialize your agent with Gemini 2.5 Pro # ... ```
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Real-time Voice-Enabled Enterprise Intelligence Agent
Develop a real-time, voice-enabled enterprise intelligence agent using Google's Agent Development Kit (ADK) with Gemini 2.5 Pro. Inspired by the growing need for intelligent agents to interface with enterprise data platforms like Snowflake, this challenge focuses on creating an assistant that can answer complex business queries, summarize dashboards, and provide insights through a natural voice interface. The agent will leverage Gemini's multimodal capabilities to process voice commands and generate conversational responses, while using custom tools to query and interact with a simulated or actual Snowflake data warehouse. The goal is to deliver immediate, accurate information to users via voice, streamlining access to critical business intelligence and mimicking the functionality of an advanced AI assistant within messaging or communication platforms.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.