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 16 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.
Using Google ADK, define the initial agent structure. This should include an agent that can access current location, retrieve map data, and speak. Provide the Python code for initializing the `adk.Agent` and registering a simple `get_current_location` tool and a `speak` tool. Assume you have already set up your Google Cloud project and authenticated.
```python
import adk
from adk import agent_builder
from google.cloud import texttospeech_v1 as tts
def get_current_location():
# Placeholder for actual location retrieval
return {"latitude": 34.0195, "longitude": -118.4912}
def speak(text: str):
# Placeholder for TTS output
print(f"Agent says: {text}")
builder = agent_builder.AgentBuilder()
builder.add_tool(get_current_location)
builder.add_tool(speak)
# ... continue to build the rest of the agent
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.
Gemini-powered Voice Navigator Agent
Develop a hands-free, multimodal conversational agent using Google's Agent Development Kit (ADK) that integrates with Google Maps for real-time navigational assistance. The agent should leverage Gemini's multimodal capabilities to understand voice commands, provide spoken directions, and offer context-aware information based on the user's location and activity (e.g., walking, cycling). This challenge focuses on building robust, real-time voice interfaces that seamlessly integrate generative AI with location-based services, prioritizing safety and natural interaction.
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.