Troubleshooting Missing Tool Calls in the Nova Sonic Model

#AWS#novasonic#speech#tutorial

When the model gathers parameters but never sends the tool call

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Problem

Developers using the Nova Sonic speech model are seeing inconsistent tool-calling behavior. In one developer’s tests, about 25% of the time the model collects all required parameters, signals it will “perform the task,” but never actually produces a toolUse event. The session stalls until the user nudges the model again.

Clarifying the Issue

This is not an issue with how the calling application handles tool execution. In the failing cases, the toolUse event is never generated by the LLM in the first place. That means there’s nothing for the client code to route or process.
The disconnect: some replies suggested adding another API call or fixing client-side handling, but the root issue lies with the model’s decision process around when to fire tool events.

Why It Matters

• Developers need deterministic tool-calling for production workflows.
• Missing events create poor user experience: the system appears “hung.”
• Without clarity, teams waste time debugging their own code instead of focusing on model configuration and behavior.

Key Terms

• toolUse event: JSON message emitted by the LLM to signal a tool call.
• toolChoice: Parameter that controls whether the model autonomously chooses tools (auto), always calls one (any), or locks to a specific tool.
• Temperature / top_p: Sampling controls that influence determinism.
• System prompt: Instruction block that guides conversation and tool-use rules.

Steps at a Glance

1. Verify that missing calls are truly absent, not just unhandled.
2. Switch from toolChoice=auto to a more explicit setting (any or a specific tool) for testing.
3. Reduce temperature closer to 0 to encourage deterministic tool-calling.
4. Strengthen the system prompt to instruct the model to immediately call tools once parameters are collected.
5. Monitor logs to confirm whether events are being skipped at generation time.

Detailed Steps

1. Confirm Event Absence
Check logs to ensure the model never emitted a toolUse event in failing cases. This rules out client-side handling issues.

// Expected successful case
{ "type": "toolUse", "name": "get_reward_eligibility", "parameters": { "accountId": "12345" } }

// Failing case
// No toolUse event appears after assistant says "Let me check..."

2. Adjust toolChoice
Change from auto to any in your request. This forces the model to commit to one of the declared tools each time:

"toolChoice": "any"

For debugging, you can also target a single tool explicitly:

"toolChoice": { "name": "get_reward_eligibility" }

Why this helps: auto gives the model more autonomy, which can introduce inconsistency. any forces a deterministic path toward tool use.

3. Reduce Sampling
Set temperature=0 and lower top_p if needed. This removes variance in decision-making:

"temperature": 0,
"top_p": 0.9

4. Improve System Prompt
Instruct Nova Sonic explicitly to call the tool immediately after parameters are gathered:

System: When you ask the user for tool parameters and they provide them, always generate a toolUse event right away before continuing the conversation.

5. Monitor and Compare
Run controlled tests with 50–100 calls. Track the rate of missing events under each configuration. Compare against baseline “auto” runs.

Conclusion

The problem isn’t in how your client code handles tool calls, but in the model’s behavior. Nova Sonic sometimes stalls in a “thinking” state instead of emitting the tool call. By tightening toolChoice, reducing randomness, and strengthening the system prompt, you can push the model toward more consistent behavior. If the skip rate remains high after these adjustments, that’s a candidate for an AWS support ticket—since it points to gaps in the speech model’s event generation logic rather than your integration.

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Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of The Rose Theory series on math and physics.