voice ai reliability auditor
Audit voice AI agents for latency, turn-taking logic, and side-effect reliability to ensure production readiness.
$5
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsvoice ai reliability auditor
Example session with this skill installed
Audit this trace: User said 'Book it' at 10.5s. STT finished at 11.2s. Tool call 'create_booking' fired at 11.5s but timed out. Agent confirmed 'All set!' at 12.0s. Why did this fail?
- Read your context and instructions
- Compiled the voice ai reliability
The audit identifies a critical state-consistency failure. The agent provided an affirmative confirmation ('All set!') before the side-effecting tool ('create_booking') reached an authoritative state.
Voice Reliability Audit Report
| Metric / Layer | Timestamp | Delta | Status |
|---|---|---|---|
| User Utterance | 10.5s | 0.0s | Baseline |
| STT Latency | 11.2s | 0.7s | Acceptable |
| Tool Execution | 11.5s | 0.3s | FAIL (Timeout) |
| TTS Start | 12.0s | 0.5s | Premature |
| Total Turn Latency | 1.5s | -- | Critical Logic Error |
Root Cause Analysis
Race Condition: The conversation policy lacks a dependency check. It allows the LLM to generate a confirmation response while the tool call is still in a pending or timeout state.
2.
Missing Idempotency: The system does not specify how to handle the create_booking timeout. If the user retries, a duplicate record may be created if the first call eventually succeeds.
Minimal Repairs
Logic Gate: Implement a requirement that the create_booking tool must return a 200 OK before the LLM is permitted to use the confirmation_policy prompt.
Error Handling: Define a "waiting" or "failure" utterance (e.g., "One moment while I check that...") for tool timeouts exceeding 2 seconds.
Golden-Call Verification
- Test Case 1 (Success): Verify confirmation only fires after tool success.
- Test Case 2 (Timeout): Verify the agent asks for a moment instead of confirming a failed booking.
- Test Case 3 (Retry): Ensure
booking_idorclient_refis used to prevent duplicates on re-entry.
Next steps
- Wrap the
create_bookingfunction in a retry block with a unique idempotency key. - Update the agent prompt to prevent optimistic confirmations.
- Rerun the trace test with a simulated 3000ms tool delay.
Connects securely to your tools. The creator never sees your data.
About this skill
Voice AI agents often suffer from high latency, broken barge-in logic, and duplicate tool execution that kills production reliability. This skill audits your voice stack to identify why your agent is lagging or misfiring during live calls.
What it does
- Latency profiling builds a complete timeline from user speech through STT, LLM reasoning, and TTS playback.
- Turn-taking analysis evaluates barge-in behavior and speech endpointing to fix awkward interruptions.
- Idempotency auditing checks side-effecting tools to prevent duplicate bookings or double-billing during timeouts.
- State verification ensures spoken confirmations only occur after the backend state is successfully reconciled.
- Root cause repair provides minimal, targeted fixes for logic failures and state-consistency issues.
How it works
- Provide your architecture details, call logs, or traces and tool behavior.
- The auditor maps the user-perceived turn boundaries and internal processing times.
- It identifies specific layer failures across the STT, reasoning, and synthesis pipeline.
- You receive a readiness verdict and a golden-call test suite for validation.
Frameworks & tools
Works with any voice stack including Vapi, Retell AI, Bland AI, Twilio, Deepgram, and custom WebRTC or SIP integrations.
Why this beats prompting it yourself
Generic prompts miss the nuances of user-perceived latency versus internal model time. This skill enforces strict quality rules for idempotency and side-effect handling that standard LLMs often overlook in voice contexts.
Use cases
- Debugging why an agent interrupts users mid-sentence or stays silent too long.
- Preventing duplicate API calls when a user repeats a request during a slow turn.
- Auditing a new agent for production readiness before a high-volume launch.
- Creating a testing suite for edge cases like slow networks or failed tool calls.
Known limitations
Requires specific call timestamps or architecture logs to provide precise latency measurements. Does not perform real-time monitoring of live calls.
Requires manual provision of call logs or architecture traces. Does not provide real-time monitoring or direct integration with live telephony streams.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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