Top 5 platforms for debugging voice agents

TL;DR

Voice agents introduce unique debugging challenges that traditional LLM monitoring tools can't handle. Speech-to-text errors, latency issues, conversational flow breakdowns, and audio quality problems require specialized observability. This guide compares the top 5 platforms for debugging voice agents: Maxim AI (comprehensive simulation and evaluation for voice workflows), Braintrust (evaluation-first approach with audio attachments), Arize (production monitoring with multimodal tracing), LangSmith (deep agent tracing with OpenTelemetry), and Retell AI (native voice agent platform with built-in analytics). Choose based on your team's needs: Maxim for end-to-end quality management, Braintrust for dataset-driven evaluation, Arize for ML observability, LangSmith for framework-agnostic tracing, and Retell for turnkey voice automation.

Table of Contents

1. Why Voice Agent Debugging Matters
2. Key Debugging Challenges for Voice Agents
3. Platform Comparisons
   • Maxim AI
   • Braintrust
   • Arize
   • LangSmith
   • Retell AI
4. Feature Comparison Table
5. How to Choose the Right Platform
6. Conclusion

Why Voice Agent Debugging Matters

Voice agents have evolved from basic command responders to sophisticated conversational systems handling customer support, appointment scheduling, and sales calls. The global voice AI market is projected to reach $29.28 billion by 2026, driven by increasing adoption of AI-powered phone automation.

Unlike text-based AI systems, voice agents face unique evaluation challenges that make traditional debugging approaches inadequate:

• Real-time latency requirements: Delays of even 200ms disrupt conversational flow and feel robotic to users
• Multi-component failure modes: Issues can originate from speech-to-text, language models, or text-to-speech layers
• Audio quality dependencies: Background noise, accents, and connection quality affect transcription accuracy
• Conversational context: Interruptions, topic changes, and multi-turn context require specialized tracking

Without proper debugging infrastructure, teams discover quality issues only after users complain. This reactive approach leads to customer frustration, lost revenue from dropped calls, and extended development cycles as engineers manually review call transcripts without understanding root causes.

[Image placeholder: Voice agent pipeline showing STT > LLM > TTS with failure points marked at each stage]

Key Debugging Challenges for Voice Agents

1. Speech Recognition Errors

Voice agents rely on accurate speech-to-text transcription, but real-world audio introduces noise, accents, and domain-specific terminology. A misheard word early in a conversation cascades into incorrect responses throughout the interaction.

Debugging needs: Confidence scores, alternative transcriptions, and audio replay capabilities to identify systematic STT failures.

2. Latency and Response Time

Conversational AI requires sub-second response times to feel natural. Voice agents combine multiple processing steps (STT, LLM inference, TTS generation), each adding latency. Teams need visibility into which component causes slowdowns.

Debugging needs: Span-level timing data, p95/p99 latency metrics, and component-level performance tracking.

3. Conversational Flow Breakdown

Voice agents handle interruptions, context switches, and multi-turn dialogue. Understanding why an agent lost context or failed to complete a task requires tracing entire conversation trajectories, not individual LLM calls.

Debugging needs: Session-level tracing, conversation replay, and trajectory analysis tools.

4. Tool Calling and Action Execution

Voice agents execute backend actions like booking appointments or updating records. Failures in tool calling often stem from incorrect parameter extraction or API integration issues.

Debugging needs: Tool call logging, parameter validation, and integration testing capabilities.

5. Audio Quality Assessment

Synthesized speech quality varies by voice provider and configuration. Teams need to evaluate naturalness, tone, and intelligibility beyond transcription accuracy.

Debugging needs: Audio quality metrics, voice cloning validation, and subjective evaluation workflows.

Platform Comparisons

1. Maxim AI

[Image placeholder: Maxim AI dashboard showing voice agent traces with audio playback, evaluation scores, and conversation flows]

Platform Overview

Maxim AI is an end-to-end AI quality platform that helps teams ship voice agents 5x faster through simulation, evaluation, and observability. Unlike observability-only tools, Maxim provides a complete lifecycle approach covering pre-production experimentation, automated testing, and production monitoring for multimodal agents.

The platform's voice agent capabilities include conversational-level evaluation, audio replay, multi-turn simulation, and human-in-the-loop quality checks. Teams use Maxim to test voice agents across hundreds of scenarios before deployment and continuously monitor production conversations for quality regressions.

Key Features

Conversational Simulation

• Generate synthetic voice conversations with realistic user personas and scenarios
• Test agents across hundreds of edge cases without manual calling
• Evaluate complete conversation trajectories, not just individual turns
• Identify failure points and replay conversations from specific steps

Multi-Level Evaluation

• Flexible evaluators configurable at session, trace, or span level
• Pre-built evaluators for task completion, conversational coherence, and audio quality
• Custom evaluators using deterministic rules, statistical methods, or LLM-as-a-judge
• Human review workflows for nuanced quality assessment

Production Observability

• Real-time monitoring with distributed tracing for voice pipelines
• Audio attachments linked to traces for debugging transcription issues
• Custom dashboards tracking latency, completion rates, and quality metrics
• Automated alerts when conversations deviate from expected behavior

Data Curation Engine

• Import audio datasets including voice recordings and transcripts
• Curate datasets from production logs with human labeling
• Continuously evolve test suites using evaluation feedback
• Support for multimodal data (audio, text, metadata)

Cross-Functional Collaboration

• No-code UI for product teams to configure evaluations and review results
• SDKs in Python, TypeScript, Java, and Go for engineering teams
• Custom dashboards without engineering dependency
• Shared workspace for AI engineers, product managers, and QA teams

Integration Capabilities

• Native integrations with popular voice frameworks (LiveKit, Twilio, Retell)
• OpenTelemetry-compatible for vendor-agnostic tracing
• REST API for programmatic access to evaluation and observability data
• Webhook support for real-time notifications

[Diagram placeholder: Maxim AI workflow showing Experimentation > Simulation > Evaluation > Production Monitoring cycle with feedback loops]

Best For

Maxim AI excels for teams that need:

• End-to-end quality management from pre-production testing through production monitoring
• Cross-functional workflows where product teams drive quality without engineering bottlenecks
• Advanced evaluation combining automated metrics with human judgment
• Multimodal agent development supporting voice, text, and visual interactions
• Rapid iteration cycles with simulation reducing manual testing overhead

Organizations using Maxim for voice agents include Clinc (conversational banking), Comm100 (customer support), and Atomicwork (enterprise support automation).

Comparison with competitors: While Braintrust focuses primarily on evaluation and LangSmith emphasizes observability, Maxim provides the full stack. Compared to Arize, Maxim offers deeper support for agent-specific workflows like simulation and trajectory-level evaluation.

2. Braintrust

Platform Overview

Braintrust is an AI evaluation and observability platform with strong voice agent capabilities. The platform takes an evaluation-first approach, automatically logging traces from production and converting them into evaluation datasets for continuous improvement.

Braintrust's voice support includes audio attachments on traces, integration with Evalion for simulated conversations, and pre-built evaluators for voice-specific metrics like STT confidence and latency.

Key Features

• Audio trace attachments: Link raw audio files to traces for debugging what the agent actually heard
• Evalion integration: Run automated conversation simulations with realistic caller behavior
• Voice-specific evaluators: Assess STT confidence, intent classification accuracy, and response latency
• Production monitoring: Track key metrics (STT scores, latency percentiles, escalation rates)
• Dataset creation from traces: Convert failed production calls into test cases

Best For

Teams who prioritize offline evaluation and want to systematically test voice agents before deployment. Works well for organizations already using Braintrust for text-based AI who need to add voice capabilities.

3. Arize

Platform Overview

Arize provides ML observability and LLM monitoring with support for multimodal agents including voice. The platform uses OpenInference instrumentation to automatically capture traces from agent frameworks, offering production monitoring for voice pipelines.

Arize's strength lies in its ML background, providing drift detection, bias monitoring, and comprehensive tracing for complex agent systems.

Key Features

• Multimodal tracing: Unified traces for voice, text, and visual inputs
• OpenTelemetry compatibility: Framework-agnostic instrumentation
• LLM-as-a-judge evaluations: Automated quality assessment for voice interactions
• Session tracking: Group conversations for debugging user journeys
• Real-time monitoring: Dashboards tracking latency, token usage, and failures
• Phoenix open-source option: Self-hosted deployment for data-sensitive environments

Best For

Teams with ML operations background who need production monitoring for voice agents. Best suited for organizations requiring comprehensive observability across traditional ML models and LLM-based agents.

4. LangSmith

Platform Overview

LangSmith is the observability and evaluation platform from LangChain, offering deep tracing for AI agents regardless of framework. While not voice-specific, LangSmith's agent tracing capabilities support multimodal workflows including voice applications.

The platform provides detailed run-tree views showing every step of agent execution, from initial speech input through final audio output.

Key Features

• Deep agent tracing: Capture every LLM call, tool invocation, and decision point
• Framework agnostic: Works with LangChain, custom agents, and voice frameworks via OpenTelemetry
• Polly debugging assistant: AI-powered trace analysis to identify failure patterns
• LangSmith Fetch CLI: Terminal-based debugging for coding agents
• Multi-turn evaluations: Score complete conversation threads
• Insights Agent: Automatically categorize production usage patterns

Best For

Engineering teams building custom voice agents who need maximum visibility into agent behavior. Ideal for developers comfortable with code-first approaches and terminal-based workflows.

5. Retell AI

Platform Overview

Retell AI is a specialized voice agent platform that includes native debugging and analytics. Unlike general-purpose observability tools, Retell provides end-to-end voice automation with built-in monitoring, making it the easiest option for teams focused exclusively on phone call automation.

The platform handles telephony integration, speech processing, and agent orchestration while providing comprehensive analytics on call performance.

Key Features

• Native voice platform: Built-in STT, LLM, and TTS orchestration
• Low-latency architecture: ~600ms response time with proprietary turn-taking model
• Post-call analysis: Automatic transcripts, latency metrics, and call summaries
• Drag-and-drop workflows: Visual builder for conversation flows
• BYOC telephony: Integration with Twilio, Telnyx, or custom SIP trunks
• Real-time dashboards: Monitor call volumes, success rates, and quality metrics
• Compliance-ready: SOC 2, HIPAA, and GDPR support

Best For

Organizations building production voice agents who want an all-in-one platform. Best suited for teams without deep AI/ML expertise who need turnkey voice automation with built-in monitoring.

Feature Comparison Table

[Table placeholder: Comparison matrix with platforms as rows and features as columns]

Conclusion

Voice agents represent the next frontier in AI automation, but their unique characteristics demand specialized debugging tools. While text-based observability platforms struggle with audio quality, latency, and conversational flow, the platforms covered here provide voice-specific capabilities.

For teams serious about shipping reliable voice agents at scale, the choice often comes down to lifecycle scope. Observability-only tools catch production issues but offer limited pre-deployment testing. Voice-native platforms like Retell simplify deployment but lack flexibility for custom workflows.

Maxim AI bridges this gap by providing comprehensive simulation, evaluation, and observability in a single platform. Teams using Maxim test voice agents across hundreds of scenarios before deployment, catch quality regressions in production, and iterate faster through cross-functional workflows.

The future of AI quality management lies in end-to-end platforms that support the full agent lifecycle. As voice agents become more sophisticated, handling complex multi-turn dialogues and executing critical business workflows, the need for robust debugging infrastructure only intensifies.

Start building better voice agents today with Maxim's free trial, or explore our agent evaluation guide for best practices on measuring voice agent quality.

Related Resources:

• AI Agent Quality Evaluation
• Agent Tracing for Debugging Multi-Agent Systems
• What Are AI Evals?
• LLM Observability Guide