New For voice and chat bots

Monitoring & Observability Solutions for Conversational AI Agents

Monitor every conversation, measure quality and reliability across thousands of metrics, and automatically correct prompts and test the fix when live calls reveal a problem.

01 · Granular checks

Score every conversation.

Every voice call and chat thread gets a quality score with the transcript, trace, model response, tools, latency, and outcome attached.

Measure what the user experienced.

Track thousands of metrics across task success, hallucination, tone, compliance, interruptions, handoffs, latency, and custom rubrics.

Review the entire interaction.

Replay audio, transcript, turns, tool calls, model output, and trace spans as one connected conversation.

02 · Auto improve

From a failed conversation to the turn that caused it.

One interaction, every signal, one investigation. See what the user said, what the agent did, which tool failed, and why the outcome broke.

  1. alert trace4f9ac21e…8b7d40f2

    The live call is flagged.

    A conversation misses its goal, violates a policy, or leaves the user unresolved. Prompt Toaster captures the exact call, turn, score, and evidence instead of leaving you with a vague quality number.

  2. trace trace4f9ac21e…8b7d40f2

    The failing turn is identified.

    Open the precise user request, agent response, audio moment, model, prompt version, and tool result that caused the failure.

  3. logs trace4f9ac21e…8b7d40f2

    The technical cause is connected.

    Follow the conversation into model latency, retrieval, tool calls, retries, downstream APIs, token usage, and deployment context.

  4. agent trace4f9ac21e…8b7d40f2

    The correction is tested automatically.

    Prompt Toaster drafts a targeted prompt change, runs it against periodic scenarios and prior failures, and verifies the result before expanding it to more live traffic.

03 · Features

Everything your agent needs to get better.

Before launch, in production, and always improving: Prompt Toaster measures conversations, finds failures, and verifies automatic prompt corrections.

01

Simulation testing

Run realistic scenarios, edge cases, adversarial prompts, and periodic regression tests before a new agent version reaches users.

02

See dependencies move.

Score every live call and chat across quality, latency, compliance, task success, and custom metrics with the evidence attached.

03

Human review & ground truth

Label conversations, define the correct outcome, and compare human judgment with automated scores to improve evaluation accuracy.

04

Alerts with the context attached

Track failures, regressions, and threshold breaches across thousands of metrics, then notify your team with the exact evidence.

05

Prompt optimizer

Draft evidence-grounded prompt corrections from failing conversations instead of asking your team to guess what changed.

06

Automatic verification

Run corrected prompts against prior failures and periodic scenarios, then confirm the target metric improved without new regressions.

04 · Privacy & Compliance

Protect every conversation your agents handle.

Prompt Toaster keeps recordings, transcripts, evaluations, and agent traces governed by the policies your business and customers require.

01 · Conversation privacy
Keep recordings, transcripts, evaluations, and agent traces protected with clear access, retention, and redaction policies.
02 · Compliance evidence
Review the exact conversation, metric, rubric, model response, and tool call behind every quality or policy decision.
03 · Private by design
Keep recordings, transcripts, evaluations, and agent traces under your control. Set retention and residency policies that fit your customers, industry, and compliance requirements.
04 · Controlled improvement
Automated prompt corrections pass through repeatable tests and approval policies before they affect more customers.

05 · Metrics

Thousands of metrics. One quality picture.

Track every dimension of agent behavior across calls and chats: quality, latency, compliance, task success, model, prompt, voice, provider, tool, version, customer segment, and environment.

Audio-native metrics
Measure pronunciation, pauses, interruptions, response timing, accent clarity, and speech quality across every voice interaction.
Conversation and cause, joined
Connect every quality score to the exact turn, prompt version, model response, tool call, trace span, and deployment context.
Custom quality metrics
Define your own rubrics for resolution, compliance, tone, hallucination, task success, and agent behavior, then score them automatically.
agent quality monitor · 192 checks · avg score 44% LIVE
CHECKS 192
PASSING 184
REVIEW 6
FAILING 2
AT RISK 11
voice-quality voice 72
chat-quality chat 32
latency latency 40
compliance compliance 24
outcomes outcomes 24
CPU
0% 100% degraded failing

06 · Live monitoring

Correct the prompt before the next failure.

Prompt Toaster monitors live voice and chat conversations, brings a human reviewer into uncertain cases, and connects MCP-compatible AI agents to investigate failures and correct prompts.

Finds failures in live calls
Detect task failures, hallucinations, policy violations, awkward handoffs, latency spikes, and tool errors across every conversation.
Corrects prompts automatically
Use the failing conversation as evidence to draft a focused prompt change instead of guessing at a rewrite.
Human review and MCP agents
Route uncertain evaluations to a human reviewer or an MCP-connected AI agent, then run periodic tests before expanding a correction.
mcp · agent session LIVE
> ask prompt toaster
show voice-agent failures from today's calls
→ get_call_metrics { window: "today", channel: "voice" }
← 4 quality checks need attention
call handledpassed92%calls1,284 dropped earlyfailed7.8%calls100 appointment bookedpassed64%calls821 asked for namemissed18%calls231
> why are early drops increasing?
→ investigate_call_failures { metric: "dropped_early", compare: "last_7d" }
← prompt version v18 · 3 failure patterns
long greeting  ·  handoff delay  ·  no recovery after silence
→ inspect_conversations { metric: "asked_for_name", result: "missed" }
← 31 calls skipped name collection after interruption
sample call: call_9f3c · appointment request
→ propose_prompt_fix { issue: "missed_name", fix: "confirm_name_before_booking" }
← Claude drafted a fix. 240 regression calls queued.
> 
4 tool calls · 4.2s MCP-connected AI agents
Feature comparison

The cost of knowing every conversation.

Price coverage by conversations, minutes, evaluations, retention, and review volume—not seats or opaque infrastructure multipliers.

Line item Prompt Toaster Others
Additional per agent or per seat pricing None Yes
Live monitoring rate $0.1 / min $0.5 + / min
Metric evaluation $0.01 / metric / min $0.1 + / metric / min
Spam detection & monitoring Free None
Custom metrics Free Additional charges
Client facing dashboards & reports Included Included
API, MCP support Included None
Workspaces Unlimited Per workspace pricing
Concurrent lines Unlimited 10

Pricing

Pricing that won't break your bank

No per-seat fees.

Base 30-day trial
$50 /month

All features included.

Contact us

Cancel anytime

Includes
Audit call and chat logs
Included
$0.01 / metric / min
Red teaming, data privacy & compliance
Included
$0.01 / metric / min
Metric evaluation & scoring
Included
$0.01 / metric / min
Prompt corrections & verification tests
Included
$0.01 / metric / min
FAQ

Frequently asked questions

What is Prompt Toaster?
Prompt Toaster monitors voice and chat conversations, scores agent quality, connects failures to their technical causes, and helps teams improve prompts with evidence from live interactions.
What can Prompt Toaster measure?
Track thousands of metrics across task success, resolution, hallucination, tone, compliance, latency, interruptions, handoffs, tool calls, model behavior, and custom evaluation rubrics.
Can Prompt Toaster monitor voice and chat agents?
Yes. It is designed for both voice calls and chat threads, with conversation context, transcripts, audio moments, model responses, tool calls, and traces connected in one investigation.
How does Prompt Toaster find failures?
Live conversations are evaluated against quality and operational metrics. When an interaction fails, the system identifies the relevant turn, evidence, prompt version, model, tool, and technical context.
Can Prompt Toaster correct prompts automatically?
Yes. When a live call reveals an error, Prompt Toaster can draft an evidence-grounded prompt correction, run periodic tests against prior failures, and verify the change before broader rollout.
Can my team define its own evaluations?
Yes. Create custom metrics and rubrics for your agent's task, industry, policies, and customer experience, then combine automated scoring with human review and ground truth.