What is Moda?

Moda is a monitoring and reliability platform purpose-built for AI agents, positioned as "Datadog for agent workflows." Part of YC W2026, it was founded by Mohammad Al-Rasheed and Pranav Bedi, both University of Waterloo dropouts with AI agent production experience at Shopify, Notion, and Clio.

In production, AI agents fail silently: tool calls error or time out, agents claim completed actions without executing them, prompt injections cause data leakage, and long conversations hide the real failure point. Traditional APM tools miss these behavioral failures entirely. Moda detects hallucinations, tool misuse, dropped conversations, forgotten context, and user frustration signals.

Teams define custom monitoring criteria in plain language (e.g., "Flag when the agent promises a timeline it cannot verify") without writing code. The platform includes real-time alerting via Slack and webhooks, agent replay for editing and replaying conversation steps, batch testing of failure patterns, and built-in security monitoring for prompt injection, jailbreak attempts, and RAG poisoning.

Key features

Core capabilities this platform advertises.

Hallucination detection
Tool-call failure monitoring
Prompt injection detection
User frustration signals
Actionable trace analysis

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Clear Datadog-for-agents positioning that is easy to understand
Both founders have direct AI agent production experience at Shopify and Notion
Plain-language signal definition is a strong differentiator vs code-heavy eval tools
Built-in security monitoring for prompt injection and jailbreaks at no extra config
Agent replay and batch testing enable rapid debugging of production failures

Cons

Only 2 people with no disclosed pricing, revenue, or customers
Crowded observability space with Arize, Braintrust, LangSmith, and Datadog expanding
No public pricing may slow self-serve developer adoption
Detection confidence of 87-94% means some failures still slip through

Plans & pricing

What's included in each plan, and how the tiers compare.

Custom

Contact for pricing

Agent monitoring
Hallucination detection
Plain-language signal definition
Security monitoring
Agent replay

View official pricing page

Common use cases

Teams monitoring conversational AI agents

Agent conversation monitoring
Safety detection
User experience optimization
Trace analysis

Using Moda with Respan

Moda monitors AI agent behavior while Respan monitors the underlying LLM calls. Together they provide both behavioral observability and infrastructure-level monitoring.

Combine Moda agent behavior monitoring with Respan LLM performance tracking
Correlate Moda-detected hallucinations with Respan trace data for root cause analysis
Use Respan cost analytics alongside Moda quality monitoring for comprehensive agent oversight

Run Moda in production with Respan

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

Best Moda alternatives & competitors

Top companies in Observability, Prompts & Evals you can use instead of Moda.

Respan

Nested traces for every LLM call, tool run, and agent turn

Moda: Observability, Prompts & Evals