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Sentrial: Observability, Prompts & Evals
Founded 2025|San Francisco, CA|2-10 people|Unknown
What is Sentrial?
Sentrial is a production monitoring platform purpose-built for AI agents — positioned as "Datadog for Agent Reliability." Part of YC W2026, it was founded by Neel Sharma (CEO, UC Berkeley CS, ex-Sense) and Anay Shukla (UC Berkeley CS, deployed DevOps agents at Accenture).
The platform semantically detects when agents loop, hallucinate, misuse tools, or frustrate users in production, then helps engineering teams diagnose the root cause and fix it fast. Integration requires just a few lines of code via SDK or MCP. Sentrial learns what "correct" looks like for each workflow and flags drift from expected behavior.
The founders built Sentrial after encountering real production failures: a support agent misclassifying refund requests as product questions, and a document drafting agent hallucinating missing sections. Traditional observability tools track latency and errors but cannot semantically evaluate whether an agent's output is actually correct — Sentrial fills this gap with AI-native monitoring.
Key features
Core capabilities this platform advertises.
- Agent failure root cause analysis
- Tool selection monitoring
- Cost budget tracking
- Production agent observability
- Real-time alerting
Strengths and tradeoffs
What this tool does well, and the limitations to keep in mind.
Pros
- Addresses genuine growing pain point as agents move into production
- Semantic detection of loops, hallucinations, and tool misuse goes beyond basic logging
- Low-friction integration with just a few lines of code via SDK or MCP
- Proven Datadog-for-X positioning in enterprise software
- Founders have relevant agent deployment experience at Sense and Accenture
Cons
Plans & pricing
What's included in each plan, and how the tiers compare.
Paid
Contact for pricing
- Semantic agent monitoring
- Root cause analysis
Common use cases
Teams running AI agents in production
- Agent reliability monitoring
- Cost optimization
- Failure debugging
- Production observability
Using Sentrial with Respan
Sentrial monitors agent behavior semantically while Respan monitors the underlying LLM calls. Together they provide both behavioral and infrastructure-level observability.
- Combine Sentrial semantic detection with Respan LLM trace analysis
- Correlate agent behavior issues with LLM performance metrics in Respan
- Use Respan cost data alongside Sentrial quality monitoring
Run Sentrial in production with Respan
10KFree traces/mo
500+Models
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Last verified: March 27, 2026
