Catch and fix agent errors before your users do

Agents that score well on standard benchmarks often fail on the domain-specific, incident-aware questions your users actually ask.
Rockfish generates realistic time-series eval suites — grounded in your data — so you see exactly where your agent breaks before it reaches production.

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State-of-the-art agents pass benchmarks —
then fail on your users' actual questions

The problem isn't always the agent. It's the eval.
Generic benchmarks under-test the patterns and questions that matter in production.

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The Dataset Gap

Generic evals use flat or made-up datasets. Real observability, telecom, and security data has time-aware structure — correlated signals, incident windows, anomaly spikes, and event sequences that unfold across time. Agents tested on generic data aren't tested on your data.

The Query Gap

Simple lookups and averages don't reflect production questions. Real users ask incident-aware, context-specific questions: "Why did latency spike after the 2:15 PM deployment?" An agent that can't answer that is not production-ready — but generic evals would never catch it.

Agents that scored 73% on standard benchmark-style tasks fell to 10–20% accuracy on domain-specific, incident-focused queries.

Read our new arXiv paper on AgentFuel: expressive evals for timeseries data analysis agents.

How Rockfish builds your eval suite with AgentFuel

Turn your schema and domain context into a bespoke, expressive eval suite — grounded in the real patterns your agent will encounter in production.

Step 1

Bring your schema + context

- Schema or Sample Data

- Agent to be tested

- Business Context

Step 2

Rockfish builds eval suite

- Time-series generated dataset

- Scenario Injection

- Q & A Pair

Step 3

Your custom eval suite

- Time Series datasets

- Q&A Eval Pairs

- Agent Scorecard

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What teams get from Rockfish evals

Single-signal behaviors

Find where your agent breaks on time-series and incident-aware questions — before production, not after a user reports a wrong answer that cost them trust.

Improve continuously

Refine your agent with grounded, domain-specific scorecards. Track improvements and regressions with reproducible, versioned benchmarks across every release.

Deploy with confidence

Ship on evidence, not benchmark optimism. Generic benchmarks show possibility. Bespoke evals show whether your agent is actually ready for your users.

An agent that passes benchmarks
isn't the same as one that's ready.

Find out where yours actually stands — with an eval suite
built for your domain, your data, and your users' real questions.