AI Agent Evaluation

The missing piece in today's agent architectures.

Rippletide evaluates agent outputs and outcomes, not prompts, at runtime, before the answer reaches the user.

The problem

Not the LLM kind of hallucination. The agent kind.

Rippletide's first module is focused on a single recurring problem in every agent stack: hallucinations. But not the LLM kind, the agent kind, the ones compounding in each multi-step process, including false claims about your products or documentation.

False product claims

Your agent invents features, prices, or specs that don't exist in your documentation.

Compounding errors

Each step in a multi-step process amplifies inaccuracies from the previous step.

Invisible failures

Bad answers look perfectly fluent. Without verification, you can't tell truth from fabrication.

How it works

Claim-level verification, at runtime.

For each candidate answer your agent prepares, Rippletide runs a three-step verification pipeline, before the response is delivered.

01

Extract factual claims

For each candidate answer your agent prepares, Rippletide extracts every factual claim: entity, attribute, and relationship.

02

Search your trusted data

Each claim is checked against an exhaustive hypergraph containing your trusted data, including your RAG index if you want.

03

Verify or block

Every claim is classified as supported, unsupported, or contradicted. Rippletide sends back the information to block the answer and computes a hallucination rate.

Why runtime matters

Three approaches. Only one prevents damage.

Pre-deployment testing
When: Before deployment
What: Prompt evaluation
Enforcement: Static
Result: Tells you after the fact that something went wrong

Output monitoring
When: After execution
What: Log analysis
Enforcement: Reactive
Result: Alerts you after the damage is done

Rippletide
When: At runtime, before delivery
What: Claim-level verification
Enforcement: Deterministic
Result: Blocks hallucinations before the user sees them

Outputs, not prompts

We evaluate what the agent actually says, not the instructions it received. Because the gap between intent and execution is where hallucinations live.

Runtime, not post-hoc

Other approaches tell you after the fact that something went wrong. Rippletide performs evaluation during execution, before a bad answer is returned.

Your data, your truth

We import everything you share, product docs, knowledge bases, your RAG index. Verification happens against your ground truth, not a generic model.

Developer tools

Eval Platform

Measure hallucinations, inspect detailed traces, and get production-ready guardrails, all from a single dashboard.

CLI Tool

Evaluate AI agent endpoints directly from your terminal. Test against predefined questions, validate responses, get instant feedback with real-time progress tracking.

SDK

Integrate Rippletide evaluation directly into your agent pipeline with our SDK. Framework-agnostic, drop-in integration.

Runtime Agent Evaluation

Stop shipping hallucinations.

Rippletide is the runtime evaluation layer that catches agent hallucinations before they reach your users. Less than 1% hallucination rate. 100% guardrail compliance. Full explainability.