Evaluate your agent before it answers.
Evaluate your agent before it answers.
Evaluate your agent before it answers.
AI agents promise autonomy.
But autonomy without evaluation is unpredictable.
AI agents promise autonomy.
But autonomy without evaluation is unpredictable.
AI agents promise autonomy.
But autonomy without evaluation is unpredictable.
Rippletide introduces Agent Evaluation, a runtime-first framework that evaluates your agent before it answers, not after. It detects hallucinations, checks factual grounding and gives your team deterministic signals you can trust in production.
Build decisions on a hypergraph database.
Keep language in the LLM, move planning,
policies and outcomes to a system you can
test, trace and ship.
Rippletide introduces Agent Evaluation, a runtime-first framework that evaluates your agent before it answers, not after. It detects hallucinations, checks factual grounding and gives your team deterministic signals you can trust in production.
We evaluate agents outputs and outcomes, not prompts.
We evaluate agents outputs and outcomes, not prompts.
We evaluate agents outputs
and outcomes, not prompts.
We evaluate at runtime, before
the answer reaches the user.
We evaluate at runtime, before
the answer reaches the user.
We evaluate at runtime, before the answer reaches the user.
Why agent evaluation is not "Evals"?
Why agent evaluation is not "Evals"?
Why agent evaluation is not "Evals"?
Most evaluation methods today look backward:
Most evaluation methods today look backward:
Most evaluation methods today look backward:
LLM benchmarks
LLM benchmarks
measure model
performance or
classify outputs.
measure model performance or
classify outputs.
measure model performance or classify outputs.
Promptfoo
Promptfoo
tests prompts,
not the agent’s
planning or tool use.
tests prompts, not the agent’s
planning or tool use.
tests prompts, not the agent’s planning or tool use.
noisy, inconsistent,
not scalable,
irrelevant for
autonomous
agents.
Human evals
Human evals
noisy, inconsistent, not scalable,
irrelevant for autonomous agents.
noisy, inconsistent,
not scalable,
irrelevant for
autonomous
agents.
noisy, inconsistent, not scalable, irrelevant for autonomous agents.
LLM-as-a-judge
LLM-as-a-judge
the “judge” is
itself probabilistic
and can hallucinate.
the “judge” is itself probabilistic and can hallucinate.
the “judge” is itself probabilistic
and can hallucinate.
The two first approaches tell you after the fact that something went wrong.
But autonomous agents need something different:
The two first approaches tell you after the fact that something went wrong.
But autonomous agents need something different:
The two first approaches tell you after the fact that something went wrong.
But autonomous agents need something different:
Evaluation during execution, before a bad answer is returned.
Evaluation during execution, before a bad answer is returned.
Evaluation during execution, before a bad answer is returned.
That’s why Rippletide focuses on runtime agent evaluation.
This is the missing piece in today’s agent architectures.
That’s why Rippletide focuses on runtime agent evaluation. This is the missing piece in today’s agent architectures.
That’s why Rippletide focuses on runtime agent evaluation.
This is the missing piece in today’s agent architectures.
Our philosophy:
evaluate before the answer
Our philosophy:
evaluate before the answer
Our philosophy:
evaluate before the answer
When an agent reasons, plans, selects tools, and prepares a response,
Rippletide evaluates what it’s about to say.
When an agent reasons, plans, selects tools, and prepares a response,
Rippletide evaluates what it’s about to say.
When an agent reasons, plans, selects tools, and prepares a response,
Rippletide evaluates what it’s about to say.
At runtime, we can:
At runtime, we can:
At runtime, we can:
Inspect the agent’s candidate answer
Inspect the agent’s candidate answer
Inspect the agent’s candidate answer
Extract the factual claims it makes
Extract the factual claims it makes
Extract the factual claims it makes
Ground each claim in your data
Ground each claim in your data
Ground each claim in your data
Compute a deterministic score
Compute a deterministic score
Compute a deterministic score
Highlight hallucinations
Highlight hallucinations
Highlight hallucinations
Let you decide what to do before the answer is shown
Let you decide what to do before
the answer is shown
Let you decide what to do before the answer is shown
This is the opposite of “hope it works”. This is evaluation built for production.
This is the opposite of “hope it works”.
This is evaluation built for production.
This is the opposite of “hope it works”.
This is evaluation built for production.
Micro, macro
and the bigger picture
Micro, macro
and the bigger picture
Micro, macro and the bigger picture
We keep it simple on this page,
the full theoretical model is explained in our article:
Micro, macro and multi-determinism for AI agents
We keep it simple on this page,
the full theoretical model is explained in our article:
Micro, macro and multi-determinism for AI agents
We keep it simple on this page,
the full theoretical model is explained in our article:
Micro, macro and multi-determinism for AI agents
In short:
Micro evaluation: is this answer grounded, repeatable, and using tools correctly?
Macro evaluation: is the agent converging toward your policies and business outcomes?
Runtime: we intervene before the agent replies.
This pillar starts with the most urgent micro capability: Hallucination Evaluation for agents.
In short:
Micro evaluation: is this answer grounded, repeatable, and using tools correctly?
Macro evaluation: is the agent converging toward your policies and business outcomes?
Runtime: we intervene before the agent replies.
This pillar starts with the most urgent micro capability: Hallucination Evaluation for agents.
In short:
Micro evaluation: is this answer grounded, repeatable, and using tools correctly?
Macro evaluation: is the agent converging toward your policies and business outcomes?
Runtime: we intervene before the agent replies.
This pillar starts with the most urgent micro capability: Hallucination Evaluation for agents.
Hallucination Evaluation for LangChain agents
Hallucination Evaluation
for LangChain agents
Hallucination Evaluation for LangChain agents
Why?
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 of the multi-step process:
Invented facts
Invented functions / APIs
Wrong policies
Wrong regulatory statements
False claims about your products or documentation.
Why?
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 of the multi-step process:
Invented facts
Invented functions / APIs
Wrong policies
Wrong regulatory statements
False claims about your products or documentation.
Why?
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 of the multi-step process:
Invented facts
Invented functions / APIs
Wrong policies
Wrong regulatory statements
False claims about your products or documentation.
What we evaluate:
For each candidate answer your agent prepares, Rippletide:
What we evaluate:
For each candidate answer your agent prepares, Rippletide:
Extracts the factual claims (entity, attribute, relationship).
Extracts the factual claims
(entity, attribute, relationship).
Searches an exhaustive hypergraph containing your trusted data
(we import everything you share, including your RAG index if you want).
Searches an exhaustive hypergraph
containing your trusted data
(we import everything you share,
including your RAG index if you want).
Searches an exhaustive hypergraph containing your
trusted data (we import everything you share, including
your RAG index if you want).
Checks each claim: Supported, unsupported, contradicted
Checks each claim: Supported,
unsupported, contradicted
Sends you back the information to block the answer
Sends you back the information to
block the answer
You can also use it for cold benchmarks:
You can also use it for cold benchmarks:
Computes a hallucination rate.
Computes a hallucination rate.
Returns an agent readiness score from 1 to 4 (4 = best).
Returns an agent readiness score
from 1 to 4 (4 = best).
Highlights exactly what was hallucinated.(we import everything you share,
including your RAG index if you want).
Highlights exactly what was
hallucinated.(we import everything
you share, including your RAG index
if you want).
If the information exists, our engine will find it. If it doesn’t, we flag it.
No probabilistic judges. No opinions. Only your truth sources.
If the information exists, our engine will find it. If it doesn’t, we flag it. No probabilistic judges. No opinions. Only your truth sources.
If the information exists, our engine will find it. If it doesn’t, we flag it. No probabilistic judges. No opinions. Only your truth sources.
Understanding the score (from 1 to 4)
Understanding the score
(from 1 to 4)
Understanding the score (from 1 to 4)
We think in terms of thresholds:
We think in terms of thresholds:
We think in terms of thresholds:
Thresholds can be tuned per organisation and per use case.
What does not change is the principle: the score is deterministic and grounded in your data, not in another model’s opinion.
Thresholds can be tuned per organisation and per use case.
What does not change is the principle: the score is deterministic and grounded in your data, not in another model’s opinion.
Thresholds can be tuned per organisation and per use case.
What does not change is the principle: the score is deterministic and grounded in your data, not in another model’s opinion.
What’s coming next: runtime hallucination blocking
(Enterprise beta)
What’s coming next: runtime hallucination blocking (Enterprise beta)
What’s coming next: runtime hallucination blocking (Enterprise beta)
Today we start with evaluation. But some organisations need more. We are already testing runtime blocking with selected enterprise partners:
Today we start with evaluation. But some organisations need more. We are already testing runtime blocking with selected enterprise partners:
If the hallucination score drops
below a threshold
If the hallucination score drops below
a threshold
Or if a high-risk fact is unsupported
Or if a high-risk fact is unsupported
Or if a key policy is contradicted
Or if a key policy is contradicted
Rippletide can intervene before the answer is revealed:
Rippletide can intervene before the answer
is revealed:
Rippletide can intervene before the answer is revealed:
Block the answer
Block the answer
Trigger a clarification step
Trigger a clarification step
Escalate to monitoring platform
Escalate to monitoring platform
This is currently in Enterprise beta. If you want early access:
This is currently in Enterprise beta.
If you want early access:
This is currently in Enterprise beta. If you want early access:
Try it and stay in the loop
Try it and stay in the loop
Try it and stay in the loop
We are opening access gradually to the first evaluation module:
We are opening access gradually to the first evaluation module:
Connect your LangChain agent
Connect your LangChain agent
Import your data and/or RAG
Import your data and/or RAG
See hallucinations highlighted with a deterministic score
See hallucinations highlighted with
a deterministic score
Join Rippletide Newsletter
Join Rippletide Newsletter
Join Rippletide Newsletter
Short, sharp updates on new evaluation modules, runtime blocking,
and what’s coming next from our research & engineering teams.
Short, sharp updates on new evaluation modules, runtime blocking and what’s coming next from our research & engineering teams.
Frequently
Asked Questions
Frequently
Asked Questions
Your AI challenges deserve tailored solutions. Let’s discuss your use case today.
Your AI challenges deserve tailored solutions. Let’s discuss your use case today.
What problem does Rippletide solve for enterprises?
How does Rippletide reduce hallucinations in AI agents?
How are guardrails enforced in Rippletide?
Can Rippletide integrate with our existing systems (CRM, ERP, Data Warehouse)?
What is “forward deployment” at Rippletide?
What use cases fit Rippletide best?
What is Rippletide’s architecture?
What’s the difference compared to an LLM-only agent?
How fast can we go live?
What problem does Rippletide solve for enterprises?
How does Rippletide reduce hallucinations in AI agents?
How are guardrails enforced in Rippletide?
Can Rippletide integrate with our existing systems (CRM, ERP, Data Warehouse)?
What is “forward deployment” at Rippletide?
What use cases fit Rippletide best?
What is Rippletide’s architecture?
What’s the difference compared to an LLM-only agent?
How fast can we go live?
Ready to see how autonomous agents transform your enterprise?
Rippletide helps large organizations unlock growth with enterprise-grade autonomous agents
Ready to see how autonomous agents transform your enterprise?
Rippletide helps large organizations unlock growth with enterprise-grade autonomous agents
Ready to see how autonomous agents transform your enterprise?
Rippletide helps large organizations unlock growth with enterprise-grade autonomous agents
Frequently
Asked Questions
Your AI challenges deserve tailored solutions. Let’s discuss your use case today.
What problem does Rippletide solve for enterprises?
How does Rippletide reduce hallucinations in AI agents?
How are guardrails enforced in Rippletide?
Can Rippletide integrate with our existing systems (CRM, ERP, Data Warehouse)?
What is “forward deployment” at Rippletide?
What use cases fit Rippletide best?
What is Rippletide’s architecture?
What’s the difference compared to an LLM-only agent?
How fast can we go live?
What problem does Rippletide solve for enterprises?
How does Rippletide reduce hallucinations in AI agents?
How are guardrails enforced in Rippletide?
Can Rippletide integrate with our existing systems (CRM, ERP, Data Warehouse)?
What is “forward deployment” at Rippletide?
What use cases fit Rippletide best?
What is Rippletide’s architecture?
What’s the difference compared to an LLM-only agent?
How fast can we go live?
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Rippletide USA corp. I 2 embarcadero 94111 San Francisco, CA, USA
Stay up to date with the latest product news,
expert tips, and Rippletide resources
delivered straight to your inbox!
Developers
Resources
© 2025 Rippletide All rights reserved.
Rippletide USA corp. I 2 embarcadero 94111 San Francisco, CA, USA
Stay up to date with the latest product news,
expert tips, and Rippletide resources
delivered straight to your inbox!
Developers
Resources
© 2025 Rippletide All rights reserved.
Rippletide USA corp. I 2 embarcadero 94111 San Francisco, CA, USA