Block risky AI agent actions before they execute.
Rippletide sits between your AI agents and the tools they want to use: CRM, ticketing, payments, codebase, and APIs. It checks each proposed action against your policies, permissions, business rules, and live context before execution.
Example: if an agent tries to refund 2,400 USD but policy allows 2,000 USD, Rippletide routes it to manager approval and logs why.
AGENT PROPOSES. RIPPLETIDE CHECKS. ONLY VALID ACTIONS EXECUTE.
- +15 pts agent outcome on evaluated workflows
- Blocks or escalates risky actions
- Full audit trail
Agents start by answering questions.
Then they resolve tickets.
Eventually they need to take real actions.
That's where most companies stop.
Not because the model isn't capable.
Because nobody owns the decision.
Before any of that reaches production, Rippletide checks whether the action is allowed.
Turn your policies into rules agents must follow.
To check an agent action, Rippletide needs a reliable reference: what exists, which process applies, which rule matters, and where the company's limits are.
Rippletide Automatic Ontologies builds and maintains that reference from your documents, APIs, workflow logs, vector stores, and evaluated agent traces, then turns it into the rule layer each action is checked against.
See Automatic Ontologies →Ontologies
Entities, relationships, business objects.
Processes
Workflows, approvals, escalation paths.
Rules
Thresholds, exceptions, obligations.
Limits
Authority, compliance, risk boundaries.
The Missing Layer.
Agents can act. Most cannot prove they should.
Your LLM can reason, plan, and execute across your stack. But a good prompt cannot prove that this specific refund, account change, approval, or API call is allowed right now.
That is the gap Rippletide fills: the execution check between an agent's proposed action and your production systems.
No one ships an API without auth. No one ships a payment flow without approvals. Why ship agents without an execution check?
Agent proposes. Rippletide checks. Valid actions execute.
Rippletide is the Decision Runtime between your agent and the real world. Every action is checked against facts, policies, permissions, and live context before it reaches production systems.
Drop-in. Framework-agnostic.
What changes for your agents.
Risky actions blocked before execution
Blocked before it breaks.
Policy violations and unsupported actions are intercepted before anything reaches your systems. This isn't monitoring. This isn't alerting three days later. This is prevention by design.
Full audit trail
Every decision, fully traceable.
Which data. Which rule. Which outcome. Why. Every agent decision is logged with a complete causal trace, before your compliance team even asks. Regulator-ready, board-ready, production-ready.
Works with your current stack
No rip-and-replace.
Your agent keeps using the same model, framework, and tools. Rippletide adds the execution check before sensitive actions run, whether you use Bedrock, LangChain, CrewAI, or your own infra.
Rules, not model guesses
Rules, not probabilities.
Every agent action is resolved by structured rules and verified facts, not token predictions. Your agent either has the authority and the data to act, or it doesn't. That's what makes it production-ready.
Beyond guardrails.
Most tools watch agents fail. Rippletide prevents it.
Live integrations with the frameworks and platforms you already use, with more on the way.
From evaluation to production in 12 weeks.
Risk review sprint
Pick one high-risk agent workflow. We connect the relevant sources, build the first rule layer, test blocked actions, and show the audit trail before you scale.
Integration & Governance
We integrate into your stack, encode your business rules, and test under real conditions.
Production & Scale
Your agents go live. With monitoring, alerting, and long-term support.
We're building the infrastructure that makes AI agents trustworthy, from cloud orchestration to the edge. Every agent. Every decision. Every environment.
This isn't a tool. It's a new standard.
Questions & answers.
Everything you need to know about AI decision enforcement. Can't find your answer? Talk to our team
Rippletide checks AI agent actions before they execute. If an agent wants to refund a customer, modify an account, approve a payment, call an API, or merge code, Rippletide verifies whether that action is allowed. Approved actions run. Risky actions are blocked or escalated. Every decision is logged.
Rippletide sits between your AI agents and the tools they want to use: CRM, ticketing, payments, codebase, internal APIs, and workflow systems. Your LLM and agent framework still reason and plan. Rippletide handles the execution check before the action reaches production.
Rippletide checks the proposed action against your policies, permissions, business rules, verified facts, live context, and authority limits. The core question is simple: is this specific action allowed for this customer, account, amount, user, tool, and moment?
A blocked action can be routed to a human approver, sent to a fallback workflow, or rejected with a clear reason. The trace records what the agent tried to do, which data was used, which rule applied, what outcome was chosen, and why.
No. Rippletide is a Decision Runtime for AI agent governance. Prompt guardrails guide or filter model output. Monitoring tells you what happened after the fact. Rippletide checks the action before execution and can stop it before it reaches your systems.
No. Rippletide works alongside your current LLM and agent framework, including OpenAI, Anthropic, LangChain, CrewAI, Bedrock, or custom stacks. Your agent still proposes the action. Rippletide decides whether that action is allowed to execute.
Automatic Ontologies turn your policies, SOPs, API schemas, workflow logs, and evaluated agent traces into rules agents must follow. Instead of asking teams to manually model every rule for months, Rippletide builds the first decision reference from the sources your business already uses.
The Context Graph is the live facts layer Rippletide uses during the execution check. It keeps track of relevant facts, relationships, permissions, provenance, and validity windows so agents do not act on stale, unrelated, or unauthorized context.
Every checked action produces a decision trace: what the agent proposed, what context was used, which rule or policy applied, what happened, and why. Compliance, security, and operations teams can review the evidence instead of reconstructing events after an incident.
Most teams start with one high-risk agent workflow, such as refunds, account changes, approvals, incident response, or code changes. Rippletide connects the relevant sources, builds the first rule layer, tests blocked actions, and shows the audit trail before broader rollout.
Rippletide is designed to run inline before execution. The goal is to add a fast execution check without replacing the agent, changing the model, or forcing teams to rebuild their stack.
Rippletide was founded by Patrick Joubert and Yann Bilien. The team works from Paris and San Francisco and focuses on making production AI agents controllable, auditable, and safe to let act.
Your agents are already making decisions.
The question is whether you control them.
We start with a 2-week validation sprint. No commitment. Concrete results.