Decision Context Graph
Structured facts, provenance, and temporal validity eliminate information gaps that cause unreliable agent behaviour in production.
Production reliability
AI Agent Reliability in Production
Agents that perform well in controlled environments produce unpredictable outcomes at scale. Rippletide validates every action before execution, giving engineering and compliance teams the confidence to deploy autonomous agents in production.
The production reliability gap
The gap between prototype performance and production reliability is not incremental. It is structural, and it blocks enterprise deployment at scale.
- 95% accuracy in testing means 1 in 20 failures in production
- Multi-step workflows compound individual action failure rates
- POC-grade reliability blocks enterprise deployment at scale
- Reactive monitoring catches failures after damage is done
How Rippletide delivers production reliability
Rippletide transforms agent reliability from a statistical property into a deterministic guarantee through structured validation at every decision point.
Pre-Execution Enforcement
Every action must pass deterministic validation before execution. Non-compliant or unverifiable decisions are blocked automatically.
Continuous Verification
The decision runtime monitors policy conformance across multi-step workflows, ensuring reliability is maintained at every stage of execution.
Built for production reliability
<1%Hallucination outcomes
100%Guardrail compliance
99.9%Uptime
24/7Production grade reliability
The math behind 95% accuracy
The deceptive thing about 95% accuracy is that it sounds like a passing grade. In a single-step interaction it almost is. In an agent workflow it is not.
| Steps in the workflow | Per-step accuracy | End-to-end success |
|---|---|---|
| 1 | 95% | 95% |
| 3 | 95% | ~86% |
| 5 | 95% | ~77% |
| 10 | 95% | ~60% |
| 20 | 95% | ~36% |
Multiply by a fleet of agents and a year of operation and the number of incorrect actions touching production systems becomes the operating reality, not the edge case. See why 95% accuracy fails in production for the full argument.
What changes with deterministic enforcement
Reliability stops being a statistical property of the model and becomes a property of the runtime. Rippletide does not improve LLM accuracy. It removes the dependency on LLM accuracy for the part that matters: whether the action should execute.
- Per-action enforcement: each step is validated, so errors do not compound.
- Drift detection: when the decision context graph rejects more actions, you see it before customers do.
- Replayability: the same input plus the same graph plus the same policy version always produces the same decision.
Frequently asked questions
Why is 95% accuracy not enough for production AI agents?
95% accuracy at the action level means 1 in 20 actions is wrong. In a 10-step workflow, the chance that all steps succeed drops below 60%. In a fleet of 1,000 agents executing 100 actions per day, that is 5,000 errors per day reaching production systems.
What is the latency cost of pre-execution enforcement?
Sub-600 milliseconds per decision in production. Evaluation happens in line with the agent loop, so the perceived latency is the same as a single tool call. The reliability gain trades against latency that the agent loop already absorbs.
Does this work for multi-agent systems?
Yes. Rippletide enforces the same decision context graph across every agent in the fleet, so multi-step workflows do not compound failure rates. Each action is validated independently against the same source of truth, removing the drift that breaks multi-agent systems in production.
Learn more
See how Rippletide prevents AI agent hallucinations at their source. Learn how AI agent auditability supports compliance at scale. Explore enterprise use cases to see reliability in practice.
Production Reliability
Deploy AI agents with production-grade confidence
Rippletide validates every agent decision before execution, turning autonomous agents into reliable, auditable enterprise systems.
- Deterministic validation for every agent action
- Production-grade reliability at enterprise scale
- Complete causal traceability for every decision