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 can still fail on production write-actions. Rippletide makes the risky action, evidence, approval, and expected outcome reviewable before access is enabled.
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 makes reliability reviewable
Rippletide moves the approval question away from model-level averages and onto the evidence and rules for a specific write-action.
Pre-Execution Enforcement
Decision previews show which scenarios should allow, escalate, or block. Runtime enforcement is the expansion path after the boundary is proven.
Continuous Verification
Regression scenarios preserve the tested boundary as policies, workflows, and agent versions change.
What the first reliability scope produces
1Riskiest write-action
3Decision preview outcomes
10Business days for the 10-Day Proof
TraceEvidence linked to each preview
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.
Does the first reliability review require production access?
No. The initial review can use tool definitions, policies, representative traces, and unsafe scenarios. No live production access is required.
Does this work for multi-agent systems?
Yes. The same review method applies across an agent fleet, while each Safety Case remains scoped to one action and its evidence and approval boundary.
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.
Free Risk Review
Start with the write-action most likely to block approval
Map the action, evidence, and approval gaps in one 30-minute working session. No live production access required.
- Risky write-action map
- Evidence and approval requirements
- Unsafe scenarios and decision previews