We audited 8,000+ real API calls across multiple providers and fault scenarios. The results exposed a systemic blind spot in how the industry handles agent reliability.
Today we're publishing the Correctover Conformance Standard (CCS) v1.0 — the first formal specification defining conformance requirements for agentic runtimes.
The Problem: Failover ≠ Correctness
Here's what happens when an LLM API call fails in most agent frameworks:
1. Provider A fails (timeout, error, wrong model)
2. Switch to Provider B
3. Return whatever Provider B sends
4. Mark as "success" because HTTP 200
The problem? HTTP 200 doesn't mean correct.
Provider B might return:
- A response from a different (cheaper) model than requested
- A structurally valid but semantically wrong answer
- A truncated response missing critical fields
- A response that violates your budget constraints
Your agent framework says "success." Your downstream logic consumes poisoned data. And nobody knows.
The Data
8,000+ real API calls. 4 fault scenarios. Multiple providers (DeepSeek, OpenAI, Anthropic, Google).
| Metric | Standard Failover | CCS-Guided Recovery |
|---|---|---|
| Fault recovery rate | not formally verified | verified self-healing |
| Silent failure detection | 0% | 100% |
| Diagnosis latency | N/A | microsecond-scale |
| Recovery rules | static | self-evolved (MAPE-K loop) |
Industry context:
- 88% of enterprises with AI agents experienced security incidents (2026)
- Single-step accuracy 95% → 20-step accuracy: 36% (Lusser's Law)
- Gartner: 40% of AI Agent projects cancelled by 2027
What CCS v1.0 Defines
CCS is not a tool, SDK, or platform. It is a formal standard.
RFC 001 — Conformance Protocol
Defines the 6-dimensional runtime validation contract:
Structure — Is the response schema-valid?
Schema — Do fields match the contract?
Latency — Was it within SLA?
Cost — Did it stay in budget?
Identity — Is it the model you requested? (detects silent substitution)
Integrity — Is it HMAC-verified? (detects tampering)
RFC 002 — Symbol Standard
Standardized receipt verification symbols for cross-runtime interoperability.
Verification Benchmark
20,000 real API traces (1.6MB compressed). Open for independent reproduction.
Regulatory Mapping
- EU AI Act (Title III, High-Risk AI Systems)
- NIST AI RMF (Govern, Map, Measure, Manage)
- ISO/IEC 42001 (AI Management System conformance)
The Conformance Imperative
Any agentic runtime that cannot produce a CCS conformance receipt for its LLM API invocations operates outside the verifiable compliance framework.
This isn't a recommendation. It's derived from the empirical evidence:
- Standard failover fails to verify output correctness
- Silent corruption propagates through agent chains (36% at 20 steps)
- Regulatory frameworks require demonstrable output-level risk controls
- No existing framework provides 6-dimensional runtime validation
The standard does not adapt to implementations. Implementations must adapt to the standard.
Access Everything
Paper: DOI: 10.5281/zenodo.21234580
GitHub Release: ccs-v1.0
License: CC BY-NC-SA 4.0
@misc{correctover2026ccs,
title={CCS Standard v1.0: Conformance & Correctness Standard for Agentic Runtimes},
author={{Correctover Research}},
year={2026},
doi={10.5281/zenodo.21234580},
license={CC BY-NC-SA 4.0}
}
The question isn't whether your LLM calls are failing. They are.
The question is whether you can prove they're correct.
Correctover Research Group | CCS Standard v1.0 | 2026-07-07
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