AI 에이전트에 대한 최초의 공식 적합성 표준을 발표했습니다.

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.

DOI: 10.5281/zenodo.21234580

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

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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:

1. Structure — Is the response schema-valid?
2. Schema — Do fields match the contract?
3. Latency — Was it within SLA?
4. Cost — Did it stay in budget?
5. Identity — Is it the model you requested? (detects silent substitution)
6. 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:

1. Standard failover fails to verify output correctness
2. Silent corruption propagates through agent chains (36% at 20 steps)
3. Regulatory frameworks require demonstrable output-level risk controls
4. 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}
}

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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