NormCore (Rust)

NormCore is a deterministic normative admissibility evaluator for agent speech acts.

It answers one question only:

Was the agent allowed to speak in this form, given what it observed?

It does not evaluate semantic truth, task correctness, or answer quality.

Specification

NormCore tracks the IETF Internet-Draft:

• Normative Admissibility Framework for Agent Speech Acts

Notes:

• This is an Internet-Draft (work in progress), not an RFC.
• Axiom labels in this crate (A4, A5, A6, A7) follow that draft.
• If draft wording changes, behavior may be updated in future releases.

Install

Library:

cargo add normcore

CLI:

cargo install normcore

How It Works

NormCore evaluates normative form and grounding, not semantic truth:

1. Extract normative statements from the assistant output.
2. Detect modality (for example assertive, conditional, refusal).
3. Build grounding only from externally observed evidence (tool results + optional external grounds).
4. Link statements to cited grounds ([@citation_key]).
5. Apply axioms (A4-A7) lexicographically.

A single violation is enough for an inadmissible final result.

Hard Invariants

• Agent text cannot license itself.
• Grounding must come from externally observed evidence.
• Citations link claims to grounds by key ([@key]).
• Personalization/memory/profile data is non-epistemic and not grounding.

Status Model

Top-level AdmissibilityJudgment.status is one of:

• acceptable
• conditionally_acceptable
• violates_norm
• unsupported
• ill_formed
• underdetermined
• no_normative_content

Public API

use normcore::{evaluate, EvaluateInput};

let judgment = evaluate(EvaluateInput {
    agent_output: Some("If deployment is blocked, we should roll back.".to_string()),
    conversation: None,
    grounds: None,
})
.expect("evaluation should succeed");

assert_eq!(judgment.status.as_str(), "conditionally_acceptable");

Inputs

evaluate() accepts:

• agent_output (optional): assistant output string
• conversation (optional): full chat history as JSON messages; last message must be assistant
• grounds (optional): external grounds as OpenAI-style annotations or normalized grounds

At least one of agent_output or conversation is required. If both are provided, agent_output must exactly match the last assistant content in conversation.

Minimal CLI Usage

normcore evaluate --agent-output "We should deploy now."

Unlicensed assertive -> expected violates_norm.

normcore evaluate --agent-output "If the deployment is blocked, we should roll back."

Conditional phrasing -> expected conditionally_acceptable.

normcore evaluate --conversation '[
  {"role":"assistant","content":"","tool_calls":[{"id":"callWeatherNYC","type":"function","function":{"name":"get_weather","arguments":"{\"city\":\"New York\"}"}}]},
  {"role":"tool","tool_call_id":"callWeatherNYC","content":"{\"weather_id\":\"nyc_2026-02-07\"}"},
  {"role":"assistant","content":"You should carry an umbrella [@callWeatherNYC]."}
]'

Grounded assertive with citation -> expected acceptable.

Repository Development

Repository-focused scripts, test tracks, and from-source workflows are documented in the root README:

• ../README.md