v1.0 — Decision-Ready Applied AI Research Translation
Overview
This release establishes Applied AI Research Translator v1.0 as a decision-complete, non-agentic reference implementation for translating applied AI research into auditable, decision-ready artifacts.
v1.0 formalizes the core premise of the repository:
AI risk in production is primarily a decision governance problem, not a model capability problem.
Rather than showcasing autonomous agents or orchestration patterns, this release demonstrates how applied research can be translated into explicit claims, bounded tasks, evaluation evidence, and human-owned decisions suitable for regulated or high-stakes environments.
What’s New in v1.0
🧭 Decision-Complete Translation Semantics
The repository now explicitly supports all three legitimate translation outcomes:
- Accept translation — evidence supports bounded use
- Reject translation — research does not translate to a defensible task
- Abstain / defer — evidence insufficient; no silent progression
These outcomes are recorded deterministically in signed Decision Summaries.
📦 Canonical Research Translation Packs
Added two new, fully structured research translation examples:
-
haic_reliance_review_59e257ff
Human–AI collaboration and reliance calibration
→ Translation-positive (decision support, non-agentic) -
multi_agent_failure_modes_e0228882
Multi-agent LLM failure modes
→ Translation-negative (explicit, defensible rejection)
Together with the existing Measuring Agents in Production pack, these form a coherent research lineage:
measurement → failure analysis → decision-centric alternatives
🔒 Locked Governance Demo Runs
Introduced three locked demo runs (docs/demo-runs/A, B, C) illustrating:
- explicit abstention
- human override
- disagreement → abstention
These runs demonstrate that no execution path can silently produce or enact a decision.
📖 Research Context Documentation
Added docs/research-context.md to explain:
- how research papers inform translation,
- why some research is intentionally rejected,
- and why the system stops at decision support rather than autonomy.
This document is designed for reviewers performing academic, regulatory, or governance due diligence.
🚫 Explicit Non-Agentic Boundary
The README has been rewritten to clearly state that this repository is:
- not an agent framework,
- not an autonomy platform,
- not a prompt-engineering showcase.
AI is bounded to producing structured candidate outputs; decision authority always remains human.
Why This Release Matters
v1.0 demonstrates a practical alternative to agent-centric AI adoption:
- makes uncertainty explicit
- preserves singular human accountability
- prevents silent automation
- enables audit, re-evaluation, and regulatory review
- supports phase-gate and executive decision forums
This release is intentionally conservative by design and optimized for trust, defensibility, and governance, not speed or autonomy.
Intended Audience
- Principal Engineers
- AI Governance & Risk Leads
- Research-to-Production Architects
- Technical decision owners in regulated or high-stakes environments
Status
Reference implementation — stable (v1.0)
This repository is not a product and does not mandate organizational process.
It serves as a public, non-commercial case study demonstrating disciplined applied AI adoption.