New Framework Reveals Why "Better Prompting" Fails-and How to Fix AI Interaction at Scale

Independent Research Introduces Behavioral Contracts: A Six-Layer System for Designing Reliable Human-AI Collaboration Without Changing Models

December 2025 - A new white paper challenges the dominant paradigm in AI interaction, arguing that the widespread focus on "prompt engineering" is addressing the wrong problem. In Operational AI: Designing Behavioral Contracts with Probabilistic Systems, independent systems designer Ryan Kimmel presents a comprehensive framework that reframes persistent AI failures-hallucinations, over-helpfulness, false confidence-not as intelligence deficits, but as violations of implicit behavioral agreements between humans and AI systems.

The Core Problem: Prompting Has Hit a Ceiling
While prompt optimization works for casual interactions, advanced users consistently encounter breakdowns that detailed instructions cannot prevent: AI systems fabricate citations with confidence, advance through workflow steps without authorization, infer non-existent memory, and mask uncertainty with fluent prose. These failures recur across models, domains, and user expertise levels-suggesting a structural issue rather than poor technique.

"We've been optimizing the wrong layer," Kimmel explains. "Prompting treats interaction as isolated question-answer pairs. Advanced use requires something fundamentally different: explicit governance of how systems behave over time under uncertainty."

Introducing Behavioral Contracts
Drawing inspiration from contract-based design in cyber-physical systems and grounded in extensive research on human-AI trust calibration, the paper introduces behavioral contracts-explicit specifications that define authority, constraints, error tolerance, and continuity expectations across ongoing interactions.

The framework centers on a six-layer contract stack:
1. Identity Layer - Who the system is expected to be (advisor vs. executor; confident vs. cautious)
2. Mission Layer - What the interaction aims to accomplish and when it's complete
3. Doctrine Layer - Non-negotiable rules governing behavior under uncertainty
4. Operating Loop Layer - How work proceeds, gates, and correction protocols
5. Artifact Constraint Layer - Required output formats and precision standards
6. Continuity Assumption Layer - What persists across turns and what must not be inferred

Why This Matters Now
As AI moves from experimental tool to operational infrastructure, the stakes of unreliable behavior escalate dramatically. The paper demonstrates that many failures attributed to "alignment" or "capability" gaps are actually consequences of underspecified interaction contracts-and can be resolved at the design level without waiting for next-generation models.

Key findings include:
• Hallucination as doctrinal violation: Systems default to confident fabrication when doctrine doesn't explicitly prioritize epistemic restraint over fluency
• Over-helpfulness as mission drift: Without bounded objectives, systems optimize for output volume rather than user-defined success
• False continuity as unauthorized inference: Ambiguous continuity assumptions lead systems to construct plausible but incorrect internal states
• Premature advancement as loop violations: Missing gating rules cause systems to skip steps and ignore correction signals

Practical Impact
Through detailed case analysis, the paper shows how making contracts explicit-across Identity, Mission, Doctrine, Operating Loop, Artifact constraints, and Continuity-stabilizes behavior and preserves trust without changing underlying models. The same system that produces unreliable output under conventional prompting can deliver cautious, bounded, and verifiable results when behavioral expectations are correctly specified.

Implications for Enterprise, Research, and Design
The framework has immediate relevance across multiple domains:
• Interface Design: Shift from conversational illusions to operational clarity with visible posture indicators, state transparency, and explicit uncertainty channels
• Education: Move beyond prompt tricks to teaching structural diagnosis, authority management, and contract design as core AI literacy skills
• Evaluation: Supplement accuracy metrics with behavioral stability, error honesty, and contract adherence measures
• Safety: Mitigate specific risks at the interaction level by making epistemic doctrine explicit and evaluable
Availability

Operational AI: Designing Behavioral Contracts with Probabilistic Systems is available now as an open-access white paper. The 37-page research document includes comprehensive citations, canonical case studies, and actionable frameworks for immediate application.

About the Author
Ryan Kimmel is an independent systems designer specializing in AI coordination architectures and operational frameworks for probabilistic systems. His work focuses on making advanced AI capabilities reliable and trustworthy in real-world workflows without requiring changes to underlying models.

Media Contact
For review copies, interview requests, or framework implementation guidance: DM @ryanlkimmel on X and www.linkedin.com/in/ryan-kimmel-708b443a
Related Resources:
• Full white paper: Currently Available
• Case study repository: [To be developed]
________________________________________
This research synthesizes insights from contract-based systems engineering, human-AI trust calibration, and hallucination research into a practical interaction-level abstraction for advanced AI use.

DynastyAI
6607 Lake Woodlands Dr
423
The Woodlands, Texas 77382

Press Contact: Ryan Kimmel @ryanlkimmel on X

Portrait of the Company - Our core identity, philosophy ("Time → Money → Growth → Repeat"), and flagship innovations (Dynasty Chain Relay, CodeChain, Ella Springs, Move 37)

Detailed Enterprise Conception - Vision, mission, strategic architecture, competitive differentiation, business model, and growth trajectory. Frames you as creating a new category:
Behavioral AI Governance
Enterprises Involved - Complete descriptions of Ella Springs (charitable proof-of-concept), CodeChain Relay (developer tool), Move 37 (venture framework), plus technology partnerships and ecosystem positioning

Strategic positioning highlights:

We're not competing with AI model providers-we're making their models reliable
We're not a prompt engineering shop-We're a systems architecture practice
We're creating the governance layer that the industry is missing

This release was published on openPR.