Architecture Overview
Runtime-First AI Architecture
Jarvis is built around a runtime-first architectural model, where language models are treated as replaceable execution providers, not as sources of system behavior or identity.
This approach deliberately separates behavioral control from language generation, allowing the system to remain stable, predictable, and governable even as underlying models change.
Core Architectural Assumptions
- LLMs are external providers, responsible for linguistic rendering, not decision-making.
- System behavior is defined before generation, through explicit control and governance layers.
- Continuity over time matters, requiring memory and state beyond a single prompt.
- Constraints are a feature, not a limitation, and are enforced independently of user input.
These assumptions lead to an architecture focused on control, consistency, and long-term reliability rather than prompt optimization.
Structural Components
Manages lifecycle, configuration, sessions, and integration of all subsystems.
Selects strategies, routes tasks, enforces consistency, and determines how the system should think before any text is generated.
Provides continuity across interactions, influencing future decisions rather than merely extending context length.
Separates internal responsibilities and reasoning modes, enabling structured thinking, verification, and controlled output.
Defines non-negotiable behavioral invariants and constraints that cannot be overridden by prompts or model output.
Model Independence
Because system behavior is governed by runtime control, memory, and normative constraints, Jarvis can operate across different language models without loss of identity.
Model replacement may affect tone, latency, or expressive style, but does not redefine system behavior.
Architectural Note
Jarvis implements a runtime-first architecture where language models are treated as replaceable providers rather than sources of system behavior.
Systems that independently converge toward similar structural assumptions — including externalized reasoning control, memory-driven continuity, and normative governance — often encounter non-trivial considerations when transitioning from experimentation to commercial deployment.
Jarvis provides a documented, production-tested, and licensable implementation of this model.
Commercial Context
This architecture reflects a specific interpretation of runtime-governed AI systems.
Commercial use of structurally similar approaches may require careful evaluation of licensing and governance considerations.
Design Intent
- behavioral consistency
- long-term predictability
- controlled evolution
- responsible deployment at scale
The result is a system that prioritizes governed intelligence over raw generative capability.