ARC is the missing runtime governance layer for AI agents. Instead of relying on a single assistant, ARC orchestrates ensembles of specialized agents, each wrapped in a control layer that defines role, constraints, tone, and rules for evolution.

With ARC, agentic AI becomes adaptable but bounded.

Multi-agent orchestration without drift:

Agents collaborate, hybridize, or escalate across domains under explicit role boundaries.

Guardrails at runtime:

Every action is checked against constraint stacks, with fallback and temporary overrides applied instantly.

Governed memory: 

Memory is segmented into short-term, semantic, episodic, and procedural layers, with built-in compression and constraint checks to ensure safe continuity.

Adaptive tone modulation:

Urgency, empathy, and formality shift dynamically without breaking role identity.

Transparent outcomes:

Delegation chains, provenance tags, and cryptographic logs make every response accountable and interpretable.

ARC is model-agnostic and runtime-adaptive. It transforms opaque agent swarms into trustworthy collaborators. ARC agents evolve with context while remaining safe, transparent, and aligned.