Data Agent
Dynamic memory: short-term context, long-term retrieval, external knowledge, episodic recall. Learns to focus, recall, abstract, and enrich data across modalities.
What is VFMOS?
A virtualization layer that decouples what an agent needs to accomplish from how it executes. The virtual FM abstraction gives apps the illusion of a fully dedicated, always-optimized model instance. Behind the scenes, three agent hierarchies—Data, Model, and Trust—dynamically manage knowledge, compute, and reasoning.
Model Optimizer
Smart routing: selects, composes, distills, and adapts physical FMs. Routes requests dynamically. Updates and evolves materialized models for optimal quality/cost.
Trust Agent
Graduated verification: switches between fast thinking (system 1) and deliberate reasoning (system 2). Integrates tools, reasoning, ethics, and domain logic seamlessly.
Who benefits? Three personas, three applications.
The Orchestrator: Customer Support at Scale
Problem: HPE support teams manually manage 400+ decision trees, knowledge bases, and historical cases for each customer query. Context switching and model selection become a bottleneck.
With VFMOS: Upload your support playbooks, FAQs, CRM history, and escalation rules once. The Data Agent automatically retrieves relevant context. The Model Optimizer picks a model sized for latency/cost. The Trust Agent ensures responses follow compliance policies. Your agent logic just says "answer this ticket"—VFMOS handles the rest. Result: 3x faster resolution, fewer hallucinations, auditable decisions.
The Conductor: Multi-Tenant Observability Agents
Problem: Building observability agents for 100s of customer environments requires manual data alignment, model selection per customer, and reasoning/verification rules hardcoded into your pipeline. Scale becomes fragile.
With VFMOS: Define your multi-agent observability workflow once (correlation detection, anomaly reasoning, recommendation generation). VFMOS Data Agent automatically adapts embeddings and retrieval per customer's data schema. Model Optimizer routes queries to lightweight models for simple cases, reasoning models for complex anomalies. Trust Agent enforces SLOs and privacy policies without your code knowing about them. Deploy globally; let VFMOS co-optimize. Result: reduced operational load, consistent quality across tenants, built-in observability.
The Pioneer: Scientific Discovery (Materials + Catalysis)
Problem: Autonomous materials discovery agents must integrate thousands of papers (text, figures, tables), run simulations, verify hypotheses against physics principles, and continually evolve as new data arrives—without retraining or redeploying.
With VFMOS: Build your agent to reason over microscopy images for catalytic sites. VFMOS Data Agent orchestrates multi-modal retrieval (papers, atomic structures, experimental results), alignment across sources, and just-in-time learning of new representations. Model Optimizer materializes a composite FM combining vision models (image analysis), NLP models (paper synthesis), and physics-aware reasoning engines. Trust Agent ensures all generated hypotheses satisfy constraint solvers and domain rules. Your agent focuses on strategy; VFMOS evolves and optimizes silently. Result: faster discovery cycles, reproducible reasoning, seamless integration of new knowledge and simulation tools.
Three big shifts
From manual glue to abstraction
No more custom code for context management, prompt structuring, or model routing. VFMOS abstracts these as OS services.
From static to adaptive
Models, data representations, and reasoning strategies adapt automatically to workload and environment—similar to how an OS kernel adapts process scheduling.
From isolated to co-optimized
Agent logic, data retrieval, model selection, and trust verification co-evolve and co-optimize. Changes in one layer automatically propagate benefits to others.
Key research challenges
VFMOS opens rich research frontiers at the intersection of machine learning and systems:
- Virtualization mechanisms: How do we design learnable abstractions that virtualize FM capabilities while maintaining fidelity, safety, and efficiency?
- Self-evolution: How can VFMOS agents continually adapt to new data, domains, and reasoning paradigms without catastrophic forgetting or drift?
- Co-optimization: How do we co-optimize across data selection, model composition, and trust policies to maximize end-to-end utility?
- Hierarchical abstractions: How do we build layered abstractions that enable both high-level app development and fine-grained performance tuning?
- Ecosystem integration: How do we ensure VFMOS works seamlessly with LangChain, CrewAI, MCP, and emerging agent frameworks without proprietary lock-in?
Take-away
VFMOS reframes foundation model engineering from manual orchestration into platform thinking. Just as operating systems hid hardware complexity and enabled application innovation, VFMOS can hide FM complexity and unlock faster, safer, more adaptive agent development.
The key insight: virtualization through hierarchical abstraction and self-evolution can be as transformative for AI systems as it was for computer systems.