Beyond Transformers:
The Evolution of AI Architectures
Gartner emphasizes a shift from single Transformer LLMs to composite architectures—integrating agents, domain-specific models, and verifiable workflows.
Strategic Significance
The expansion beyond single-model dependency aims to deliver trustworthy and auditable workflows. By combining diverse technologies, enterprises can achieve specific domain value that general-purpose models alone cannot provide.
Key Technology Approaches
Multi-Agent Systems (MAS)
Collaboration or competition between specialized agents to decompose complex tasks into testable, reusable units.
Agent AI & Management
Orchestration through Agent Management Platforms (AMP) and "guardian agents" for safety and policy enforcement.
Modular Composite AI
Using LLM routers, RAG, and knowledge graphs to optimize for cost, accuracy, and contextual relevance.
Domain-Specific & Knowledge Graphs
Integration of fine-tuned domain models with structured semantic layers for enterprise-grade precision.
New Paradigms
- → World Models
- → Collective Intelligence
- → Decision Intelligence
Infrastructure
Hardware & Ops
Infrastructure & Ops
"Foundations for high-performance and low-latency scaling."
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AI-Assisted Data Mesh
Knowledge graphs and semantic layers supporting RAG workflows.
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Hardware Acceleration
Mixed workloads across GPUs, DPUs, and NPUs for edge deployments.
Operational Lifecycle
Safety & Governance
- Embedded Governance: RBAC, BYOK/KMS, and auditable logs built into the design phase.
- Model Diversity: "2+1" strategy (2 frontier APIs + 1 open-weight) to mitigate vendor lock-in.
- Automated Guardrails: Drift alarms and escalation playbooks codified into platform services.
Key Market Categories
Practical Next Steps
Recognize "Beyond Transformers" as a Systems Problem. Success requires the orchestration of models, agents, and semantic layers.