Beyond transformers: Building enterprise AI for the multi-model era

Every AI system organisations uses today whether from OpenAI, Anthropic, or Google runs on the same foundational architecture called a transformer. Transformers process language by examining relationships between every word in a sequence simultaneously. This approach powers the chatbots, copilots, and assistants now embedded across enterprise workflows.

Transformers excel at text prediction. But the architecture carries inherent constraints that become visible when you push beyond simple query-response patterns. Processing long documents becomes exponentially expensive. The system forgets everything between conversations. Complex reasoning happens linearly, without the ability to reconsider earlier steps. These aren't failures to fix through better training—they're structural limitations of how transformers compute.

Recent news about Yann LeCun's reported plans to leave Meta to pursue 'world models'; systems that understand spatial relationships and causal structure, signals where leading researchers see the next frontier. World models represent one direction. Memory systems that persist across sessions represent another. Architectures optimised for structured reasoning represent a third.

The question for enterprises isn't which approach will win. It's whether infrastructure can adapt as multiple specialised architectures mature.

This article explores five emerging categories: base architectures for efficient processing, memory systems for continuous learning, reasoning architectures for structured problem-solving, world models for causal understanding, and orchestration layers coordinating these components.

1. Base architectures: Making long-context processing viable

When your legal team analyses a 200-page contract, transformer-based systems face a cost problem. Processing costs escalate dramatically with document length—doubling content quadruples compute. This makes comprehensive document analysis prohibitively expensive at scale.

New architectures eliminate this scaling problem.

Mamba

Rather than examining relationships between every word pair simultaneously, Mamba maintains a compressed running summary that updates as it processes each token. Processing 100,000 tokens costs roughly 10× more than 10,000 tokens, not 100× more.

Mamba's selective state-space approach scales linearly with sequence length, compared to quadratic scaling for transformer attention. Benchmarks show competitive language modelling performance at similar parameter counts, though transformers retain advantages in in-context learning and precise recall tasks. Hybrid architectures combining both approaches often outperform either alone which is why IBM's Granite 4.0 uses this combination.

Sub-quadratic attention

For organisations invested in foundation model infrastructure, SCOUT and HiP provide migration paths without complete model replacement. SCOUT compresses document segments into checkpoints, applying attention only across compressed representations. HiP prunes attention connections hierarchically, preserving important relationships whilst eliminating redundant computation.

Critically, HiP operates training-free—extending context capabilities of deployed models without retraining.

2. Memory systems: Moving beyond stateless inference

Current AI systems start every conversation from zero. Your customer service agent reasons through familiar problems from first principles every time. Your compliance reviewer learns nothing from thousands of similar reviews.

Memory systems provide persistent storage that models query and update across sessions, transforming stateless responders into systems that accumulate expertise.

Procedural memory

When an AI system successfully completes a complex task, MEMP captures that sequence as a reusable procedure. Future similar requests execute the learned procedure directly rather than reasoning through every step again. The research demonstrates improved success rates and reduced inference costs as the procedure repository grows.

Episodic memory

Mem0 implements graph-structured episodic memory where conversations become nodes connected by semantic relationships. When a customer initiates contact, the system retrieves related past exchanges. Retrieval combines vector similarity with graph traversal, maintaining profiles that update incrementally.

Agentic memory

A-MEM gives models control over their own knowledge management. The system decides what to store, how to organise it, when to retrieve, and when to delete based on task requirements building custom memory structures dynamically rather than relying on fixed indexing schemes.

Knowledge graphs

Vector databases excel at finding similar content but lose explicit relationships between entities. GraphRAG constructs knowledge graphs from unstructured text, extracting entities and relationships automatically. Queries combine semantic search with structured pattern matching for specific relationship types.

3. Reasoning architectures: Structured problem-solving

Language models generate responses token by token in a single forward pass. This makes them unreliable for systematic reasoning requiring backtracking or verification.

Reasoning architectures separate planning from execution, constructing explicit structures that can be inspected and refined before producing results.

Hierarchical reasoning

The Hierarchical Reasoning Model implements explicit planning, execution, and verification phases. The system generates a problem decomposition tree, executes subtasks in parallel where possible, then validates results against constraints before combining them. This outperforms flat reasoning chains on complex multi-step problems whilst producing interpretable intermediate outputs.

Reflective reasoning

OmniReflect analyses past problem-solving trajectories—what worked, what failed, what proved inefficient—and extracts principles that improve future reasoning. Abstract learnings capture generalisable patterns. Error analysis documents mistakes and corrections. These reflections distil into constitutional guidelines that shape reasoning on similar future tasks.

After processing hundreds of compliance reviews, the system incorporates learned patterns: which document types indicate higher risk, which approval sequences cause delays, which edge cases require escalation.

4. World models: Learning causal structure

Language models learn statistical patterns from text. They predict what words typically follow other words, but don't build causal models of how systems actually work.

World models learn representations of environments that support prediction, planning, and simulation.

Joint embedding predictive architecture (JEPA)

I-JEPA predicts abstract representations of future states rather than surface-level outputs. By operating in representation space, it captures semantic relationships and causal structure. Learning and Leveraging World Models extends this to capture temporal dynamics and causal relationships across sequences.

For strategic planning and scenario analysis, world models enable testing interventions in simulation before committing resources.

Self-play

Robust Autonomy Emerges from Self-Play trains agents through simulations, generating unlimited synthetic data covering scenarios that rarely appear in historical records. Autonomous driving policies trained through 1.6 billion kilometres of simulated driving achieve state-of-the-art real-world performance without using human driving data.

For market shock response, operational incidents, or safety-critical decisions, self-play enables training on rare but critical scenarios through synthetic experience.

5. Orchestration: Coordinating the cognitive stack

Individual architectures provide distinct capabilities. Orchestration layers coordinate these components, routing requests to appropriate systems and enforcing governance policies.

Routing and context management

Different tasks require different capabilities. Context routers like RCR-Router analyse incoming requests and determine optimal routing considering task type, resources, latency, and cost. Multi-agent memory systems like MIRIX provide shared memory foundations accessible to multiple specialised agents.

Effective routing determines cost-performance trade-offs at scale. Organisations pay for the capability each task actually requires.

Governance and auditability

Orchestration layers capture traces of agent reasoning, tool invocations, and decision points—providing audit trails for compliance review and debugging. For regulated industries, orchestration provides the mechanism that makes autonomous systems deployable under compliance requirements.

Building for what's next

The industry isn't waiting. Frontier labs and leading AI startups are already shipping multi-model architectures in production. IBM's Granite 4.0 combines Mamba state-space models with transformers today. The capabilities explored throughout this article—linear-time processing, persistent memory, structured reasoning, causal models—are no longer theoretical. They're deployable.

Organisations locked into single-vendor, transformer-only infrastructure face a choice: costly migrations under competitive pressure, or proactive preparation now.

Three questions determine readiness:

• Can your data layer serve multiple architectures? When episodic memory retrieves past interactions and hierarchical reasoning interprets them, meaning must preserve across both. Ontologies designed for transformer-only systems will bottleneck everything that follows.

• Does governance live where coordination happens? As autonomous systems make decisions spanning memory, reasoning, and action, accountability must be embedded in orchestration—not reconstructed after the fact.

• Can you trace how components work together? Isolated metrics tell you whether individual models perform. Tracing reveals whether they actually collaborate. Regulators will demand proof that multi-model decisions remain coherent and auditable.

The shift to multi-model systems is underway. Rather than predicting which specific architectures will dominate, the focus should be on building infrastructure that accommodates evolution flexible data layers & ontologies, modular orchestration, observability that spans components. Organisations approaching this thoughtfully will find themselves better positioned to adopt what works as capabilities mature. Those treating current infrastructure as fixed will face harder decisions later.