Gartner’s top tech trends for 2026: AI supercomputing, multi-agent systems lead the pack

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Gartner’s Top Strategic Technology Trends for 2026: AI Supercomputing and Multi-Agent Systems Lead the Way

Global research and consulting firm Gartner has released its forecast for the Top 10 Strategic Technology Trends for 2026, highlighting the rapid evolution of AI-driven, secure, and decentralised computing ecosystems. The trends underline a major technological inflection point marked by AI supercomputing, multi-agent systems, and domain-specific models reshaping enterprises and governance alike.

KEY TECH TRENDS 2026

1. AI Supercomputing Platforms

Integration of CPUs, GPUs, AI ASICs, neuromorphic chips, and orchestration software for complex AI and data workloads.
Enables massive computing power, efficiency, and performance across ML, simulation, and analytics.
Prediction: By 2028, 40% of enterprises will use hybrid computing architectures (up from 8%).

2. Multi-Agent Systems (MAS)

Collections of autonomous AI agents that interact, coordinate, and execute complex goals independently or collaboratively.
Reflects the rise of agentic AI—AI systems capable of planning, reasoning, and using tools autonomously.
Represents a shift from static AI models to dynamic goal-oriented ecosystems.

3. Domain-Specific Language Models (DSLMs)

Industry-focused AI models tailored for specific domains (e.g., law, medicine, finance).
Offer higher accuracy, lower cost, and stronger compliance than generic LLMs.
Aim to deliver tangible business value from AI investments.

4. AI Security Platforms

Provide unified security for custom and third-party AI systems.
Safeguard against prompt injection, data leakage, and rogue AI behaviour.
Prediction: 50% of enterprises will use such platforms by 2028 to protect AI infrastructure.

5. AI-Native Development Platforms

Enable software creation through GenAI-assisted coding and AI–human collaboration.
Small teams of developers can rapidly produce applications using natural language and automated tools.
Marks a move toward AI-driven software engineering ecosystems.

6. Confidential Computing

Protects data in use via hardware-based Trusted Execution Environments (TEEs).
Ensures privacy even from infrastructure or cloud operators.
Prediction: By 2029, 75% of workloads on untrusted infrastructure will use confidential computing.

7. Physical AI

Embedding intelligence in machines, robots, and smart equipment that sense, decide, and act in real environments.
Drives automation in sectors like manufacturing, logistics, defence, and healthcare.
Demands integration between IT, operations, and engineering disciplines.

8. Preemptive Cybersecurity

Focuses on proactive defence through predictive analytics and AI threat modelling.
Moves security spending from reactive response to anticipatory protection.
Forecast: By 2030, preemptive systems will make up half of global cybersecurity spending.

9. Digital Provenance

Ensures traceability and authenticity of software, data, and AI-generated content.
Becomes vital amid rising use of open-source and synthetic data.
Prediction: By 2029, lack of provenance measures could lead to major sanctions and compliance risks.

10. Geopatriation

Shift of company data and apps from global public clouds to sovereign or regional infrastructures due to geopolitical concerns.
Expands beyond governments and banks to private corporations amid global instability.
Reflects a broader move toward data sovereignty and localisation.

THEME INSIGHT

2026 marks the AI industrialisation phase, where organisations are expected to:

Operationalise AI governance and security,
Build multi-agent ecosystems,
Invest in sovereign and confidential infrastructure, and
Balance innovation with compliance and resilience.

Updated - October 27, 2025 ; 1:26 PM | Business Line