Terminus Group's Theory of Agentic AI Evolution: From Specialized to General, and Towards Super Agents

As large language models approach the "knowledge ceiling", AI Agents are emerging as their "second growth curve". From "question-answering machines" in the digital world to autonomous actors in the physical world, a global race around spatial intelligence, causal reasoning, and embodied interaction has already begun.

Dr. Ling Shao, Chief Scientist and International President of Terminus Group, outlines a three-phase evolution model—using technology, product, and paradigm shifts as the axes—to present a panoramic view of AI's trajectory from specialized, to general, and ultimately to super agents.

The Evolutionary Path of Agents: A Three-Phase Transition Curve

Dr. Shao divides the progression of agentic AI into three major stages:

• Specialized Agentic AI: Excels at single tasks with superhuman efficiency and accuracy, but has clear boundaries and limited transferability.

• General Agentic AI: Possesses human-like cross-domain reasoning, spatial perception, and physical interaction; capable of autonomous planning and continuous evolution.

• Super Agentic AI: Achieves a qualitative leap in cognition, with recursive self-improvement and ultimate control over the physical world, potentially redefining "intelligence" itself.

Current Status: Still in the "Specialized Phase"

Despite the success of products like GitHub Copilot or Manus, they essentially remain specialized agents powered by large models and manual orchestration:

• Their performance ceiling is determined by training data, not autonomous reasoning.

• They lack spatial perception and physical interaction.

• Their task boundaries are rigid, making cross-domain transfer difficult.

Thus, the industry overall remains in the first phase.

Signature Characteristics Across the Three Stages

Key Technical Breakthroughs

From Specialized → General:

• Deep Understanding: From "text matching" to "world models"

• Efficient Reasoning: MoE sparsification, hardware–algorithm co-design, caching & precomputation

• Spatial Intelligence: Unified 3D geometry, physical laws, and embodied interaction

From General → Super:

• Recursive meta-learning

• New computing paradigms (optical computing, quantum–classical hybrids)

• Foundational innovations in controllability and alignment theory

Is "General Agentic AI" a False Proposition?

• Definition Debate: If "general" = human-level across multiple fields, early forms may appear within 10 years. If it requires "all-domain mastery," a fundamental theoretical revolution will be needed.

• The Balancing Triangle: Increasing model size boosts generality but worsens latency. The solution lies in dynamic collaborative systems:

• Architecture: MoE + edge–cloud synergy

• Algorithms: Inference compression, speculative decoding

• Hardware: Compute–storage integration, optical interconnects

Entry Points: Lightweight Ubiquity × Unified Hub

Future interfaces will adopt a hybrid topology:

• Lightweight: Earbuds, glasses, home sensors—contextualized, edge-side inference, privacy-friendly

• Unified Hub: Cross-device context synchronization, long-term memory, and complex task orchestration

Together they form an integrated cloud–edge–end agent network.

Next-Stage "X Variables" of AI Form

• Embodied Intelligence: Humanoid robots, flexible robotic arms bridging the digital–physical loop

• All-Modal Models: End-to-end alignment of vision, language, touch, and audio from pre-training

• World Models: Shifting from statistical correlation to physical causality, enabling counterfactual prediction

• Distributed Multi-Agent Systems: Swarm intelligence and cooperative game dynamics

• Human–AI Integration: Brain–computer interfaces, bio–silicon hybrid computing

The Terminus Group's Paradigm: Dual-Engine of Engineering × Models

• Foundational Models: Focused on spatial intelligence with multimodal AIoT models—not general-purpose foundations but domain-enhanced models

• Engineering Capability: System architecture, toolchains, compliance, and edge–cloud deployment to ensure usability, reliability, and controllability

• Product Deployment:

• Current: Specialized agent HALI, already applied in wearables and robotics

• Mid-term: Exploratory research on general agents

• Long-term: Continuous "technology radar" tracking of super agents

Conclusion: Between the Known and the Unknown

From specialized, to general, and towards super, each leap in AI agents is accompanied by the resonance of technology, products, and paradigms. Terminus Group chooses to anchor itself in spatial intelligence, advancing with the dual engines of engineering and models: deepening expertise within known boundaries, while exploring the unknown frontier.

As Dr. Shao concludes:"General agents may not be the endpoint, but rather the next starting line in the co-evolution of humanity and intelligence."