In Real-World Commercial Production, Dynamic Self-Evolving Agents Are the True Path for Consumer Services

In the wave of AI application deployment, consumer-facing service scenarios (such as intelligent customer service, personal assistants, and lifestyle service planning) pose three extremely rigorous and mutually constraining challenges for AI systems. These constitute the "Impossible Triangle" of engineering implementation: Openness, Controllability, and Economy.

I. The Dilemma Before the Breakthrough: The "Impossible Triangle" of Commercialization

Current mainstream solutions on the market, whether directly applying General LLMs or building traditional limited-domain Agents, can usually only satisfy one or two angles of the triangle, but never all three simultaneously.

1. The Fatal Flaw of General LLMs: Openness without Commercial Viability

Deploying general-purpose LLMs (such as GPT-4, Claude 3.5, etc.) directly as consumer interfaces perfectly satisfies the need for "Openness" (understanding and responding to bizarre and varied queries), but faces three fatal flaws in commercial implementation:

• Uncontrollability: LLMs are probabilistic models by nature. In serious scenarios like finance, healthcare, or after-sales support, randomness can lead to severe consequences (e.g., promising non-existent refund policies).
• Security Risks: They are highly susceptible to prompt injection attacks or being induced to output harmful content.
• High Cost: For consumer-facing services with hundreds of millions of daily calls, relying entirely on large-parameter models for Chain-of-Thought reasoning results in token consumption and latency that are unbearable for most business models.

2. The Limitation of Limited-Domain Agents: Safety without Flexibility

To solve hallucination and compliance issues, the traditional approach is to build Agents based on Finite State Machines (FSM). They use "converged tools" and fixed flows, offering low costs and high safety.

• The Pain Point: Human needs in the real world are highly divergent.
• The Consequence: Once a user's intent exceeds the preset flow boundaries (e.g., "I want to book a ticket to Paris, but my passport is expiring soon. Can you check the expedited visa process before booking?"), the traditional Agent falls into paralysis or an infinite loop.

II. Yunjue Agent: A Self-Evolving Architecture Breaking the Triangle Constraints

We are dedicated to building the Yunjue Agent. This system aims to expand capability boundaries through a self-evolution mechanism combining offline and online states. It ensures safety by utilizing a converged toolset while significantly reducing costs through "path caching," all while satisfying the demand for openness.

Its core logic involves reconstruction in the following four dimensions:

1. Self-Evolving Toolset

The Agent's basic operational units (Tools) are no longer static code but self-optimizing and formally verified modules.

• Safety: Underlying execution actions (API calls, DB read/write) are absolutely safe.
• Dynamism: Capability boundaries expand dynamically with demand, rather than being hard-coded by engineers.

2. Self-Evolving Orchestration

The system no longer relies on rigid code logic but generates "strategies" through self-play and experience distillation.

• Intent Routing: Automatically distinguishes whether a user request is chit-chat, an analytical task, or a delivery task.
• Simulation: Simulates long-tail scenarios in an offline state (or even during system-level pre-training) to explore new tool combination paths.

3. Path Caching & Layered Summary

This is the key to cost reduction and efficiency enhancement.

• Crystallization Mechanism: Once a path handling a complex demand is verified as effective and safe, it is "crystallized" into a static path template and stored in the trajectory library.
• Layering: Trajectories and tools undergo layered summarization to form reusable high-level primitives.

4. Extreme Cost Reduction at Runtime

During online service, the system prioritizes matching pre-stored paths. For common high-frequency complex demands, execution only requires filling in parameters, eliminating the need for expensive large model reasoning. The deep reasoning engine is triggered only when facing entirely new, unknown demands.

III. Paradigm Shift in Software Engineering: From "Static Artifact" to "Organism"

Yunjue Agent is not just a product; it represents a fundamental transformation in the software development paradigm.

1. Entropy and Latency in Traditional Software Iteration

The current software industry (even under Agile/DevOps) still suffers from a long and fragile feedback chain:

User Feedback $\rightarrow$ PM Analysis $\rightarrow$ Engineer Coding $\rightarrow$ QA/AB Testing $\rightarrow$ Release

• Translation Loss: The process of converting unstructured pain points into structured code is full of information loss.
• Speed of Light Limit: Constrained by Brooks's Law, a linear increase in human capital cannot solve the exponential growth in communication costs.
• Resource Idling: Expensive A/B testing causes code branches to be locked for long periods.

2. Unlocking New Agile: Real-Time Reflection and Parallel Evolution

Yunjue Agent transforms software development from "waiting for artisans to patch" to "real-time self-adaptation." In scenarios where consumer requests arrive in batches, this creates the perfect soil for Batch Parallel Evolution.

Based on the Agent's self-reflection, we distribute problem-solving paths as follows:

Under this paradigm, software is a living organism capable of fine-tuning and restructuring itself on a minute-level timescale.

IV. A Pragmatic Path to AGI

Eddie Wu (CEO of Alibaba) asserted that the path to ASI (Artificial Super Intelligence) is inevitably Self-Iteration.

Yunjue Agent, as a commercial implementation, is practicing this path:

1. Commercially: Providing controllable, low-cost, and open consumer-facing services.
2. Academically: Open-sourcing a self-evolution architecture comparable to the world's top tier, ensuring that academia is not left behind by closed-source giants and remains on the same starting line in the exploration of AGI.

Current Progress: As a first step, we have completed the validation of In-Situ Tool Self-Evolution. For the detailed technical report, please refer to our Technical Paper.