Pretrained Models: The Technical Foundation of Innate Intelligence for AI Agents

Recently, two types of AI agents have attracted significant attention: one can move autonomously in virtual 3D spaces, recognize objects, and perform exploration and interaction tasks; the other can understand natural-language requirements and complete the full software development workflow, from requirements breakdown to coding, debugging, and refactoring. At first glance, they seem to belong to different fields: embodied intelligence and software engineering. However, when examining their technical core, both share the same foundation: prior knowledge enabled by large pretrained models.

This prior knowledge can be understood as the “general education” an agent receives before it begins practical work. For example, a strategy agent may seem to have already studied tactical principles and understand offense and defense. It does not start from scratch to learn rules; instead, it inherits knowledge distilled from human experience. Understanding this logic is important for developers building agent systems.

1. From Executing Tasks to Making Decisions: A Fundamental Shift in Capability

Traditional automation follows if-then rules: developers enumerate possible input scenarios and write corresponding processing logic. This works in closed, deterministic environments, but it often fails when facing the open, dynamic, and incomplete real world. Rule engineers cannot exhaustively cover every edge case.

Pretrained models introduce a different approach. Instead of relying only on hand-written rules, the system learns general patterns and regularities from large amounts of data. These patterns are stored in model parameters and form a form of intuitive judgment. When an agent encounters a new scenario, it can make reasonable inferences based on existing knowledge rather than learning from scratch every time. This is the core meaning of “innate intelligence”: it is not a task-specific skill, but a general cognitive foundation that can be transferred across tasks.

2. Where Does a Pretrained Model’s Prior Knowledge Come From?

Understanding the “innate intelligence” of agents requires looking at two key parts of pretraining.

The first is self-supervised learning from large-scale corpora. Models are trained on large amounts of text, as well as images, code, and multimodal data. Training objectives such as “predicting the next token” or “completing missing spans” do not require manual labeling. This self-supervised mechanism allows models to absorb language structure, commonsense knowledge, reasoning patterns, and even code syntax without expensive annotation. What the model learns is not a set of isolated facts, but the relationships and distributions among pieces of knowledge.

The second is the construction of a representation space. After pretraining, the model maps inputs into a high-dimensional semantic space where concepts with similar meanings are close to one another. This representation capability helps an agent “generalize from one case to another.” For example, it can recognize a new object after seeing similar objects, or transfer a problem-solving approach from one programming language to another.

Because pretraining exposes the model to such broad general knowledge, the agent can respond quickly when facing downstream tasks. This also explains why agents in different domains can share similar technical foundations: they are concrete applications of the same pretrained capability in different scenarios.

3. Application Scenario 1: Autonomous Exploration in 3D Environments

In virtual or physical 3D spaces, agents need to perform perception, localization, planning, and execution. Taking autonomous exploration as an example, the agent often operates in a partially observable environment: it can only see a small area at a time, but it must infer the overall structure and plan a path to the goal.

In this scenario, pretrained models provide “world knowledge” and “task understanding.” When the agent sees a door, it can infer from learned knowledge that a room usually lies behind it and that the handle can be pushed to open it. When it receives an instruction such as “find the kitchen,” it can map the language description to visual features and spatial positions. This ability to align language, vision, and actions is a result of multimodal pretraining.

From an engineering perspective, a practical embodied agent usually includes the following modules:

Perception layer (multimodal input parsing)
   ↓
Understanding layer (intent recognition + scene reasoning, driven by pretrained models)
   ↓
Planning layer (task decomposition + path/action sequence generation)
   ↓
Execution layer (action control + environmental feedback)
   ↓
Memory layer (storage of historical states and experience)

Among these, the understanding and planning layers currently receive the largest contribution from pretrained models. The perception layer converts raw data into features that the model can process, while the execution layer turns the model’s decisions into actual actions. The intermediate “brain” relies on reasoning capabilities obtained through pretraining to make key decisions.

4. Application Scenario 2: Autonomous Programming and Code Generation

In software development, the capabilities of autonomous programming agents are also rooted in pretraining. Code itself is a highly structured language. Since the pretraining corpus contains a large amount of public code, models can learn programming syntax, common algorithm patterns, and mappings between problems and solutions.

A typical autonomous programming workflow is as follows:

1. Requirement understanding: Convert the user’s natural-language description into clear technical goals.
2. Solution breakdown: Decompose complex requirements into independently implementable subtasks.
3. Code generation: Write code for each subtask.
4. Execution and verification: Run the code and capture errors and exceptions.
5. Iterative repair: Fix the code based on execution results until it passes verification.

The key to making this workflow “autonomous” is the model’s ability to reinterpret error messages as improvement directions. This closed-loop debugging capability reflects the causal reasoning and error-pattern recognition learned during pretraining, applied to practical tasks. The model does not simply generate code mechanically; it can continuously revise its judgment based on feedback.

5. The Shared Technical Foundation: Why the Two Scenarios Converge

Returning to the initial observation: 3D environment exploration and autonomous programming seem unrelated, yet they share the same technical foundation. The reason is that both require three core capabilities, and all three are provided by pretrained models:

• General reasoning capability: Whether dealing with spatial paths or code logic, the agent needs multi-step reasoning and causal inference.
• Knowledge transfer capability: The agent applies learned patterns to new scenarios instead of learning from scratch.
• Multi-turn interaction capability: The agent dynamically adjusts its strategy based on environmental or runtime feedback, forming a “perceive-decide-feedback” loop.

In this sense, pretrained models provide agents with a general “cognitive operating system,” while specific application scenarios are different “applications” running on top of that system. Understanding this point helps developers avoid rebuilding everything for each new task. Instead, they can focus on scenario adaptation and engineering optimization.

6. Engineering Deployment: Helping Agents Apply Their Knowledge

Understanding the principles is only the first step. In practice, developers must solve a key problem: how can general pretrained capability be adapted to specific business scenarios? Common industry approaches include the following, and developers can combine them according to actual needs.

Retrieval-augmented generation (RAG): Connect the model to external knowledge sources and dynamically retrieve relevant information during inference, injecting it into the context. This can alleviate the lack of private or time-sensitive knowledge without retraining. It is suitable for scenarios where knowledge is updated frequently.

Instruction fine-tuning and parameter-efficient fine-tuning (PEFT): Further adjust the model’s behavior on top of the pretrained model using a small amount of domain data. PEFT techniques, such as training only a small number of additional parameters, can make outputs more aligned with specific domain conventions while keeping training costs manageable and without significantly changing the original model.

Tool calling and function orchestration: Enable the model to call external tools such as calculators, database query systems, code execution environments, and search engines. This helps the agent move beyond pure text generation and perform tasks that require precise computation or real-time data.

Memory mechanism design: Introduce short-term memory, such as the current conversation context, and long-term memory, such as persistent storage of historical experience. This allows the agent to accumulate experience and maintain consistency over long-term interactions.

In practical system design, these capabilities often work together with common cloud services such as message queues, object storage, logging, and monitoring. For example, message queues can asynchronously decouple the agent from tools, object storage can persist long-term memory, and logging and monitoring systems can trace the agent’s decision process, making failures easier to diagnose. Generic cloud platforms, such as Baidu AI Cloud, can provide the underlying support needed for stable operation of such systems.

7. Challenges and Outlook

Although pretrained models provide a solid cognitive foundation for agents, several practical challenges remain during engineering deployment:

Hallucination: Models may generate content that appears reasonable but does not match facts. In high-risk scenarios, constraints such as retrieval augmentation, result verification, and human review are necessary.

Stability of long-horizon planning: For complex tasks requiring dozens or even hundreds of steps, models may drift from the goal or enter loops. Task decomposition, state tracking, and mid-course verification are key ways to mitigate this problem.

Controllability and explainability: The more autonomous an agent becomes, the less predictable its behavior tends to be. Preserving autonomy while ensuring safety boundaries is an unavoidable issue in productization.

Cost and efficiency balance: Larger models usually provide stronger capabilities, but they also bring higher inference costs. Balancing performance and resources requires careful trade-offs based on the specific scenario.

Looking ahead, pretrained models will continue to play a crucial role as the source of “innate intelligence” for agents. As multimodal integration advances, reasoning capabilities improve, and the tool ecosystem matures, agents will move from “being able to do things” toward “understanding how to do things, planning actions, and becoming trustworthy.” For developers, the key skill is no longer just knowing how to call a particular model. It is how to leverage the cognitive foundation provided by pretrained models and turn it into practical solutions for real business problems.