Since ChatGPT's explosive rise in 2022, artificial intelligence has rapidly transitioned from mere "chatbots" capable of responding to queries, to autonomous "agents" capable of executing tasks independently. In the emerging field of AI Agents, two architectural paradigms seem to have emerged: Compiled Agents and Interpreted Agents. Understanding their differences, capabilities, and limitations is essential for grasping the broader evolution of AI-driven productivity.

To simplify:

• Compiled Agents embed intelligence predominantly during development, using pre-defined workflows and scripts. They excel in tasks with predictable outcomes.

• Interpreted Agents dynamically apply intelligence at runtime, adjusting actions based on immediate context and feedback, suited to open-ended, unpredictable tasks.

Just as traditional software differentiates between compiled (pre-wired) and interpreted (runtime-decided) languages, AI agents exhibit similar distinctions.

In LLM-native agents, "compilation" occurs during model training. Vast textual data is compressed into fixed neural parameters. Post-deployment, these parameters act like "compiled" code, setting fixed probabilistic boundaries on potential behaviors.

However, runtime inferences from LLMs reveal an "interpreted" quality, characterized by:

• Dynamic CoT (Chain-of-Thought) generated spontaneously

• Adaptive path planning reacting to real-time feedback

• Probabilistic decisions, allowing the same prompt to yield different outcomes

Thus, LLMs represent a hybrid computational paradigm, combining "probabilistic compilation" and "constrained interpretation"—leveraging pre-trained parameters while dynamically interpreting and adapting at runtime.

Unlike LLM-native agents, compiled agents follow strict, pre-defined workflows:

• Clear, predetermined logic paths

• Fixed decision branches

• Limited context management

• Deterministic results

Examples: ByteDance's Coze platform exemplifies this model. Users visually design the agentic logic via drag-and-drop workflows, ensuring consistency and reliability. Ideal for well-defined business automation tasks like RPA (Robotic Process Automation), compiled agents excel in repeatable, predictable operations.

Limitations: Rigidity and inability to adapt dynamically. Any unforeseen changes in environment or input can disrupt workflows, necessitating manual reconfiguration and/or re-training the models behind.

Interpreted agents are LLM-native autonomous agents that dynamically formulate and revise their execution plans:

• Goal-driven, high-level task definitions

• Real-time strategic planning

• Environmental awareness

• Autonomous decision-making with dynamic tool selection

Examples: Manus and AutoGPT embody interpreted agents. AutoGPT autonomously breaks tasks into subtasks, sequentially executes them, adapts based on interim results, and maintains persistent memory states to handle complex, multi-step operations. Manus, employing a multi-agent collaborative framework, autonomously executes complex workflows—from data analysis to report generation—demonstrating a complete "idea-to-execution" loop.

Strengths: Highly adaptive, capable of handling diverse, unforeseen scenarios. Ideal for research, creative tasks, and personal assistance.

Challenges: Unpredictability, higher computational resources, potential security risks, and more intricate development and testing procedures.

Agent capabilities heavily depend on interaction modes with external environments:

• Universal Interfaces (browser-like interactions) grant agents broad compatibility but face efficiency, reliability, and security issues.

• Specialized Interfaces (API calls) offer speed, stability, and security but lack flexibility and require direct integration.

Strategically, agents leveraging specialized APIs can form more robust, defendable positions, avoiding easy internalization by LLM providers.

Future agents will increasingly blend compiled reliability with interpreted adaptability, embedding runtime-flexible modules within structured workflows. Such hybrids combine precise business logic adherence with adaptive problem-solving capabilities.

Advances needed include:

• Further enhanced runtime reasoning and self-reflection via RL (Reenforcement Learning) post-training to improve decision accuracy

• Integrated multimodal perception (visual, auditory, tactile) for richer environmental understanding

• Robust resource management and runtime environments supporting persistent, background-running interpreted agents

Widespread agent deployment raises security, privacy, and ethical issues, demanding stringent governance, transparent operational oversight, and responsible AI guidelines.

Compiled and interpreted agents represent complementary, evolving paradigms. Their convergence into hybrid architectures is forming the backbone of a new, powerful LLM-native agent ecosystem. As this evolution unfolds, humans will increasingly delegate routine cognitive tasks to agents, focusing instead on strategic, creative, and emotionally intelligent roles, redefining human-AI collaboration.

In essence, the future of AI agents lies in balancing the precision and predictability of compilation with the flexibility and creativity of interpretation, forging an unprecedented path forward in human-technology synergy.

[Related]

• Xiao Hong (Red): The Man Behind the Autonomus Genral Agent Manus

• The Agent Era: The Contemporary Evolution from Chatbots to Digital Agents

• Manus website

• Does the New Reasoning Paradigm (Query+CoT+Answer) Support a New Scaling Law?

• Technical Deep Dive: Understanding DeepSeek R1's Reasoning Mechanism in Production

• DeepSeek's R1 Paper: A Storm in AI LLM Circle

• The Turbulent Second Chapter of Large Language Models: Has Scaling Stalled?

• DeepSeek_R1 paper