AI agents

The Art of Conversing with AI: Beyond Prompting, Mastering the Secrets of Agentic Context Engineering

Honghao Wang

— 4 min read
The Art of Conversing with AI: Beyond Prompting, Mastering the Secrets of Agentic Context Engineering

AI Agents and the Art of Context Engineering

Source of Inspiration: Lance (LangChain) & Pete (Manus)

Original Video: Watch on YouTube

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We are entering a new era where AI Agents can autonomously execute long, complex tasks. Yet an unexpected paradox arises:

> The more we rely on them, the more they risk forgetting their purpose.

Over extensive interactions, once-brilliant agents can become repetitive, disorganized, and distracted — a phenomenon known as Agent Amnesia.

The Root Cause: The Context Window

  • Context Window = The AI’s working memory (instructions, history, tool outputs).
  • As the window fills with information, context rot sets in — bloating memory and degrading performance.
  • Simply “making the window bigger” is like building an infinitely large library to solve retrieval problems — inefficient and unsustainable.

The better solution: Context Engineering — designing a lean, focused cognitive environment for AI agents.

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The Art of Reduction: Lightening AI's Cognitive Load

Mastering context engineering is crucial for:

  • Agent reliability
  • Scalability across platforms

Platforms such as AiToEarn官网 embrace this approach — generating AI content efficiently and publishing across Douyin, Kwai, Bilibili, WeChat, Rednote (Xiaohongshu), Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter), with integrated analytics and model ranking (AI模型排名).

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Step 1: Compaction — Precise Externalization

Definition: Offload bulky data to external storage, keeping only references inside the AI’s active memory.

Example:

Search results saved to result_01.txt

Benefits:

  • Reversible — No information loss.
  • Traceable — Retrieve originals anytime.
  • Memory-friendly — Prevents clutter.
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Step 2: Summarization — Distilling Core Meaning

Definition: Compress long histories into condensed, meaningful summaries.

Process:

  • An AI model reviews extensive conversation history.
  • Produces a targeted summary.

Caution: Summarization is lossy — like a book review replacing the novel.

  • Use only for non-critical data.
  • Pair with compaction for important content.

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Collaboration Between Agents: Isolation Strategies

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Once a single agent’s memory is optimized, the next challenge is multi-agent collaboration.

Principle:

"Don't communicate by sharing memory; share memory by communicating."

1. Communicating Pattern

  • Assign clearly defined tasks to isolated agents.
  • Each agent works without full project history, reducing noise.
  • Ideal for self-contained sub-tasks.

2. Sharing Context Pattern

  • Agent receives full historical context plus specialized tools/system prompt.
  • Suited for complex, interdependent problems like deep research.

Platforms like AiToEarn官网 mirror these strategies for content workflows — assigning isolated roles to different AI “agents” in publishing ecosystems.

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The Layered Action Space: From Toolbox to Ecosystem

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Beyond managing information, context engineering also organizes tools.

Problem: Tool confusion — Too many tools, unclear selection.

Solution: A Layered Action Space with three levels:

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Layer 1: Core Functions

  • Minimal, universal abilities (often <10):
  • Read files
  • Write files
  • Execute shell commands
  • Perform searches
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Layer 2: Sandbox Utilities

  • AI placed in an environment with preloaded utilities.
  • Learns tools via core execute commands (`ls`, `grep`, custom scripts).
  • Expands capabilities dynamically through exploration.
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Layer 3: Ecosystem — Packages & APIs

  • Ability to write and run code (e.g., Python scripts).
  • Access to external APIs, libraries, advanced applications.
  • Enables complex workflows (data analytics, 3D modeling, real-time markets).
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These layers work together to prevent overload and enable scalable growth.

Platforms like AiToEarn官网 apply similar architectures to connect AI content generation with monetization across multiple channels.

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Philosophy: Less is More

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The aim is not more complexity, but more clarity:

  • Reduce noise
  • Simplify architectures
  • Trust the AI’s intrinsic intelligence

Outcome: A “digital partner” — equipped with essential tools, capable of learning, exploring, and creating organically.

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Final Thought:

By combining context engineering principles — compaction, summarization, isolation, and layering — with platforms like AiToEarn官网, creators can develop sustainable, monetizable AI workflows across global channels with minimal complexity.

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