Harness Engineering Deep Dive: Multi-Level Memory and Multi-Agent Collaborative Development in Practice

From Prompt Engineering to Harness Engineering: A Paradigm Shift in AI Agent Development

More and more developers are facing a harsh reality: relying solely on Prompt Engineering is no longer sufficient for complex, industrial-grade tasks. Whether it's context management in multi-turn conversations, maintaining persistent memory, or orchestrating collaboration between multiple Agents, traditional prompt optimization approaches fall short.

A new core paradigm is emerging — Harness Engineering. Rather than focusing narrowly on "how to write a good prompt," it approaches the problem from a systems architecture perspective: how to design a complete framework that gives AI Agents the capabilities of autonomous evolution, persistent memory, and collaboration.

This article systematically deconstructs the implementation logic of multi-level memory, defense-in-depth, and multi-Agent collaboration based on Claude Code's underlying source architecture, and demonstrates the full pipeline from architecture design to local deployment through hands-on practice.

Deep Dive into Claude Code's Architecture

Design Philosophy of the Underlying Source Code

Claude Code is far more than a simple API wrapper. Its underlying architecture embodies several key engineering principles:

1. Layered Abstraction: Agent capabilities are decomposed into perception, decision, and execution layers, each with independent interface definitions and implementation logic
2. State Management: Through persistent state mechanisms, the Agent maintains context without loss during long-running sessions
3. Security Boundaries: Built-in defense-in-depth mechanisms prevent uncontrollable behavior during autonomous execution

The core philosophy behind this architecture is: Code isn't cobbled together — it's the product of engineering thinking and restructuring.

Implementation Principles of the Multi-Level Memory System

One of the most critical capabilities of an industrial-grade Agent is memory management. Claude Code's memory system employs a three-tier architecture, with each level serving a different purpose:

• Short-term Memory (Working Memory): The context window of the current session, handling immediate interactions with the fastest response time
• Mid-term Memory (Session Memory): Session state across conversation turns, maintaining task continuity and ensuring coherence in multi-turn interactions
• Long-term Memory (Persistent Memory): Persistently stored knowledge and experience, supporting the Agent's autonomous evolution and knowledge accumulation

These three memory tiers are coordinated through carefully designed read/write strategies, ensuring the Agent can respond quickly to current needs while accumulating long-term experience. This multi-level memory mechanism is one of the core differentiators between Harness Engineering and traditional Prompt Engineering.

Core Structure of Harness Engineering

Definition and Components of Harness Engineering

Harness Engineering can be understood as "the engineering methodology for harnessing AI." It focuses not on optimizing individual prompts, but on the architectural design of the entire Agent system. It consists of four major modules:

• Input Pipeline: Efficiently integrates multi-source information (user instructions, environment state, historical memory) to provide complete context for decision-making
• Decision Engine: Enables the Agent to make sound judgments in complex scenarios, balancing efficiency and accuracy
• Output Control: Ensures the Agent's behavior meets expectations and remains safely controllable, preventing runaway outputs
• Feedback Loop: Allows the Agent to learn and evolve from execution results, continuously improving task completion quality

Engineering Implementation of Defense-in-Depth

In industrial-grade applications, security is a non-negotiable baseline. Harness Engineering employs a multi-layered defense-in-depth strategy, with checkpoints spanning permissions, auditing, rollback, and isolation:

1. Permission Tiering: Different operations correspond to different permission levels; high-risk operations require additional confirmation to prevent costly mistakes
2. Behavior Auditing: All Agent decisions and executions have complete log records for post-hoc tracing and troubleshooting
3. Rollback Mechanism: When anomalous behavior is detected, the system can quickly revert to a safe state, minimizing the blast radius
4. Sandbox Isolation: Critical operations execute in restricted environments, preventing impact on external systems and ensuring production stability

Hands-On: Building a Hermes Agent with Autonomous Evolution Capabilities

From Design Philosophy to Production Implementation

The hands-on section starts from the JSTOCK design philosophy to build a Hermes Agent with Self-Purification capabilities. Self-purification means the Agent can automatically identify and clean up invalid memory fragments, maintaining high-quality knowledge bases.

Core implementation steps include:

1. Define the Agent's Core Capability Boundaries: Clarify what the Agent can and cannot do, establishing safe operational boundaries
2. Design the Skill Evolution Mechanism: Enable the Agent to automatically summarize experience after completing tasks, forming reusable Skill modules
3. Build Persistent Memory Storage: Implement efficient long-term memory retrieval based on vector databases, supporting continuous knowledge accumulation
4. Implement Multi-Agent Collaboration Protocols: Define communication formats and collaboration workflows between Agents, laying the foundation for future scaling

Two Typical Scenarios: Feishu Assistant and Terminal Agent

The hands-on demonstration covers two typical scenarios close to real business needs:

Feishu Assistant Scenario: Integrating the Hermes Agent into the Feishu platform to enable intelligent Q&A and task execution within enterprises. Key technical points include Feishu API integration, message event handling, and binding persistent memory to Feishu conversations. This scenario demonstrates how Agents can be deployed within enterprise collaboration tools.

Terminal Agent Scenario: Deploying the Agent in a local terminal to automate developer daily tasks such as code generation, file operations, and system management. This scenario is closer to how Claude Code is used, but with custom Skill evolution capabilities added, making the Agent smarter with use.

Engineering Practices for Multi-Agent Collaboration

When a single Agent's capability boundaries cannot cover complex tasks, multi-Agent collaboration becomes inevitable. The core challenges of multi-Agent systems center on four aspects:

• Task Decomposition: Reasonably splitting complex tasks among different Agents, ensuring appropriate granularity and clear boundaries
• Information Sharing: Efficiently passing context and intermediate results between Agents, minimizing information loss
• Conflict Resolution: When multiple Agents' decisions contradict each other, reaching consensus quickly through arbitration mechanisms
• Result Integration: Aggregating outputs from various Agents into a final result, ensuring completeness and consistency

In practice, the "Controller Agent + Expert Agents" architecture pattern is commonly adopted. The Controller Agent handles task understanding and distribution, Expert Agents each process sub-tasks within their specialties, and the Controller Agent ultimately integrates the output. This pattern maintains system flexibility while reducing collaboration complexity.

Conclusion: The Long-Term Value of Harness Engineering

Harness Engineering represents the next phase of AI Agent development. It requires developers not only to know how to converse with AI, but also to possess systems architecture thinking. From multi-level memory management to defense-in-depth, from autonomous evolution to multi-Agent collaboration, every component demands careful engineering design.

For developers looking to go deep in the Agent development space, understanding these underlying architectural principles holds more long-term value than mastering any specific framework. Once you truly grasp the Harness Engineering methodology, you'll be able to rapidly build industrial-grade intelligent agent systems regardless of how the underlying models evolve.