# Let Your AI Agent Do ITS OWN Harness Engineering (Here's How)

## Executive summary

The video details 'Harness Engineering,' a methodology for continuously improving AI agents deployed in production. This process involves tracking agent mistakes and retroactively updating the agent's rules, skills, and memory. The speaker demonstrates how to automate this improvement using LangChain's Managed Deep Agents and LangSmith Engine, implementing two core feedback loops: 1) Learning from user escalations (Memory), and 2) Improving core instructions and workflows (Engine). Crucially, the system maintains human oversight, allowing the engineer to approve all proposed changes before deployment, ensuring control and reliability.

## Key takeaways

- Harness Engineering Defined: Harness engineering is the practice of improving the rules, skills, and memory surrounding an AI model over time by analyzing production mistakes, rather than just building the agent once. This concept translates directly from coding agents to general production automation agents.
- Two Core Improvement Loops: The system utilizes two loops for continuous improvement: 1) Learning from escalations (building a knowledge base of common user issues) and 2) Using the LangSmith Engine to analyze production traces and suggest high-level improvements to the agent's core instructions and rules.
- Maintaining Human Control: While the agent can autonomously surface and propose fixes, the engineer remains 'in the loop.' All proposed changes—whether to memories, skills, or core instructions—must be reviewed and explicitly approved by the user before being applied to the production agent.

## Technical details

- Agent Architecture and Components: The agent's harness is defined using Markdown documents for flexibility, including `instructions.md` (system prompt/global rules), skills (workflows), and memories. This structure allows LangChain to automatically stitch together the components when deployed.
- Managed Deep Agents Setup: The platform used for demonstration is LangChain's Managed Deep Agents, which handles the complex deployment and connection of all harness components within LangSmith. This simplifies the process compared to manually writing extensive plumbing code.
- Loop 1: Memory and Escalations: This loop captures instances where the agent is uncertain and escalates the issue to a human team inbox. The human correction is then stored as a 'lesson' in the Context Hub, which updates both the core agent rules (`core_agents.md`) and the private memory for the specific user.
- Loop 2: LangSmith Engine: The LangSmith Engine analyzes production trace data to identify systemic failures. It proposes specific, concise fixes (e.g., changes to `instructions.md`) that address the root cause of the failure, allowing the engineer to apply the fix with a single click.

## Practical implications

- Reduces the manual effort required for maintaining and improving complex AI agents in production.
- Provides a structured, auditable process (like Git pull requests) for updating agent rules and skills.
- Increases agent reliability by systematically capturing and applying lessons learned from real-world user interactions and failures.

## Topics

AI Agents, Harness Engineering, LangChain, LangSmith, LLMs, Production AI, Build Automation, LangChain Managed Deep Agents, Managed Deep Agents docs, LangSmith Engine

Source: https://www.youtube.com/watch?v=TVhhmhvCovg